DevOps Skills

CI/CD, deployment, and infrastructure. This page lists all 616 DevOps skills indexed from 86 repositories (updated 2026-10-26). Every skill is free and open source — open one for the install command, or filter the interactive catalog to DevOps.

1. issue-resolver luongnv89/idd · score 95/100 (grade A)
   Create an atomic PR closing a GitHub issue end-to-end via a 6-step pipeline. Use to resolve, fix, or implement issue #N. Don't use for analysis without fixing (/issue-analysis), reviewing a PR (/issue-pr-review), or bulk backlog work (/auto-pilot).
2. dev-machine-setup luongnv89/skills · score 93/100 (grade A)
   Set up or tune any dev machine, fresh or drifted, on macOS, Linux, or Windows: report what's missing, install only that, then fix PATH, duplicate runtimes, and shell config. Don't use for Dockerfiles, CI images, or single package installs.
3. devops-pipeline luongnv89/skills · score 93/100 (grade A)
   Configure pre-commit hooks and lean GitHub Actions for shift-left quality assurance. Use when adding or auditing CI/CD to maximize local test coverage and minimize CI cost. Skip for Terraform/K8s, deployment pipelines, or non-GitHub CI providers.
4. github-pages-publish luongnv89/skills · score 93/100 (grade A)
   Publish or update a static site (gh-pages) on GitHub Pages via GitHub Actions in any repo: text or pre-built HTML in, workflow set up, live URL verified. Don't use for CI test pipelines, framework/SSG builds, or Netlify/Vercel hosting.
5. install-script-generator luongnv89/skills · score 93/100 (grade A)
   Generate cross-platform install scripts for any software or library — a standalone install.sh runnable via a curl/wget one-liner with OS, arch, and package manager detection. Don't use for Dockerfiles, CI/CD pipelines, or one-off shell scripts.
6. security-setup luongnv89/skills · score 93/100 (grade A)
   Install local-first security hardening: pre-commit secret detection, offline dependency scans, static analysis, reports, and gated free CI. Use when hardening repos or adding security hooks. Don't use for incident response or cloud security reviews.
7. azure-devops sickn33/antigravity-awesome-skills · score 91/100 (grade A)
   Set up Azure Pipelines for CI/CD, configure build and release pipelines,
8. nextflow K-Dense-AI/claude-scientific-skills · score 91/100 (grade A)
   Builds, runs, and debugs Nextflow DSL2 pipelines and nf-core workflows. Use for Nextflow, nf-core, .nf files, nextflow.config, processes/channels/operators, samplesheets, nf-test, modules/subworkflows, container and executor configuration, HPC/SLURM or cloud deployment, and failed or resumed pipeline runs.
9. archify tt-a1i/archify · score 89/100 (grade B)
   Create validated architecture, workflow, sequence, data-flow, lifecycle/state, ERD, class, tree, timeline, and waterfall diagrams as explorable standalone HTML with dark/light themes, motion and image/SVG/WebM export. Accept requirements or Mermaid; inspect repository evidence for code-backed diagrams. Use when visualizing systems, API calls, pipelines, schemas, histories, timings, or everyday steps, parts, relationships and states. Also turn an answer into a one-page explainer, with the conclusion first, structured panels, native diagrams and charts, when the user asks for a page, an explainer or an answer to share (做成一页, 用页面讲), or proactively when the user's rules opt in with an [archify explainer: ...] line. Not for dashboards.
10. cloudish sickn33/antigravity-awesome-skills · score 89/100 (grade B)
    Deploy a Dockerfile, source folder, or existing image to Cloudish as a running container at a live URL, built server-side with no local Docker, with confirmation before spending credits.
11. elk-stack sickn33/antigravity-awesome-skills · score 89/100 (grade B)
    Deploy and manage the ELK Stack (Elasticsearch, Logstash, Kibana) for
12. github-pages-deployer Affitor/affiliate-skills · score 89/100 (grade B)
    Deploy affiliate content to GitHub Pages for free hosting. Triggers on: "deploy to GitHub Pages", "host on GitHub Pages", "free hosting for my affiliate site", "push to GitHub Pages", "GitHub Pages setup", "deploy my landing page to GitHub", "host my bio link on GitHub", "free affiliate website hosting", "github pages affiliate", "set up GitHub Pages for my site", "deploy HTML to GitHub", "free static hosting", "publish my affiliate page for free", "github pages custom domain".
13. pixeltable sickn33/antigravity-awesome-skills · score 89/100 (grade B)
    Build Pixeltable apps in app.py with tables, computed columns, embedding search, and HTTP routes. Use for RAG, media pipelines, and tool-calling agents.
14. shopify-firebase-app sickn33/antigravity-awesome-skills · score 89/100 (grade B)
    Scaffold, extend and deploy Shopify embedded apps on Firebase with Nitrogen. Use for a Shopify app on Cloud Functions, Firestore and Hosting, or inside a Nitrogen project.
15. waf-setup sickn33/antigravity-awesome-skills · score 89/100 (grade B)
    Deploy and tune Web Application Firewalls. Configure rules for OWASP
16. cloudflare-workers sickn33/antigravity-awesome-skills · score 88/100 (grade B)
    Build and deploy edge functions with Cloudflare Workers and Wrangler.
17. dropthehassle-publish sickn33/antigravity-awesome-skills · score 88/100 (grade B)
    Publish a finished static site to a free HTTPS link with DropTheHassle: build it if needed, deploy, hand over the claim link, verify it is live, never spend money.
18. github-actions sickn33/antigravity-awesome-skills · score 88/100 (grade B)
    Build, test, and deploy applications using GitHub Actions workflows.
19. jenkins sickn33/antigravity-awesome-skills · score 88/100 (grade B)
    Create and manage Jenkins CI/CD pipelines, configure agents, manage plugins,
20. newman-cicd-integration sickn33/antigravity-awesome-skills · score 88/100 (grade B)
    Generate ready-to-use CI/CD pipeline configurations that install and run Newman for automated API testing.
21. production-audit sickn33/antigravity-awesome-skills · score 88/100 (grade B)
    Audit a shipped repo for production-readiness gaps across RLS, webhooks, secrets, grants, Stripe idempotency, mobile UX, and deployment health.
22. shipvela-publish sickn33/antigravity-awesome-skills · score 88/100 (grade B)
    Publish an explicitly selected website through the user's connected Shipvela MCP account, track the exact deployment, and return its live HTTPS URL after success.
23. argocd-gitops sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Implement GitOps with ArgoCD for declarative Kubernetes deployments.
24. container-registries sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Manage container registries including ECR, ACR, GCR, and Docker Hub.
25. gitlab-ci sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Configure GitLab CI/CD pipelines and runners for automated building,
26. helm-charts sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Create, manage, and deploy Helm charts for Kubernetes package management.
27. latchbio-integration K-Dense-AI/claude-scientific-skills · score 87/100 (grade B)
    Builds, registers, debugs, and operates bioinformatics workflows on Latch using the Python SDK, CLI, Latch Data and Registry, Nextflow, Snakemake, programmatic execution, and Latch MCP. Use when authoring or deploying Latch workflows, configuring resources or interfaces, moving data, integrating Registry, or launching and monitoring runs.
28. llmops-platform-engineering sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Build production LLMOps platforms with CI/CD, model promotion workflows,
29. model-serving-kubernetes sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA
30. openshift sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Manage Red Hat OpenShift clusters and deployments.
31. postman-newman-automation sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Generate Newman CLI commands, configuration files, Jenkins pipeline scripts, and shell automation for running Postman collections in CI/CD or local environments.
32. shipping-and-launch sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Prepares production launches. Use when preparing to deploy to production. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
33. vercel-deployments sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Deploy frontend and full-stack apps on Vercel with previews, edge functions,
34. vllm-server sickn33/antigravity-awesome-skills · score 87/100 (grade B)
    Deploy and manage vLLM for high-throughput LLM inference. Configure continuous
35. cloudflare-pages sickn33/antigravity-awesome-skills · score 86/100 (grade B)
    Deploy static sites and full-stack apps on Cloudflare Pages with previews,
36. docker-management sickn33/antigravity-awesome-skills · score 86/100 (grade B)
    Build, optimize, and troubleshoot Docker containers and images.
37. llm-gateway sickn33/antigravity-awesome-skills · score 86/100 (grade B)
    Deploy an API gateway for LLM traffic with load balancing, rate limiting,
38. pre-release-review sickn33/antigravity-awesome-skills · score 86/100 (grade B)
    Run a read-only pre-release review for deploy readiness, migrations, config, secrets, rollout order, rollback risk, and launch blockers.
39. pre-ship-gate sickn33/antigravity-awesome-skills · score 86/100 (grade B)
    A ship gate that runs before any production deploy: checks the silent failure modes that make a deploy 'succeed' while prod stays broken, then verifies the live revision instead of trusting deploy output.
40. azure-developer-cli github/awesome-copilot · score 85/100 (grade B)
    Design, create, review, migrate, or troubleshoot Azure Developer CLI (azd) projects using current Microsoft guidance. Use for azd, azure.yaml, AZD templates, Bicep or Terraform under infra, AZD environments and secrets, hooks, deployment workflows, and azd-managed CI/CD.
41. expo-deployment sickn33/antigravity-awesome-skills · score 85/100 (grade B)
    Deploy Expo apps with EAS: build and submit iOS and Android releases, configure build and submit profiles, manage versions and store metadata, and deploy web bundles or API routes.
42. netlify-deploy openai/skills · score 85/100 (grade B)
    Deploy web projects to Netlify using the Netlify CLI (`npx netlify`). Use when the user asks to deploy, host, publish, or link a site/repo on Netlify, including preview and production deploys.
43. opentofu-migration sickn33/antigravity-awesome-skills · score 84/100 (grade B)
    Migrate from Terraform to OpenTofu with state compatibility, provider
44. sshepherd sickn33/antigravity-awesome-skills · score 84/100 (grade B)
    Zero-knowledge SSH ops CLI — server health checks, docker/systemd control, log tailing, Postgres introspection, and declarative deploys, without ever exposing credentials to the agent.
45. ai-pipeline-orchestration sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Orchestrate AI/ML pipelines for data ingestion, model training, batch
46. ai-security-hardening sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Harden AI/LLM deployments against prompt injection, data exfiltration,
47. apple-container sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Build, run, and manage OCI/Linux containers as lightweight per-container VMs on Apple-silicon macOS using Apple's open-source container CLI, no Docker daemon required.
48. blue-green-deploy sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Configure zero-downtime deployment strategies including blue-green, canary,
49. cellprofiler K-Dense-AI/claude-scientific-skills · score 83/100 (grade B)
    Runs reproducible CellProfiler microscopy pipelines for nuclear segmentation, cell counts, and per-object fluorescence measurements. Supports image/channel manifests, headless batch execution, segmentation overlays, and measurement QC for 2D fluorescence assays.
50. client-secret-exposure-audit sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Audit a deployed web app for secrets exposed to the browser: hardcoded API keys/tokens in JS, secrets in HTML meta/attributes/comments, publicly reachable source/config/deploy files, and header/CORS misconfig.
51. docker-compose sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Define and run multi-container Docker applications using Docker Compose.
52. gha-security-review sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Review GitHub Actions workflows for exploitable vulnerabilities with concrete attack paths and harmless proof-of-concept sketches. Require explicit authorization for active testing.
53. meteora-dlmm-pool-screening sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Screen and rank Meteora DLMM pools for LP quality using public Meteora APIs (fee/TVL, bin step, organic score). Read-only: never deploys, swaps, or signs.
54. turborepo-caching sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Configure Turborepo for efficient monorepo builds with local and remote caching. Use when setting up Turborepo, optimizing build pipelines, or implementing distributed caching.
55. using-n8n-mcp-skills sickn33/antigravity-awesome-skills · score 83/100 (grade B)
    Route n8n MCP workflow design, editing, validation, testing, deployment, credential, execution, and debugging tasks to specialist guidance.
56. aws-agentic-ai sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations.
57. aws-lambda sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    Build and deploy serverless functions on AWS Lambda. Configure triggers,
58. azure-aks sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    Deploy and manage Azure Kubernetes Service clusters. Configure node pools,
59. ci-cd-and-automation sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
60. expo-cicd-workflows sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    Helps understand and write EAS workflow YAML files for Expo projects. Use this skill when the user asks about CI/CD or workflows in an Expo or EAS context, mentions .eas/workflows/, or wants help with EAS build pipelines or deployment automation.
61. histolab K-Dense-AI/claude-scientific-skills · score 82/100 (grade B)
    Extracts and preprocesses whole-slide histology image tiles with Histolab. Use for WSI inspection, tissue masks, random/grid/score-based tile extraction, H&E stain normalization, and tile dataset preparation. For multiplexed imaging or deep learning inference pipelines, use pathml.
62. huggingface-spaces sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants.
63. logic-fix-all sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    Autonomous repository-wide audit-and-fix pipeline: health → review → locate/explain → fix → diff-verify → iterate until clean. Starts with a mandatory consent prompt (token-intensive); after consent runs hands-free.
64. minimax-docx MiniMax-AI/skills · score 82/100 (grade B)
    Professional DOCX document creation, editing, and formatting using OpenXML SDK (.NET). Three pipelines: (A) create new documents from scratch, (B) fill/edit content in existing documents, (C) apply template formatting with XSD validation gate-check. MUST use this skill whenever the user wants to produce, modify, or format a Word document — including when they say "write a report", "draft a proposal", "make a contract", "fill in this form", "reformat to match this template", or any task whose final output is a .docx file. Even if the user doesn't mention "docx" explicitly, if the task implies a printable/formal document, use this skill.
65. mirrord sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    Run a local process inside a live Kubernetes cluster's network, env and traffic with mirrord, so changes are tested against real services without deploying.
66. remote-gpu-trainer sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    Deploy, monitor, and debug long GPU jobs on RENTED/remote instances (AutoDL, RunPod, vast.ai, Lambda, Slurm, K8s): teardown/billing safety, spot resilience, resumable checkpointing, OOM/NaN triage.
67. render-deploy openai/skills · score 82/100 (grade B)
    Deploy applications to Render by analyzing codebases, generating render.yaml Blueprints, and providing Dashboard deeplinks. Use when the user wants to deploy, host, publish, or set up their application on Render's cloud platform.
68. terraform-patterns alirezarezvani/claude-skills · score 82/100 (grade B)
    Terraform infrastructure-as-code agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Covers module design patterns, state management strategies, provider configuration, security hardening, policy-as-code with Sentinel/OPA, and CI/CD plan/apply workflows. Use when: user wants to design Terraform modules, manage state backends, review Terraform security, implement multi-region deployments, or follow IaC best practices.
69. vercel-optimize sickn33/antigravity-awesome-skills · score 82/100 (grade B)
    Audit deployed Vercel apps for cost and performance issues using metrics, project config, code scans, and version-aware recommendations.
70. aws-ecs-fargate sickn33/antigravity-awesome-skills · score 81/100 (grade B)
    Deploy containers on ECS and Fargate. Configure task definitions, services,
71. bio-link-deployer Affitor/affiliate-skills · score 81/100 (grade B)
    Create a Linktree-style bio link hub page as a single self-contained HTML file. Triggers on: "create a bio link page", "make a linktree", "link in bio page", "bio link", "link hub", "create my link page", "all my links on one page", "linktree alternative", "build a link page", "bio page for my affiliate links", "I need a link in bio", "make a page with all my links", "link aggregator page".
72. chaos-engineering alirezarezvani/claude-skills · score 81/100 (grade B)
    Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets).
73. gpu-kubernetes-operations sickn33/antigravity-awesome-skills · score 81/100 (grade B)
    Operate GPU-backed Kubernetes clusters for AI inference and training
74. hugging-face-model-trainer sickn33/antigravity-awesome-skills · score 81/100 (grade B)
    Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment.
75. kubernetes-hardening sickn33/antigravity-awesome-skills · score 81/100 (grade B)
    Implement Kubernetes security contexts, Pod Security Standards, and network
76. mise-configurator sickn33/antigravity-awesome-skills · score 81/100 (grade B)
    Generate production-ready mise.toml setups for local development, CI/CD pipelines, and toolchain standardization.
77. monte-carlo-performance-diagnosis sickn33/antigravity-awesome-skills · score 81/100 (grade B)
    Diagnoses pipeline performance issues -- slow jobs, expensive queries, latency trends -- using Monte Carlo's cross-platform observability. Uses a tiered investigation approach: discover problems, bridge to affected tables, then drill into root causes.
78. runbook-generator alirezarezvani/claude-skills · score 81/100 (grade B)
    Generate operational runbooks from a service name — deployment, incident response, maintenance, and rollback workflows. Templated structure customizable per environment. Use when documenting on-call procedures for a new service, standardizing incident response across teams, or producing runbooks before launching to production.
79. squirrel sickn33/antigravity-awesome-skills · score 81/100 (grade B)
    Full-cycle AI coding skill: plans, builds, tests, lints, fixes bugs, and writes production-grade docs. Auto-detects project state and adapts its 8-phase pipeline.
80. terraform-skill antonbabenko/terraform-skill · score 81/100 (grade B)
    Use when writing, reviewing, or debugging Terraform/OpenTofu modules, tests, CI, scans, or state ops - diagnoses failure mode (identity churn, secrets, blast radius, CI drift, state corruption) with version-aware guards.
81. transformers K-Dense-AI/claude-scientific-skills · score 81/100 (grade B)
    Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Applies when working with AutoModel, pipelines, tokenizers, generation configs, or TrainingArguments within Transformers.
82. vector-database-ops sickn33/antigravity-awesome-skills · score 81/100 (grade B)
    Deploy, manage, and optimize vector databases for AI applications.
83. vercel-cli-with-tokens sickn33/antigravity-awesome-skills · score 81/100 (grade B)
    Deploy and manage projects on Vercel using token-based authentication. Use when working with Vercel CLI using access tokens rather than interactive login — e.g. \"deploy to vercel\", \"set up vercel\", \"add environment variables to vercel\".
84. apk-redteam-pipeline sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    End-to-end Android APK red-team pipeline
85. azd-deployment sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Prepare and verify authorized Azure Container Apps deployments with azd. Use for azure.yaml, Bicep, remote builds, managed identity and post-deploy checks.
86. azure-ai-projects-dotnet sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.
87. azure-resource-manager-mysql-dotnet sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Azure MySQL Flexible Server SDK for .NET. Database management for MySQL Flexible Server deployments.
88. azure-resource-manager-postgresql-dotnet sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Azure PostgreSQL Flexible Server SDK for .NET. Database management for PostgreSQL Flexible Server deployments.
89. backend-development-feature-development sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Orchestrate end-to-end backend feature development from requirements to deployment. Use when coordinating multi-phase feature delivery across teams and services.
90. ci-fix warpdotdev/oz-skills · score 80/100 (grade B)
    Diagnose and fix GitHub Actions CI failures. Inspects workflow runs and logs, identifies root causes, implements minimal fixes, and pushes to a fix branch. Use when CI is failing, red, broken, or needs diagnosis.
91. gcp-cloud-functions sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Deploy serverless functions on Google Cloud Functions. Configure triggers
92. github-actions-debugger sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Specialized skill for diagnosing, analyzing, and fixing failing GitHub Actions workflows by parsing run logs and pipeline definitions.
93. google-cloud-storage-bucket-architect google/skills · score 80/100 (grade B)
    Creates Cloud Storage on Google Cloud (also known colloquially as GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.
94. hunt-cicd sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Hunt CI/CD pipeline vulnerabilities
95. hunt-rag-vector sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP
96. k8s-security-policies sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Guide for NetworkPolicy, RBAC, and Pod Security Admission/Standards (PodSecurityPolicy was removed in Kubernetes1.25) in Kubernetes.
97. kubestellar-console sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Multi-cluster Kubernetes dashboard with AI-powered operations via MCP server and 10+ built-in agent skills
98. msstore-cli github/awesome-copilot · score 80/100 (grade B)
    Microsoft Store Developer CLI (msstore) for publishing Windows applications to the Microsoft Store. Use when asked to configure Store credentials, list Store apps, check submission status, publish submissions, manage package flights, set up CI/CD for Store publishing, or integrate with Partner Center. Supports Windows App SDK/WinUI, UWP, .NET MAUI, Flutter, Electron, React Native, and PWA applications.
99. openclaw-deployment-hardening sickn33/antigravity-awesome-skills · score 80/100 (grade B)
    Secure OpenClaw deployments with preflight hardening checks, CI/CD guardrails,
100. recsys-pipeline-architect sickn33/antigravity-awesome-skills · score 80/100 (grade B)
     Designs composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework
101. scikit-learn sickn33/antigravity-awesome-skills · score 80/100 (grade B)
     Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.
102. service-mesh-observability sickn33/antigravity-awesome-skills · score 80/100 (grade B)
     Complete guide to observability patterns for Istio, Linkerd, and service mesh deployments.
103. terraform-module-library sickn33/antigravity-awesome-skills · score 80/100 (grade B)
     Production-ready Terraform module patterns for AWS, Azure, and GCP infrastructure.
104. vercel-automation sickn33/antigravity-awesome-skills · score 80/100 (grade B)
     Automate Vercel tasks via Rube MCP (Composio): manage deployments, domains, DNS, env vars, projects, and teams. Always search tools first for current schemas.
105. agent-platform-prompt-management google/skills · score 79/100 (grade C)
     Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
106. application-integration-basics google/skills · score 79/100 (grade C)
     Explains Google Cloud Application Integration concepts, region provisioning, IAM roles, run-as service accounts, interface choices (REST, Python discovery client, Terraform, ADK), and platform limits. Use when setting up Application Integration in a Google Cloud project, enabling required APIs, provisioning a region, configuring IAM or run-as service accounts, choosing between Application Integration and custom code, or checking quotas and timeouts. Do not use for building, publishing, or invoking integrations (use application-integration-authoring) or diagnosing failed or stuck runs and logs (use application-integration-troubleshooting).
107. arm-templates sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Deploy Azure resources with ARM templates and Bicep. Create modular deployments
108. circleci-automation sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Automate CircleCI tasks via Rube MCP (Composio): trigger pipelines, monitor workflows/jobs, retrieve artifacts and test metadata. Always search tools first for current schemas.
109. cloudformation sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Deploy AWS resources with CloudFormation templates. Create stacks, use
110. deployment-pipeline-design sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies.
111. github-actions-templates sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Production-ready GitHub Actions workflow patterns for testing, building, and deploying applications.
112. gtm-partnership-architecture github/awesome-copilot · score 79/100 (grade C)
     Build and scale partner ecosystems that drive revenue and platform adoption. Use when building partner programs from scratch, tiering partnerships, managing co-marketing, making build-vs-partner decisions, or structuring crawl-walk-run partner deployment.
113. k6-load-testing sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Comprehensive k6 load testing skill for API, browser, and scalability testing. Write realistic load scenarios, analyze results, and integrate with CI/CD.
114. kubernetes-ops sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Deploy, scale, and manage Kubernetes workloads.
115. kustomize sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Customize Kubernetes manifests without templating using Kustomize.
116. llm-inference-scaling sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Auto-scale LLM inference clusters on Kubernetes using KEDA, custom GPU
117. matchms K-Dense-AI/claude-scientific-skills · score 79/100 (grade C)
     Processes, cleans, compares, and searches tandem mass spectra with matchms. Use for MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity, library matching, score matrices, and molecular-similarity networks. Use pyopenms instead for LC-MS feature detection or proteomics pipelines.
118. modal K-Dense-AI/claude-scientific-skills · score 79/100 (grade C)
     Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
119. multi-platform-apps-multi-platform sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Build and deploy the same feature consistently across web, mobile, and desktop platforms using API-first architecture and parallel implementation strategies.
120. neon-functions sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Long-running, serverless Node.js HTTP functions deployed onto your Neon branch, with DATABASE_URL injected automatically and compute that runs next to your data.
121. quarkus-verification affaan-m/ECC · score 79/100 (grade C)
     Verification loop for Quarkus projects: build, static analysis (Checkstyle, PMD, SpotBugs), tests with JaCoCo coverage, OWASP dependency and container security scans, GraalVM native compilation, health checks, and config validation. Use when verifying a Quarkus service before a PR, after major refactoring or dependency upgrades, or pre-deploy.
122. quarkus-verification affaan-m/everything-claude-code · score 79/100 (grade C)
     Verification loop for Quarkus projects: build, static analysis (Checkstyle, PMD, SpotBugs), tests with JaCoCo coverage, OWASP dependency and container security scans, GraalVM native compilation, health checks, and config validation. Use when verifying a Quarkus service before a PR, after major refactoring or dependency upgrades, or pre-deploy.
123. redirect-301-generator sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     301 Redirect Generator. Use when working with source-to-destination mapping and deployment platform.
124. scikit-learn K-Dense-AI/claude-scientific-skills · score 79/100 (grade C)
     Supports machine learning in Python with scikit-learn. Applies when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
125. security-automation sickn33/antigravity-awesome-skills · score 79/100 (grade C)
     Automate security workflows and remediation. Build security pipelines,
126. shipping-and-launch addyosmani/agent-skills · score 79/100 (grade C)
     Prepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
127. slo-architect alirezarezvani/claude-skills · score 79/100 (grade C)
     Use when defining, reviewing, or operating SLOs/SLIs/error budgets. Triggers on "define an SLO", "what should our SLO be", "error budget", "burn rate", "SLI", "service level objective", "Google SRE workbook", "multi-window burn-rate alert", or any reliability-target question. Ships SLO designer, error-budget calculator with multi-window burn-rate thresholds, and SLO reviewer that catches the common bugs (target too aggressive, window too short, conflicting SLOs, no SLI definition). 4 references on SLO principles + SLI design + error budget math + composition with feature-flags-architect/chaos-engineering/kubernetes-operator. NOT a generic observability skill — specifically the SLO discipline.
128. agent-platform-tuning-management google/skills · score 78/100 (grade C)
     Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use `agent-platform-tuning`), deploying models to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).
129. airflow-dag-patterns sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
130. appdeploy sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl.
131. audit-agent-run-evidence sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Use when an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without trusting self-reported success.
132. bash-pro sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Master of defensive Bash scripting for production automation, CI/CD
133. deploy-to-vercel sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Deploy applications and websites to Vercel. Use when the user requests deployment actions like \"deploy my app\", \"deploy and give me the link\", \"push this live\", or \"create a preview deployment\".
134. gcp-gke sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Deploy and manage Google Kubernetes Engine clusters. Configure node pools,
135. git-hooks-automation sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Master Git hooks setup with Husky, lint-staged, pre-commit framework, and commitlint. Automate code quality gates, formatting, linting, and commit message enforcement before code reaches CI.
136. gitlab-ci-patterns sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Comprehensive GitLab CI/CD pipeline patterns for automated testing, building, and deployment.
137. gitops-workflow sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Complete guide to implementing GitOps workflows with ArgoCD and Flux for automated Kubernetes deployments.
138. google-cloud-storage-basics google/skills · score 78/100 (grade C)
     Stores, retrieves, and manages data as objects in Cloud Storage on Google Cloud (also known colloquially as GCS) buckets. Use when you need to interact with Cloud Storage — set up a Storage MCP server (remote or local Toolbox), create or configure buckets, upload, download, stream, or transfer data, organize objects with folders, generate signed URLs, control access (IAM, ACLs, public access prevention), set storage classes (Standard, Nearline, Coldline, Archive), manage lifecycle and cost, protect data (versioning, CMEK, retention, Bucket Lock, holds, soft delete), host static websites, trigger Pub/Sub notifications, mount buckets (gcsfuse), or optimize performance. Covers gcloud storage / gsutil, JSON/XML APIs, client libraries, Terraform, and Cloud Storage MCP servers. Don't use for generic MCP setup that doesn't mention Cloud Storage, other MCP servers, block storage (Persistent Disk), BigQuery, or databases (Cloud SQL, Spanner, Bigtable, Firestore).
139. impediment-prioritization github/awesome-copilot · score 78/100 (grade C)
     Ranks any list of impediments and their countermeasures using a value-stream scoring model (ROI, Cost to Implement, Ease of Deployment, Risk Factor) and a fixed prioritization formula. Use when someone asks to prioritize, rank, sequence, or triage impediments, countermeasures, remediation items, risks, findings, gaps, action items, or backlog entries; or mentions value-stream prioritization, A3 / lean countermeasure ranking, ROI vs. effort scoring, or building a remediation / improvement backlog. Works with GHQR findings, audit results, retrospective action items, risk registers, architecture review gaps, or any free-form `{impediment, countermeasure}` list.
140. ios-redteam-pipeline sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     End-to-end iOS red-team pipeline
141. istio-traffic-management sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Comprehensive guide to Istio traffic management for production service mesh deployments.
142. jq sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.
143. kubernetes-deployment sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Kubernetes deployment workflow for container orchestration, Helm charts, service mesh, and production-ready K8s configurations.
144. linkerd-patterns sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Production patterns for Linkerd service mesh - the lightweight, security-first service mesh for Kubernetes.
145. machine-learning-ops-ml-pipeline sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Design and implement a complete ML pipeline for: $ARGUMENTS
146. mcp-tool-developer sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Build Model Context Protocol (MCP) servers and tools from scratch. Full-stack MCP development with TypeScript/Python, testing, deployment, and registry publishing.
147. production-runtime-certification sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Certify a deployed service with fresh evidence across source, CI, migrations, runtime health, readiness, and critical routes; use before declaring a release production-ready.
148. pyhealth K-Dense-AI/claude-scientific-skills · score 78/100 (grade C)
     Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.
149. pyopenms K-Dense-AI/claude-scientific-skills · score 78/100 (grade C)
     Processes mass spectrometry data with pyOpenMS. Supports proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.
150. render-automation sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Automate Render tasks via Rube MCP (Composio): services, deployments, projects. Always search tools first for current schemas.
151. terraform-azure sickn33/antigravity-awesome-skills · score 78/100 (grade C)
     Provision Azure infrastructure with Terraform. Configure providers, manage
152. terraform-style-check warpdotdev/oz-skills · score 78/100 (grade C)
     Generate Terraform HCL code following HashiCorp's official style conventions and best practices. Use when writing, reviewing, or generating Terraform configurations.
153. test-value tt-a1i/archify · score 78/100 (grade C)
     Improve test value and feedback cost when auditing slow, flaky, redundant or brittle tests; choosing tests during implementation; or reviewing test changes before a commit or PR. Keep ordinary implementation and pre-commit use scoped to the affected behavior, not a repository-wide audit.
154. api-integration sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Designs event-driven architectures, webhook systems, API chaining flows, ETL pipelines, and integration patterns between services.
155. apify-actor-development sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Create, modify and validate Apify Actors with scoped inputs, storage schemas, runtime logic and deployment review.
156. apify-actorization sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
157. application-design-center-design-deploy google/skills · score 77/100 (grade C)
     Processes GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.
158. aws-cdk-python-setup github/awesome-copilot · score 77/100 (grade C)
     Setup and initialization guide for developing AWS CDK (Cloud Development Kit) applications in Python. This skill enables users to configure environment prerequisites, create new CDK projects, manage dependencies, and deploy to AWS.
159. biopython K-Dense-AI/claude-scientific-skills · score 77/100 (grade C)
     Provides Biopython workflows for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Supports batch processing, custom molecular-biology pipelines, BLAST automation, structure analysis, and motif analysis.
160. ci-cd-pipeline-builder alirezarezvani/claude-skills · score 77/100 (grade C)
     Generate pragmatic CI/CD pipelines from detected project stack signals — fast baseline generation, repeatable checks, environment-aware deployment stages. Use when setting up CI for a new project, refactoring existing pipelines, or standardizing deployment workflows across multiple repos.
161. database-migration sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Master database schema and data migrations across ORMs (Sequelize, TypeORM, Prisma), including rollback strategies and zero-downtime deployments.
162. docker-development alirezarezvani/claude-skills · score 77/100 (grade C)
     Docker and container development agent skill and plugin for Dockerfile optimization, docker-compose orchestration, multi-stage builds, and container security hardening. Use when: user wants to optimize a Dockerfile, create or improve docker-compose configurations, implement multi-stage builds, audit container security, reduce image size, or follow container best practices. Covers build performance, layer caching, secret management, and production-ready container patterns.
163. earllm-build sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Build, maintain, and extend the EarLLM One Android project — a Kotlin/Compose app that connects Bluetooth earbuds to an LLM via voice pipeline.
164. flutter-expert sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Master Flutter development with Dart 3, advanced widgets, and multi-platform deployment.
165. github-workflow-automation sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Patterns for automating GitHub workflows with AI assistance, inspired by [Gemini CLI](https://github.com/google-gemini/gemini-cli) and modern DevOps practices.
166. gitlab-automation sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Automate GitLab project management, issues, merge requests, pipelines, branches, and user operations via Rube MCP (Composio). Always search tools first for current schemas.
167. import-infrastructure-as-code github/awesome-copilot · score 77/100 (grade C)
     Import existing Azure resources into Terraform using Azure CLI discovery and Azure Verified Modules (AVM). Use when asked to reverse-engineer live Azure infrastructure, generate Infrastructure as Code from existing subscriptions/resource groups/resource IDs, map dependencies, derive exact import addresses from downloaded module source, prevent configuration drift, and produce AVM-based Terraform files ready for validation and planning across any Azure resource type.
168. monte-carlo-monitor-creation sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Guides creation of Monte Carlo monitors via MCP tools, producing monitors-as-code YAML for CI/CD deployment.
169. odoo-docker-deployment sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Production-ready Docker and docker-compose setup for Odoo with PostgreSQL, persistent volumes, environment-based configuration, and Nginx reverse proxy.
170. pipedrive-automation sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Automate Pipedrive CRM operations including deals, contacts, organizations, activities, notes, and pipeline management via Rube MCP (Composio). Always search tools first for current schemas.
171. plugin-audit alirezarezvani/claude-skills · score 77/100 (grade C)
     Comprehensive audit pipeline for skills, plugins, agents, and commands. Validates structure, quality, security, marketplace compliance, cross-platform compatibility, and ecosystem integration. Runs all built-in validation tools, invokes domain-appropriate agents for code review, and produces a pass/fail gate report. Usage: /plugin-audit <skill-path>
172. pytorch-lightning K-Dense-AI/claude-scientific-skills · score 77/100 (grade C)
     Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
173. revenue-operations alirezarezvani/claude-skills · score 77/100 (grade C)
     Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization. Use when analyzing sales pipeline coverage, forecasting revenue, evaluating go-to-market performance, reviewing sales metrics, assessing pipeline analysis, tracking forecast accuracy with MAPE, calculating GTM efficiency, or measuring sales efficiency and unit economics for SaaS teams.
174. seo-aeo-orchestrator sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Runs an audit-first SEO/AEO growth workflow from project discovery through implementation, foundational content, measurement setup, deployment verification, and optional weekly monitoring.
175. setup-deploy garrytan/gstack · score 77/100 (grade C)
     Configure deployment settings for /land-and-deploy.
176. ship-gate alirezarezvani/claude-skills · score 77/100 (grade C)
     Pre-production audit that scans a codebase for security, database, deployment, code quality, AI/LLM, dependency, frontend, and observability issues. Intercepts deploy commands and blocks until critical items pass. Stack-agnostic. Use for "run ship gate", "am I ready to ship", "pre-launch audit", "can I deploy", "push to production", "go live checklist", "preflight check". Not for CI/CD setup or infra provisioning.
177. spark-optimization sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
178. terraform-infrastructure sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Terraform infrastructure as code workflow for provisioning cloud resources, creating reusable modules, and managing infrastructure at scale.
179. unity-developer sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Build Unity games with optimized C# scripts, efficient rendering, and proper asset management. Masters Unity 6 LTS, URP/HDRP pipelines, and cross-platform deployment.
180. weather-pipeline-performance-diagnosis sickn33/antigravity-awesome-skills · score 77/100 (grade C)
     Diagnose slow weather-data workflows by measuring discovery, transfer, parsing, scientific processing, and rendering separately before changing code.
181. workflow-builder alirezarezvani/claude-skills · score 77/100 (grade C)
     Design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.
182. ai-engineering-toolkit sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
183. antithesis-setup antithesishq/antithesis-skills · score 76/100 (grade C)
     Scaffold the Antithesis harness with docker-compose: initialize the working directory, write Dockerfiles and docker-compose.yaml with build directives, and prepare to submit your first Antithesis test run. If the desired setup is Kubernetes, defer to the antithesis-setup-k8s skill.
184. application-integration-troubleshooting google/skills · score 76/100 (grade C)
     Diagnoses failed, stuck, or silent Google Cloud Application Integration runs and Integration Connectors tasks using the REST API, Cloud Logging, and Cloud Monitoring. Use when an integration fails, returns HTTP 200 without doing the expected work, hangs in a suspended or retry state, or hits IAM, run-as service account, connector health, or quota errors. Do not use for initial region provisioning (use application-integration-basics) or building and deploying versions (use application-integration-authoring).
185. azure-ai-projects-ts sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
186. azure-logic-apps-standard-cli github/awesome-copilot · score 76/100 (grade C)
     Create, inspect, validate, test, run, debug, package, and deploy Azure Logic Apps Standard projects using the Azure CLI logicapp extension. Use for Standard workflow authoring, optional workspaces, connector discovery, identity-based connections, local runtimes, native workflow tests, and approved cloud lifecycle operations. Not for Consumption-only workflow development.
187. bash-defensive-patterns sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Master defensive Bash programming techniques for production-grade scripts. Use when writing robust shell scripts, CI/CD pipelines, or system utilities requiring fault tolerance and safety.
188. cloudflare-deploy openai/skills · score 76/100 (grade C)
     Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.
189. daily-gift sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Relationship-aware daily gift engine with five-stage creative pipeline — editorial judgment, synthesis, concept generation, visual strategy, and rendering in H5, image, or video
190. data-engineering-data-pipeline sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
191. deployment-validation-config-validate sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Validate application configurations with schemas, configuration tests, and checks for security and environment consistency.
192. devops-pipeline-builder sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Design and implement CI/CD pipelines, Docker configurations, deployment
193. diagram-design cathrynlavery/diagram-design · score 76/100 (grade C)
     Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, service blueprint, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.
194. engineering-advanced-skills alirezarezvani/claude-skills · score 76/100 (grade C)
     Index of 37 advanced engineering agent skills for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Use when browsing or choosing among the POWERFUL-tier engineering skills: agent design, RAG, MCP servers, CI/CD, database design, observability, security auditing, changelog/release automation, reliability (SLO/chaos/flags/operators), platform ops.
195. engineering-skills alirezarezvani/claude-skills · score 76/100 (grade C)
     Index of the engineering-team skills bundle for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw, and 6 more tools. Architecture, frontend, backend, QA, DevOps, security, AI/ML, data engineering, Playwright, Stripe, AWS, MS365 (stdlib-only Python tools). Use when browsing or choosing among engineering-team role skills — load only the one specialist SKILL.md you need, never bulk-load the bundle.
196. eu-ai-act-specialist alirezarezvani/claude-skills · score 76/100 (grade C)
     EU AI Act (Regulation (EU) 2024/1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system — prohibited (Art. 5), high-risk (Art. 6 + Annex III), limited-risk (Art. 50), or minimal-risk? (2) For high-risk systems, what's the Article 43 conformity assessment route (Module A internal control vs Module H full QMS + notified body) and what goes in the Annex IV technical documentation? (3) Per organizational role (provider / deployer / importer / distributor / authorized representative), what are the active obligations and deadlines? Use during AI system intake review, when planning conformity assessment, or when scoping deployer obligations. Cites Articles + Annexes for every output. NOT executive AI strategy (see chief-ai-officer-advisor). NOT a legal substitute.
197. flowstudio-power-automate-build github/awesome-copilot · score 76/100 (grade C)
     Build, scaffold, and deploy Power Automate cloud flows using the FlowStudio MCP server. Your agent constructs flow definitions, wires connections, deploys, and tests — all via MCP without opening the portal. Load this skill when asked to: create a flow, build a new flow, deploy a flow definition, scaffold a Power Automate workflow, construct a flow JSON, update an existing flow's actions, patch a flow definition, add actions to a flow, wire up connections, or generate a workflow definition from scratch. Requires a FlowStudio MCP subscription — see https://mcp.flowstudio.app
198. gcp-cloud-architect alirezarezvani/claude-skills · score 76/100 (grade C)
     Design GCP architectures for startups and enterprises. Use when asked to design Google Cloud infrastructure, deploy to GKE or Cloud Run, configure BigQuery pipelines, optimize GCP costs, or migrate to GCP. Covers Cloud Run, GKE, Cloud Functions, Cloud SQL, BigQuery, and cost optimization.
199. gh-fix-ci openai/skills · score 76/100 (grade C)
     Use when a user asks to debug or fix failing GitHub PR checks that run in GitHub Actions; use `gh` to inspect checks and logs, summarize failure context, draft a fix plan, and implement only after explicit approval. Treat external providers (for example Buildkite) as out of scope and report only the details URL.
200. gke-workload-identity google/skills · score 76/100 (grade C)
     Configures and diagnoses Workload Identity Federation for GKE authentication failures for Pods (403 "iam.serviceAccounts.getAccessToken" / permission denied, "could not find default credentials", or GKE metadata server unreachable) by verifying cluster and node-pool Workload Identity configuration, the Kubernetes ServiceAccount (KSA) to IAM binding (direct principal binding and legacy Google ServiceAccount impersonation), target-resource IAM roles, and gke-metadata-server health. Use when setting up KSA/GSA bindings (`roles/iam.workloadIdentityUser`, `iam.gke.io/gcp-service-account`) or when a Pod cannot authenticate to Google Cloud APIs. Don't use for in-cluster Kubernetes RBAC errors (API-server authorization), general workload crashes (use gke-workload-troubleshooting), or Pod Security Standards and NetworkPolicies (use gke-workload-security).
201. kubernetes-operator alirezarezvani/claude-skills · score 76/100 (grade C)
     Use when building a Kubernetes Operator — custom controllers that reconcile CRD state. Triggers on "build an operator", "CRD design", "reconcile loop", "controller-runtime", "kubebuilder", "operator-sdk", "metacontroller", "KOPF", "operator capability levels", or "custom resource". Ships CRD validator, reconcile-loop linter, and OperatorHub capability auditor (all stdlib Python), 4 references on the operator pattern + CRD design + reconcile patterns + tooling landscape, and a /operator-audit slash command. NOT a generic k8s skill — specifically the Operator pattern.
202. ml-pipeline-workflow sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
203. mle-workflow affaan-m/ECC · score 76/100 (grade C)
     Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
204. mle-workflow affaan-m/everything-claude-code · score 76/100 (grade C)
     Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
205. phoenix-tracing github/awesome-copilot · score 76/100 (grade C)
     OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
206. prompt-governance alirezarezvani/claude-skills · score 76/100 (grade C)
     Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt regressions, or creating eval pipelines for production AI features. Triggers: 'manage prompts in production', 'prompt versioning', 'prompt regression', 'prompt A/B test', 'prompt registry', 'eval pipeline'. NOT for writing or improving individual prompts (use senior-prompt-engineer). NOT for RAG pipeline design (use rag-architect). NOT for LLM cost reduction (use llm-cost-optimizer).
207. rowan K-Dense-AI/claude-scientific-skills · score 76/100 (grade C)
     Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.
208. skill-tester alirezarezvani/claude-skills · score 76/100 (grade C)
     Validate, test, and score the quality of skills within the claude-skills ecosystem. Comprehensive meta-skill: structure validation, Python script testing (syntax + imports + runtime + output format), multi-dimensional quality scoring with letter grades and tier classification (BASIC/STANDARD/POWERFUL). Use when authoring a new skill, auditing existing skills for tier promotion, setting up pre-commit hooks for skill quality, or integrating skill QA into CI.
209. terraform-aws sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Provision AWS infrastructure with Terraform. Create modules, manage state,
210. terraform-gcp sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Provision GCP infrastructure with Terraform. Configure providers and
211. vercel-deploy openai/skills · score 76/100 (grade C)
     Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
212. voice-ai-engine-development sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support
213. web2-recon sickn33/antigravity-awesome-skills · score 76/100 (grade C)
     Web2 recon pipeline
214. writing-skills obra/superpowers · score 76/100 (grade C)
     Use when creating new skills, editing existing skills, or verifying skills work before deployment
215. aws-solution-architect alirezarezvani/claude-skills · score 75/100 (grade C)
     Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.
216. bats-testing-patterns sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Master Bash Automated Testing System (Bats) for comprehensive shell script testing. Use when writing tests for shell scripts, CI/CD pipelines, or requiring test-driven development of shell utilities.
217. bigquery-udfs google/skills · score 75/100 (grade C)
     Creates, deploys, and troubleshoots BigQuery User-Defined Functions (UDFs) and Remote Functions across SQL, JavaScript, Python, and Cloud Run endpoints. Use when writing custom scalar or aggregate functions in BigQuery, installing PyPI packages or Cloud Storage libraries in a Python or JavaScript UDF, or calling Google Cloud and external APIs from a Python UDF (WITH CONNECTION) or Remote Function. Don't use for standard BigQuery table/dataset CRUD (use bigquery-basics), DataFrames client workflows (use bigquery-bigframes), or BigQuery ML models (use bigquery-ai-ml).
218. bio-workflows-biomarker-pipeline GPTomics/bioSkills · score 75/100 (grade C)
     End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, leakage-safe cross-validation, calibration, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.
219. bio-workflows-riboseq-pipeline GPTomics/bioSkills · score 75/100 (grade C)
     End-to-end Ribo-seq analysis from FASTQ through periodicity QC, P-site calibration, ORF detection, translation efficiency, and stalling. Use when orchestrating a full ribosome profiling pipeline and deciding harvest/dedup/alignment options and which downstream analyses the library can support.
220. candidate-talent-pool sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Candidate and prospect pool: contact details, experience, skills, consent status and date, referral source and last contact. Use for talent pipelines and re-engagement.
221. cloud-architect sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Expert cloud architect specializing in AWS/Azure/GCP multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns.
222. debate-review sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Two-model debate review of a GitHub PR, GitLab MR, Azure DevOps PR, or
223. deployment-procedures sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Production deployment principles and decision-making. Safe deployment workflows, rollback strategies, and verification. Teaches thinking, not scripts.
224. devops-deploy sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     DevOps e deploy de aplicacoes — Docker, CI/CD com GitHub Actions, AWS Lambda, SAM, Terraform, infraestrutura como codigo e monitoramento.
225. devops-troubleshooter sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability.
226. doublecheck github/awesome-copilot · score 75/100 (grade C)
     Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review.
227. efcore-d2-db-diagram github/awesome-copilot · score 75/100 (grade C)
     Generate D2 database diagrams from Entity Framework Core models. USE FOR: EF Core database diagram, Entity Framework Core ERD, DbContext diagram, C# entity relationship diagram, PostgreSQL schema visualization, generate .d2 file from EF Core entities, Fluent API mapping diagram, migrations-based database diagram, table relationships, owned types, many-to-many join tables, indexes and constraints. DO NOT USE FOR: runtime debugging, database migration execution, schema deployment, SQL performance tuning, or draw.io diagrams.
228. foundry-agent-sync github/awesome-copilot · score 75/100 (grade C)
     Create and synchronize prompt-based AI agents directly within Azure AI Foundry via REST API, from a local JSON manifest. Unlike scaffolding skills that only generate local code, this skill registers agents in the Foundry service itself — making them immediately available for invocation. Use when the user asks to create agents in Foundry, sync, deploy, register, or push agents to Foundry, update agent instructions, or scaffold the manifest and sync script for a new repository. Triggers: 'create agent in foundry', 'sync foundry agents', 'deploy agents to foundry', 'register agents in foundry', 'push agents', 'create foundry agent manifest', 'scaffold agent sync'.
229. gke-cluster-creation google/skills · score 75/100 (grade C)
     Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute). Use when creating GKE clusters, provisioning GKE environments, selecting cluster modes, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).
230. helm-chart-scaffolding sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Comprehensive guidance for creating, organizing, and managing Helm charts for packaging and deploying Kubernetes applications.
231. hunt-k8s sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Hunt Kubernetes & Docker
232. java-testcontainers github/awesome-copilot · score 75/100 (grade C)
     Write reliable integration tests in plain Java with Testcontainers 2.x: JUnit Jupiter lifecycle, singleton containers, reuse, generic containers, wait strategies, networks, init scripts, Docker Compose, CI and Docker/Podman setup. Use when adding or fixing Testcontainers tests, or migrating from 1.x. Framework-neutral.
233. k8s-manifest-generator sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Step-by-step guidance for creating production-ready Kubernetes manifests including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims.
234. laravel-verification affaan-m/ECC · score 75/100 (grade C)
     Verification loop for Laravel projects: env checks, linting, static analysis, tests with coverage, security scans, and deployment readiness. Use when verifying a Laravel project before merge or deploy — lint, static analysis, tests, coverage, security.
235. laravel-verification affaan-m/everything-claude-code · score 75/100 (grade C)
     Verification loop for Laravel projects: env checks, linting, static analysis, tests with coverage, security scans, and deployment readiness. Use when verifying a Laravel project before merge or deploy — lint, static analysis, tests, coverage, security.
236. mlops-engineer sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools.
237. network-config-validation affaan-m/ECC · score 75/100 (grade C)
     Pre-deployment checks for router and switch configuration, including dangerous commands, duplicate addresses, subnet overlaps, stale references, management-plane risk, and IOS-style security hygiene. Use when reviewing a router or switch configuration before deployment.
238. network-config-validation affaan-m/everything-claude-code · score 75/100 (grade C)
     Pre-deployment checks for router and switch configuration, including dangerous commands, duplicate addresses, subnet overlaps, stale references, management-plane risk, and IOS-style security hygiene. Use when reviewing a router or switch configuration before deployment.
239. santa-method affaan-m/ECC · score 75/100 (grade C)
     Multi-agent adversarial verification: two independent reviewers with the same rubric must both pass before output ships, with a fix-and-re-review convergence loop and human escalation cap. Use when gating publishing, production deploys, compliance or brand-sensitive content, or hallucination-prone claims before they ship.
240. santa-method affaan-m/everything-claude-code · score 75/100 (grade C)
     Multi-agent adversarial verification: two independent reviewers with the same rubric must both pass before output ships, with a fix-and-re-review convergence loop and human escalation cap. Use when gating publishing, production deploys, compliance or brand-sensitive content, or hallucination-prone claims before they ship.
241. senior-devops alirezarezvani/claude-skills · score 75/100 (grade C)
     Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes.
242. snowflake-development sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening.
243. temporal-python-pro sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Master Temporal workflow orchestration with Python SDK. Implements durable workflows, saga patterns, and distributed transactions. Covers async/await, testing strategies, and production deployment.
244. vercel-deployment sickn33/antigravity-awesome-skills · score 75/100 (grade C)
     Expert knowledge for deploying to Vercel with Next.js
245. a11y-audit alirezarezvani/claude-skills · score 74/100 (grade C)
     Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
246. agent-owasp-compliance github/awesome-copilot · score 74/100 (grade C)
     Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
247. agent-platform-alert-configuration google/skills · score 74/100 (grade C)
     Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.
248. agent-platform-deploy google/skills · score 74/100 (grade C)
     Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for running model evaluations (use `agent-platform-eval-flywheel` skill).
249. agent-platform-endpoint-management google/skills · score 74/100 (grade C)
     Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.
250. agentflow sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and recoverable pipeline design.
251. agentic-actions-auditor sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines.
252. ai-ml sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
253. azure-ai-ml-py sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
254. azure-cloud-architect alirezarezvani/claude-skills · score 74/100 (grade C)
     Design Azure architectures for startups and enterprises. Use when asked to design Azure infrastructure, create Bicep/ARM templates, optimize Azure costs, set up Azure DevOps pipelines, or migrate to Azure. Covers AKS, App Service, Azure Functions, Cosmos DB, and cost optimization.
255. azure-static-web-apps github/awesome-copilot · score 74/100 (grade C)
     Helps create, configure, and deploy Azure Static Web Apps using the SWA CLI. Use when deploying static sites to Azure, setting up SWA local development, configuring staticwebapp.config.json, adding Azure Functions APIs to SWA, or setting up GitHub Actions CI/CD for Static Web Apps.
256. bio-outlier-splicing-detection GPTomics/bioSkills · score 74/100 (grade C)
     Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.
257. brownfield-retrospective affaan-m/ECC · score 74/100 (grade C)
     Run the ECC engineering process retroactively against an existing project slice. Audit current state, reconstruct past decisions, produce a four-axis gap register (thinking, coding practice, missing features, technology selection), then execute fixes forward through the plan-TDD-review pipeline. Use when adopting ECC mid-project, taking over an inherited codebase, or asking what was missed and what would have been built differently from day one.
258. brownfield-retrospective affaan-m/everything-claude-code · score 74/100 (grade C)
     Run the ECC engineering process retroactively against an existing project slice. Audit current state, reconstruct past decisions, produce a four-axis gap register (thinking, coding practice, missing features, technology selection), then execute fixes forward through the plan-TDD-review pipeline. Use when adopting ECC mid-project, taking over an inherited codebase, or asking what was missed and what would have been built differently from day one.
259. capsule_crm-automation ComposioHQ/awesome-claude-skills · score 74/100 (grade C)
     Automate Capsule CRM tasks via Rube MCP (Composio): contacts, opportunities, cases, tasks, and pipeline management. Always search tools first for current schemas.
260. ci-cd-and-automation addyosmani/agent-skills · score 74/100 (grade C)
     Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
261. closed-loop-delivery sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     Use when a coding task must be completed against explicit acceptance criteria with minimal user re-intervention across implementation, review feedback, deployment, and runtime verification.
262. cloud-run-basics google/skills · score 74/100 (grade C)
     Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).
263. create-github-action-workflow-specification github/awesome-copilot · score 74/100 (grade C)
     Create a formal specification for an existing GitHub Actions CI/CD workflow, optimized for AI consumption and workflow maintenance.
264. django-pro sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     Master Django 5.x with async views, DRF, Celery, and Django Channels. Build scalable web applications with proper architecture, testing, and deployment.
265. django-verification affaan-m/ECC · score 74/100 (grade C)
     Run the full Django verification loop — environment check, mypy/ruff/black linting, migration safety, pytest with coverage targets, pip-audit and bandit security scans, settings and logging review, and diff review — producing a phased pass/fail report before release or PR. Use when preparing a Django pull request, validating migrations or coverage, or running pre-deploy readiness checks.
266. django-verification affaan-m/everything-claude-code · score 74/100 (grade C)
     Run the full Django verification loop — environment check, mypy/ruff/black linting, migration safety, pytest with coverage targets, pip-audit and bandit security scans, settings and logging review, and diff review — producing a phased pass/fail report before release or PR. Use when preparing a Django pull request, validating migrations or coverage, or running pre-deploy readiness checks.
267. docker_hub-automation ComposioHQ/awesome-claude-skills · score 74/100 (grade C)
     Automate Docker Hub tasks via Rube MCP (Composio): repositories, images, tags, and container registry management. Always search tools first for current schemas.
268. e2e-testing sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     End-to-end testing workflow with Playwright for browser automation, visual regression, cross-browser testing, and CI/CD integration.
269. fp-pipe-ref sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     Quick reference for pipe and flow. Use when user needs to chain functions, compose operations, or build data pipelines in fp-ts.
270. github-actions-advanced sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     Design, debug, and harden GitHub Actions CI/CD workflows, including reusable workflows, matrix builds, self-hosted runners, OIDC authentication, caching, environments, secrets, and release automation.
271. healthcare-eval-harness affaan-m/ECC · score 74/100 (grade C)
     Patient safety evaluation harness for healthcare application deployments. Automated test suites for CDSS accuracy, PHI exposure, clinical workflow integrity, and integration compliance. Blocks deployments on safety failures. Use when a healthcare deployment must be gated on patient-safety tests for CDSS accuracy, PHI exposure, and workflow integrity.
272. healthcare-eval-harness affaan-m/everything-claude-code · score 74/100 (grade C)
     Patient safety evaluation harness for healthcare application deployments. Automated test suites for CDSS accuracy, PHI exposure, clinical workflow integrity, and integration compliance. Blocks deployments on safety failures. Use when a healthcare deployment must be gated on patient-safety tests for CDSS accuracy, PHI exposure, and workflow integrity.
273. mantis-configure google/mantis · score 74/100 (grade C)
     Configures and validates Mantis pipeline environments, sandbox mechanisms, and AI models. Use to set up workflow.json, auto-detect host capabilities, switch between sandboxes (static-only, gvisor, microsandbox, gce), select AI models, and run fast 1-2s preflight tests. Don't use for scanning source code or running attack campaigns.
274. npm-trusted-publishing github/awesome-copilot · score 74/100 (grade C)
     Set up or fix npm trusted publishing (OIDC) in GitHub Actions and remove NPM_TOKEN. Use for ENEEDAUTH, E404 on PUT, E422 repository.url, or migrating off 2FA-bypass tokens before January 2027.
275. odoo-sales-crm-expert sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     Expert guide for Odoo Sales and CRM: pipeline stages, quotation templates, pricelists, sales teams, lead scoring, and forecasting.
276. pinecone-rag github/awesome-copilot · score 74/100 (grade C)
     Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for multi-tenant agents, embedding pipelines, or scaling a knowledge base beyond what local storage can handle. DO NOT use for local-only vector stores (Chroma, FAISS, pgvector) or pure keyword search with no semantic component.
277. quarkus-native-and-container github/awesome-copilot · score 74/100 (grade C)
     Build Quarkus native executables (Mandrel/GraalVM), fix native failures with reflection registration, test the native binary, and produce container images and Kubernetes/OpenShift manifests. Use for native, Docker, Jib or Kubernetes work in a Quarkus project.
278. revops coreyhaines31/marketingskills · score 74/100 (grade C)
     When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'outbound pipeline stages,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see emails. For pricing decisions, see pricing.
279. secret-scanning github/awesome-copilot · score 74/100 (grade C)
     Guide for configuring and managing GitHub secret scanning, push protection, custom patterns, and secret alert remediation. For pre-commit secret scanning in AI coding agents via the GitHub MCP Server, this skill references the Advanced Security plugin (`advanced-security@copilot-plugins`). Use this skill when enabling secret scanning, setting up push protection, defining custom patterns, triaging alerts, resolving blocked pushes, or when an agent needs to scan code for secrets before committing.
280. vpe-advisor alirezarezvani/claude-skills · score 74/100 (grade C)
     VP of Engineering advisory for startups: delivery throughput (DORA 4 metrics + bottleneck identification), engineering hiring funnel (sourcing → screen → onsite → offer conversion + time-to-fill + pipeline gap), engineering team structure (squad/tribe/chapter design + tech-lead manager-trigger thresholds), and production discipline (on-call, deployment cadence, postmortem culture). Use when sprint velocity is dropping, eng hiring is broken, team structure is unclear, or deciding when to add a tech-lead manager. NOT a CTO skill (which owns architecture) — VPE owns delivery operations and how the team ships.
281. zk-proof-verification-pipeline sickn33/antigravity-awesome-skills · score 74/100 (grade C)
     Zero-knowledge cryptographic verification pipeline register: proving system, circuit verification keys, public inputs, and gas costs.
282. zoho_bigin-automation ComposioHQ/awesome-claude-skills · score 74/100 (grade C)
     Automate Zoho Bigin tasks via Rube MCP (Composio): pipelines, contacts, companies, products, and small business CRM. Always search tools first for current schemas.
283. acceptance-orchestrator sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
284. autonomous-loops affaan-m/ECC · score 73/100 (grade C)
     Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems. Retained for compatibility only: when new autonomous loop guidance is needed, use continuous-agent-loop instead.
285. autonomous-loops affaan-m/everything-claude-code · score 73/100 (grade C)
     Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems. Retained for compatibility only: when new autonomous loop guidance is needed, use continuous-agent-loop instead.
286. autoplan garrytan/gstack · score 73/100 (grade C)
     Auto-review pipeline — reads the full CEO, design, eng, and DX review skills from disk and runs them sequentially with auto-decisions using 6 decision principles. (gstack)
287. aws-cloudwatch-investigation github/awesome-copilot · score 73/100 (grade C)
     Reusable investigation patterns for AWS CloudWatch: Logs Insights query templates, alarm-to-deployment correlation, blast-radius narrowing decision tree, and PromQL-style metric query patterns for structured incident triage.
288. channel-economics alirezarezvani/claude-skills · score 73/100 (grade C)
     Use when reviewing or rebalancing direct vs. partner-led channel economics — computing fully-loaded cost-to-serve per channel, channel ROI with cash / LTV / marginal lenses, and optimal channel mix subject to constraints. For Head of Commercial, RevOps, and VP Sales doing quarterly channel review when pipeline is mixed (e.g., 60% direct + 40% partner-led) and nobody actually knows which channel makes money after CAC, support load, partner discount, deal-velocity differences, retention differential, and overhead allocation are all loaded in. Outputs cost to serve, channel ROI verdicts (DOUBLE-DOWN / MAINTAIN / DEFUND / EXIT), a sensitivity-tested channel-mix recommendation, and the diminishing-returns inflection (e.g., 'which channel actually makes money — direct or partner?').
289. cloud-devops sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Cloud infrastructure and DevOps workflow covering AWS, Azure, GCP, Kubernetes, Terraform, CI/CD, monitoring, and cloud-native development.
290. container-audit briiirussell/cybersecurity-skills · score 73/100 (grade C)
     Audit container images, Dockerfiles, and Kubernetes manifests for misconfigurations, excessive privileges, exposed secrets, and runtime risks. Use when the user mentions 'container security,' 'Docker security,' 'Dockerfile audit,' 'Kubernetes security,' 'K8s security,' 'pod security,' 'container hardening,' 'kubectl audit,' 'image scanning,' 'distroless,' 'rootless containers,' 'pod security policy,' 'pod security standards,' 'PSS,' 'network policy,' 'OPA Gatekeeper,' 'Kyverno,' 'runtime security,' or needs to review container or orchestration security.
291. container-security-hardening sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Harden Docker/container images and runtime deployments with secure base images, non-root users, CVE scanning, SBOM/signing, seccomp/AppArmor, and Kubernetes pod security controls. Use for Dockerfile security reviews, container CVEs, image scanning, distroless images, or production hardening.
292. content-production alirezarezvani/claude-skills · score 73/100 (grade C)
     Full content production pipeline — takes a topic from blank page to published-ready piece. Use when you need to execute content: write a blog post, article, or guide end-to-end. Triggers: 'write a post about', 'draft an article', 'create content for', 'help me write', 'I need a blog post'. NOT for content strategy or calendar planning (use content-strategy). NOT for repurposing existing content (use content-repurposing). NOT for social captions only.
293. data-quality-frameworks sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
294. deployment-engineer sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Expert deployment engineer specializing in modern CI/CD pipelines, GitOps workflows, and advanced deployment automation.
295. docker-expert sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     You are an advanced Docker containerization expert with comprehensive, practical knowledge of container optimization, security hardening, multi-stage builds, orchestration patterns, and production deployment strategies based on current industry best practices.
296. e2e-testing affaan-m/ECC · score 73/100 (grade C)
     Playwright E2E testing patterns, Page Object Model, configuration, CI/CD integration, artifact management, and flaky test strategies. Use when writing Playwright tests, structuring page objects, or fixing flaky E2E runs in CI.
297. e2e-testing affaan-m/everything-claude-code · score 73/100 (grade C)
     Playwright E2E testing patterns, Page Object Model, configuration, CI/CD integration, artifact management, and flaky test strategies. Use when writing Playwright tests, structuring page objects, or fixing flaky E2E runs in CI.
298. firebase-basics google/skills · score 73/100 (grade C)
     Provides foundational Firebase CLI setup, CLI installation, version checks (`firebase-tools@latest --version`), CLI login (including --no-localhost), project creation, project selection (`firebase use`), and app config file downloads (`google-services.json`, `GoogleService-Info.plist`). Use ONLY for CLI login, project creation/switching, or downloading app config files. Don't use for Firebase Hosting deploy, Firestore, Auth, App Hosting, Data Connect, Crashlytics, or Remote Config.
299. gke-app-onboarding google/skills · score 73/100 (grade C)
     Manages GKE application onboarding, covering containerization, deployment manifests, and migration. Use when onboarding or deploying an application to GKE for the first time, or containerizing an app for GKE. Don't use for general GKE cluster administration or upgrades (use gke-basics or gke-upgrades instead).
300. gke-inference google/skills · score 73/100 (grade C)
     Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for migrating existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-migration), GKE RAG with Cloud SQL/AlloyDB (use google-cloud-solution-rag-enterprise-search-gke-sqldb), or batch/HPC (use gke-batch-hpc).
301. google-cloud-recipe-foundation-builder google/skills · score 73/100 (grade C)
     Deploys a baseline landing zone foundation for a Google Cloud Organization, establishing security guardrails using Organization Policies, resource hierarchy folders and projects, billing association, and centralized logging and monitoring. Deploys Google Cloud's recommended security controls and architecture. Use when setting up a new Google Cloud Organization or establishing a secure, enterprise-grade landing zone foundation. Don't use for individual project onboarding (use google-cloud-recipe-onboarding or product-specific skills instead).
302. google-cloud-waf-operational-excellence google/skills · score 73/100 (grade C)
     Generates operational guidance for CI/CD deployment pipelines, observability, monitoring, logging, and incident management on Google Cloud based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF). Use when the user asks about operations-related topics like capacity management, change management, incident and problem management, configuration management, deployment automation using infrastructure as code (IaC), or operational health checks for Google Cloud workloads.
303. google-cloud-waf-reliability google/skills · score 73/100 (grade C)
     Evaluates, designs, and improves the reliability, resilience, availability, fault tolerance, and disaster recovery (DR) of Google Cloud workloads based on the design principles and recommendations in the Reliability pillar of the Google Cloud Well-Architected Framework. Use when the user asks about reliability-related topics like workload uptime, designing for high availability (HA) or DR, setting and meeting RTO/RPO targets, SRE best practices, backup strategies, multi-region failover, or fault-tolerant architecture on Google Cloud. Don't use for other Well-Architected Framework pillars such as Cost Optimization, Operational Excellence, or Security, or for hands-on resource deployment and live incident troubleshooting.
304. idea-os sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Five-phase pipeline (triage → clarify → research → PRD → plan) that turns a raw idea into four linked files: clarifying questions, deep research, a PRD with non-goals and metrics, and a phased execution plan with mermaid user journey and kill criteria.
305. incident-commander alirezarezvani/claude-skills · score 73/100 (grade C)
     Comprehensive incident response framework from detection through resolution and post-incident review. Battle-tested SRE/DevOps practices: severity classification, timeline reconstruction, structured post-incident analysis. Use when declaring an incident, coordinating multi-team response during an outage, leading a post-mortem, or setting up on-call practices for a new service.
306. poka-yoke github/awesome-copilot · score 73/100 (grade C)
     Mistake-proof code so misuse cannot be expressed, rather than warning against it. Use when designing an interface, schema, or state machine and the user wants it hard to get wrong ("make invalid states unrepresentable", "so callers cannot screw it up", "type-safe API", "pit of success"); when auditing existing code for footguns ("what could bite us here", "what is easy to misuse", "poka-yoke this repo", "review this diff for ways to get it wrong"); or when a bug has recurred and the fix must close the class rather than the case ("make sure this never happens again", "this is the third time"). Especially for money, auth, permissions, deletion, migrations, and pipelines where failure is silent. Classifies every finding by what happens when the mistake occurs and how the device notices, which is what keeps it from collapsing into generic code review.
307. recruitment-pipeline sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Recruitment pipeline: candidate, position, stage, source, applied and interview dates, interview score, notice period and offer. Use for hiring tracking.
308. sandbox-npm-install github/awesome-copilot · score 73/100 (grade C)
     Install npm packages in a Docker sandbox environment. Use this skill whenever you need to install, reinstall, or update node_modules inside a container where the workspace is mounted via virtiofs. Native binaries (esbuild, lightningcss, rollup) crash on virtiofs, so packages must be installed on the local ext4 filesystem and symlinked back.
309. secops-detection-engineering google/skills · score 73/100 (grade C)
     Author, validate, test, and deploy YARA-L 2.0 detection rules and evaluate end-to-end detection coverage gaps in Google SecOps. Use when writing new detection rules, tuning existing rules, validating syntax, testing logic against historical telemetry, or evaluating detection coverage against threat intelligence blogs, CVE disclosures, and Threat Detection Opportunities (TDOs) using synthetic UDM events and long-running coverage analysis. Don't use for alert triage (use secops-triage), deep forensic event reconstruction on infected hosts (use secops-investigate), or case management operations (use secops-cases).
310. secrets-management sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Secure secrets management practices for CI/CD pipelines using Vault, AWS Secrets Manager, and other tools.
311. smoke-test bytedance/deer-flow · score 73/100 (grade C)
     End-to-end smoke test skill for DeerFlow. Guides through: 1) Pulling latest code, 2) Docker OR Local installation and deployment (user preference, default to Local if Docker network issues), 3) Service availability verification, 4) Health check, 5) Final test report. Use when the user says "run smoke test", "smoke test deployment", "verify installation", "test service availability", "end-to-end test", or similar.
312. snowflake-development alirezarezvani/claude-skills · score 73/100 (grade C)
     Use when writing Snowflake SQL, building data pipelines with Dynamic Tables or Streams/Tasks, using Cortex AI functions, creating Cortex Agents, writing Snowpark Python, configuring dbt for Snowflake, or troubleshooting Snowflake errors.
313. springboot-verification affaan-m/ECC · score 73/100 (grade C)
     Run the full Spring Boot verification loop — Maven or Gradle build, SpotBugs, PMD, and Checkstyle static analysis, unit and Testcontainers integration tests with JaCoCo coverage, OWASP dependency and secret scans, and diff review — producing a pass/fail readiness report. Use when preparing a Spring Boot pull request, validating coverage thresholds, or running pre-deploy verification.
314. springboot-verification affaan-m/everything-claude-code · score 73/100 (grade C)
     Run the full Spring Boot verification loop — Maven or Gradle build, SpotBugs, PMD, and Checkstyle static analysis, unit and Testcontainers integration tests with JaCoCo coverage, OWASP dependency and secret scans, and diff review — producing a pass/fail readiness report. Use when preparing a Spring Boot pull request, validating coverage thresholds, or running pre-deploy verification.
315. supply-chain-security sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Software supply-chain security assessment: SBOM generation, SCA scanning, CI/CD pipeline review, container image audit, build integrity, dependency provenance, and vulnerability reachability verification.
316. terraform-specialist sickn33/antigravity-awesome-skills · score 73/100 (grade C)
     Expert Terraform/OpenTofu specialist mastering advanced IaC automation, state management, and enterprise infrastructure patterns.
317. winui-app openai/skills · score 73/100 (grade C)
     Bootstrap, develop, and design modern WinUI 3 desktop applications with C# and the Windows App SDK using official Microsoft guidance, WinUI Gallery patterns, Windows App SDK samples, and CommunityToolkit components. Use when creating a brand new app, preparing a machine for WinUI, reviewing, refactoring, planning, troubleshooting, environment-checking, or setting up WinUI 3 XAML, controls, navigation, windowing, theming, accessibility, responsiveness, performance, deployment, or related Windows app design and development work.
318. academic-pipeline Imbad0202/academic-research-skills · score 72/100 (grade C)
     Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로, flujo de trabajo académico, investigación a artículo, flujo completo de artículo, pipeline de investigación completa, publicación de investigación, flujo de trabajo completo del artículo.
319. agent-platform-model-registry google/skills · score 72/100 (grade C)
     Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
320. aspire github/awesome-copilot · score 72/100 (grade C)
     Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.
321. aspnet-core openai/skills · score 72/100 (grade C)
     Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.
322. bio-batch-downloads GPTomics/bioSkills · score 72/100 (grade C)
     Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.
323. bio-crispr-screens-base-editing-analysis GPTomics/bioSkills · score 72/100 (grade C)
     Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.
324. bio-workflows-methylation-pipeline GPTomics/bioSkills · score 72/100 (grade C)
     Orchestrates the end-to-end bisulfite/EM-seq methylation pipeline from FASTQ to differentially methylated regions, chaining Trim Galore/fastp QC, Bismark alignment + deduplication, methylation calling, methylKit coverage-filtering/normalization, and selection-aware DMR detection (dmrseq/DSS). Use when gating the run on bisulfite conversion (lambda + pUC19 controls) BEFORE any beta value, committing the genome build + library directionality once, keeping mate-overlap deduplicated (--no_overlap), M-bias-trimming from the plot, filtering coverage before testing, choosing a count model (beta-binomial/DSS) over a bare-beta t-test, or using a region-selection-aware FDR (dmrseq/DSS) rather than raw methylKit tiles. Hands mechanism to the methylation-analysis component skills; not a re-teach of any single step.
325. browser-qa affaan-m/ECC · score 72/100 (grade C)
     Run automated post-deploy UI verification with a browser automation MCP (claude-in-chrome, Playwright, or Puppeteer): console-error and Core Web Vitals smoke checks, form and auth-flow interaction tests, screenshot visual regression across three breakpoints, and axe-core accessibility audits ending in a SHIP / DO-NOT-SHIP verdict. Use when testing a deployed feature on staging or preview, before shipping frontend changes, reviewing a frontend PR, or checking responsive layout and accessibility.
326. browser-qa affaan-m/everything-claude-code · score 72/100 (grade C)
     Run automated post-deploy UI verification with a browser automation MCP (claude-in-chrome, Playwright, or Puppeteer): console-error and Core Web Vitals smoke checks, form and auth-flow interaction tests, screenshot visual regression across three breakpoints, and axe-core accessibility audits ending in a SHIP / DO-NOT-SHIP verdict. Use when testing a deployed feature on staging or preview, before shipping frontend changes, reviewing a frontend PR, or checking responsive layout and accessibility.
327. c-level-agents alirezarezvani/claude-skills · score 72/100 (grade C)
     Founder-mode executive team. 13 cs-* C-suite agents (CFO, CMO, CRO, CPO, COO, CHRO, CISO, GC, CDO, CAIO, CCO, VPE, Chief of Staff) and 21 /c-level-agents:* slash commands for forcing-question office hours, multi-role boardroom deliberation, strategic sprint pipeline, and meta routing. Use when the founder needs a virtual executive team, when invoking /c-level-agents:* commands, or when orchestrating multi-role decisions.
328. cloud-build-basics google/skills · score 72/100 (grade C)
     Teaches the fundamentals of Cloud Build on Google Cloud. Covers core concepts, API enablement, console navigation to the Build History page, and the end-to-end workflow for creating and manually running a basic build trigger. Do not use for managing private pools or complex pipeline architectures.
329. container-hardening sickn33/antigravity-awesome-skills · score 72/100 (grade C)
     Secure Docker images and container runtime configurations.
330. containerize-aspnet-framework github/awesome-copilot · score 72/100 (grade C)
     Containerize an ASP.NET .NET Framework project by creating Dockerfile and .dockerfile files customized for the project.
331. data-engineer sickn33/antigravity-awesome-skills · score 72/100 (grade C)
     Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
332. deployment-patterns affaan-m/ECC · score 72/100 (grade C)
     Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications. Use when setting up CI/CD, containerizing an app, or checking production readiness before a release.
333. deployment-patterns affaan-m/everything-claude-code · score 72/100 (grade C)
     Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications. Use when setting up CI/CD, containerizing an app, or checking production readiness before a release.
334. devops-rollout-plan github/awesome-copilot · score 72/100 (grade C)
     Generate comprehensive rollout plans with preflight checks, step-by-step deployment, verification signals, rollback procedures, and communication plans for infrastructure and application changes
335. foundry-agentic-loop github/awesome-copilot · score 72/100 (grade C)
     Build agentic solutions on Microsoft Foundry, GitHub and Azure: hosted agents, toolbox tools and skills, Foundry IQ grounding and a React/FastAPI app. Three modes: rapid prototyping in an existing Foundry project; spec-driven development (SDD), where the foundry-agentic-loop-workflow runs Specify, Implement, Verify and Deploy to create the Foundry project and deploy with azd, or one phase runs alone; and Improve, which refines this skill and the workflow from past runs. Use for Agentic Loop requests and canvas actions, or end-to-end agent solutions from architecture to Azure deployment. Not for isolated Azure administration or unrelated UI edits.
336. github-actions-efficiency github/awesome-copilot · score 72/100 (grade C)
     Audit GitHub Actions workflow efficiency and recommend fixes to reduce CI minutes and costs.
337. github-ops affaan-m/ECC · score 72/100 (grade C)
     GitHub repository operations, automation, and management. Issue triage, PR management, CI/CD operations, release management, and security monitoring using the gh CLI. Use when the user wants to manage GitHub issues, PRs, CI status, releases, contributors, stale items, or any GitHub operational task beyond simple git commands.
338. github-ops affaan-m/everything-claude-code · score 72/100 (grade C)
     GitHub repository operations, automation, and management. Issue triage, PR management, CI/CD operations, release management, and security monitoring using the gh CLI. Use when the user wants to manage GitHub issues, PRs, CI status, releases, contributors, stale items, or any GitHub operational task beyond simple git commands.
339. gke-upgrades google/skills · score 72/100 (grade C)
     Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters. Produces upgrade plans, pre/post-upgrade checklists, maintenance runbooks with gcloud commands, release channel strategy, and troubleshooting guides. Handles node pool upgrade strategies (surge, blue-green), version compatibility, PDB management, and workload-specific concerns (stateful, GPU, operators). Use this skill whenever the user mentions GKE upgrades, Kubernetes version bumps, node pool maintenance, GKE patching, cluster version management, release channel selection, maintenance windows, surge upgrades, stuck upgrades, or any GKE lifecycle management task — even casual mentions like "we need to upgrade our clusters" or "plan our next GKE maintenance" or "our upgrade is stuck." Don't use for GKE cluster creation, application onboarding, general networking/routing setup, or security policy configurations (use gke-basics or relevant GKE skills instead).
340. gke-workload-troubleshooting google/skills · score 72/100 (grade C)
     Diagnoses GKE workload failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending, etc.) via logs and events. Use when pods fail to start or crash repeatedly. Don't use for GKE cluster infrastructure provisioning, node pool creation, or non-Kubernetes Google Cloud services.
341. google-cloud-recipe-onboarding google/skills · score 72/100 (grade C)
     Guides a developer's first steps on Google Cloud, covering account creation, billing setup, project management, and deploying a first resource. Use when a new developer wants to initialize their first Google Cloud project, configure billing, and verify deployment. Don't use for enterprise organization setup (use Google Cloud Setup guided flow for that instead). Don't use for complex multi-project architectures.
342. gtm-developer-ecosystem github/awesome-copilot · score 72/100 (grade C)
     Build and scale developer-led adoption through ecosystem programs. Use when deciding open vs curated ecosystems, building developer programs, scaling platform adoption, or designing student program pipelines.
343. land-and-deploy garrytan/gstack · score 72/100 (grade C)
     Land and deploy workflow. (gstack)
344. marketing-demand-acquisition alirezarezvani/claude-skills · score 72/100 (grade C)
     Creates demand generation campaigns, optimizes paid ad spend across LinkedIn, Google, and Meta, develops SEO strategies, and structures partnership programs. Use when planning demand gen strategy, growth marketing, advertising campaigns, PPC optimization, lead generation, pipeline generation, or marketing budgets. Covers multi-channel acquisition (Google Ads, LinkedIn Ads, Meta Ads), CAC analysis, MQL/SQL workflows, attribution modeling, technical SEO, and co-marketing partnerships. Default calibration profile is a Series A+ B2B SaaS scaling internationally (EU/US/Canada, hybrid PLG/Sales-Led) — adapt benchmarks for other stages and motions rather than skipping the skill.
345. pipeline-crm-automation ComposioHQ/awesome-claude-skills · score 72/100 (grade C)
     Automate Pipeline CRM tasks via Rube MCP (Composio). Always search tools first for current schemas.
346. playwright-pro alirezarezvani/claude-skills · score 72/100 (grade C)
     Production-grade Playwright testing toolkit. Use when the user mentions Playwright tests, end-to-end testing, browser automation, fixing flaky tests, test migration, CI/CD testing, or test suites. Generate tests, fix flaky failures, migrate from Cypress/Selenium, sync with TestRail, run on BrowserStack. 55 templates, 3 agents, smart reporting.
347. senior-secops alirezarezvani/claude-skills · score 72/100 (grade C)
     Senior SecOps engineer skill for application security, vulnerability management, compliance verification, and secure development practices. Runs SAST/DAST scans, generates CVE remediation plans, checks dependency vulnerabilities, creates security policies, enforces secure coding patterns, and automates compliance checks against SOC2, PCI-DSS, HIPAA, and GDPR. Use when conducting a security review or audit, responding to a CVE or security incident, hardening infrastructure, implementing authentication or secrets management, running penetration test prep, checking OWASP Top 10 exposure, or enforcing security controls in CI/CD pipelines.
348. terraform-azurerm-set-diff-analyzer github/awesome-copilot · score 72/100 (grade C)
     Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes. Use when reviewing terraform plan output for Azure resources like Application Gateway, Load Balancer, Firewall, Front Door, NSG, and other resources with Set-type attributes that cause spurious diffs due to internal ordering changes.
349. verify-agent-action github/awesome-copilot · score 72/100 (grade C)
     Review a proposed AI-agent action or human-approval packet before execution. Use when an agent wants to run a consequential tool, command, deployment, message, purchase, credential operation, or data mutation; when checking whether approval still matches the exact action; or when auditing action evidence for forged results, parameter swaps, replay, correlated reviewers, missing evidence, expiry, or stale monitoring. Produce an evidence-based review only—never execute or authorize the action.
350. agent-workflow-designer alirezarezvani/claude-skills · score 71/100 (grade C)
     Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.
351. android-cli sickn33/antigravity-awesome-skills · score 71/100 (grade C)
     Orchestrates Android development tasks including project creation, deployment, SDK management, and environment diagnostics using the `android` command-line tool.
352. antithesis-setup-k8s antithesishq/antithesis-skills · score 71/100 (grade C)
     Scaffold the Antithesis harness for customers running Kubernetes: Convert helm charts, kustomize templates, or raw Kubernetes manifests into a minimized set of manifests for Antithesis. Verify the Kubernetes manifests are in the correct shape with snouty validate and a local cluster deploy. Hands it off to antithesis-launch if the user wants to submit a run. At the end, the user is ready to run antithesis-workload to build tests. If the desired setup is docker-compose, defer to the antithesis-setup skill.
353. application-integration-authoring google/skills · score 71/100 (grade C)
     Builds, versions, publishes, tests, and executes Google Cloud Application Integration workflows through the REST API and Python discovery client, and promotes versions across environments. Use when listing integrations, downloading or uploading integration versions as JSON, configuring API triggers or config variables, publishing drafts, invoking integrations, or automating CI/CD promotion. Do not use for initial region provisioning and IAM setup (use application-integration-basics) or diagnosing failed runs and logs (use application-integration-troubleshooting).
354. assuredworkloads-environment-assessor google/skills · score 71/100 (grade C)
     Discover Google Cloud resource telemetry and effective Org Policies, evaluate brownfield workloads against sovereign data boundary and compliance standards (EU Data Boundary with Support, FedRAMP Moderate/High, DoD IL4/IL5, ITAR, CJIS, US/EU Regional Data Boundaries), and recommend the optimal Assured Workloads Framework. Use when asked to: "assess my environment for compliance", "which Assured Workloads framework should I use?", "check if my folder is ready for EU sovereignty", "evaluate compliance posture for folders/123 or projects/xyz", or "discover compliance gaps before deployment". Do NOT use for live mutating policy deployments, generating deep audit reports via Cloud Audit Manager, generating remediation scripts, or authoring custom Assured Workloads frameworks.
355. azure-architecture-autopilot github/awesome-copilot · score 71/100 (grade C)
     Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
356. azure-devops-cli github/awesome-copilot · score 71/100 (grade C)
     Manage Azure DevOps resources via CLI including projects, repos, pipelines, builds, pull requests, work items, artifacts, and service endpoints. Use when working with Azure DevOps, az commands, devops automation, CI/CD, or when user mentions Azure DevOps CLI.
357. bio-isoform-switching GPTomics/bioSkills · score 71/100 (grade C)
     Analyzes differential transcript usage (DTU) and isoform switches with functional consequence prediction (NMD via 50nt rule, ORF disruption, protein domain loss/gain, signal peptide changes, IDR alterations, coding-potential shifts). Tools include IsoformSwitchAnalyzeR v2 (auto-selects satuRn for >5 reps else DEXSeq), the manual DRIMSeq -> DEXSeq/satuRn -> stageR DTU pipeline, and fishpond/swish for inferential-uncertainty-aware DTE. Distinguishes DTU from DGE and DTE; integrates external annotators (CPC2, Pfam, SignalP, IUPred2A or DeepTMHMM). Use when investigating how splicing differences alter protein function or trigger NMD-mediated degradation.
358. bio-workflows-cnv-pipeline GPTomics/bioSkills · score 71/100 (grade C)
     Orchestrates the copy-number pipeline from BAM to segmented, integer-called, annotated CNVs, forking on germline-vs-somatic - CNVkit (somatic exome/panel: coverage -> assay-matched reference/PoN -> fix -> segment -> purity/ploidy-aware call), GATK gCNV (germline rare-CNV cohort), and allele-specific callers (ASCAT/FACETS/PURPLE) for purity/ploidy. Use when committing the build + target/access BED + PoN once (assay-matched), building the reference from normals BEFORE segmenting, fitting purity/ploidy BEFORE integer calls in tumors, centering on the true (non-diploid) mode before GISTIC2 recurrence, or routing cfDNA to ichorCNA. Hands mechanism to the copy-number component skills; not a re-teach of any single step.
359. bio-workflows-liquid-biopsy-pipeline GPTomics/bioSkills · score 71/100 (grade C)
     Orchestrates the cell-free DNA / liquid-biopsy pipeline from plasma sequencing to tumor monitoring, forking tumor-naive (screening) vs tumor-informed (MRD), and chaining pre-analytic QC, UMI/duplex error-suppression (fgbio), fragment QC, ichorCNA tumor fraction (sWGS) or VarDict low-VAF calling (panel), CHIP subtraction against matched WBC, optional fragmentomics/methylation, and longitudinal tracking. Use when treating pre-analytics as the irreversible sensitivity ceiling (tube/time-to-plasma/hemolysis), running error-suppression BEFORE calling (single-strand consensus does not remove deamination; only duplex does), reporting a VAF only with input genome-equivalents (TF ~ 2x VAF only for clonal-het-diploid), subtracting CHIP before reporting somatic, or keeping tube/panel/pipeline identical across a longitudinal MRD series. Hands mechanism to the liquid-biopsy component skills; not a re-teach of any single step.
360. bio-workflows-rnaseq-to-de GPTomics/bioSkills · score 71/100 (grade C)
     Orchestrates the end-to-end bulk RNA-seq differential-expression pipeline from FASTQ to an annotated DE gene table, chaining fastp QC/trim, Salmon (decoy-aware) or STAR+featureCounts quantification, tximport gene-level collapse, DESeq2/edgeR/limma-voom testing, apeglm shrinkage, and VST-based visualization. Use when committing the reference release and gene-ID namespace once for the whole run, sequencing steps in the defensible order (tximport before DE, raw counts into the model, VST only for viz/clustering), choosing alignment-free vs align-then-count and the DE engine, setting strandedness correctly, keeping batch in the design instead of correcting-then-testing, or handing the signed ranking statistic to downstream enrichment. Hands mechanism to the component skills; not a re-teach of any single step.
361. bio-workflows-splicing-pipeline GPTomics/bioSkills · score 71/100 (grade C)
     Orchestrates the end-to-end bulk short-read alternative-splicing pipeline from FASTQ to differential splicing, chaining fastp QC, cohort-consistent STAR 2-pass alignment (one shared junction DB), junction QC, event-level differential splicing (rMATS-turbo + leafcutter, optional MAJIQ V3), parallel isoform-level DTU (Salmon -> tximport dtuScaledTPM -> DRIMSeq/DEXSeq -> stageR), and sashimi visualization. Use when committing the annotation GTF and a shared 2-pass junction database for the whole cohort, keeping the analysis at splice-aware resolution (never collapsing to gene), choosing event-level vs isoform-level DTU and reconciling them, applying the stageR two-stage gene->transcript FDR, or off-ramping to splice-variant / outlier / long-read / single-cell splicing. Hands mechanism to the alternative-splicing component skills; not a re-teach of any single step.
362. change-impact affaan-m/ECC · score 71/100 (grade C)
     Perform evidence-backed impact analysis before changing code, data, interfaces, or governance documents, then compare the planned impact with the actual diff after implementation. Covers call paths, data and schema, API contracts, tests, documentation, ADRs, deployment, migration, and rollback. Use for cross-module or high-risk changes, database migrations, authentication, public interfaces, blast-radius questions, pre-change checks, and post-implementation alignment.
363. change-impact affaan-m/everything-claude-code · score 71/100 (grade C)
     Perform evidence-backed impact analysis before changing code, data, interfaces, or governance documents, then compare the planned impact with the actual diff after implementation. Covers call paths, data and schema, API contracts, tests, documentation, ADRs, deployment, migration, and rollback. Use for cross-module or high-risk changes, database migrations, authentication, public interfaces, blast-radius questions, pre-change checks, and post-implementation alignment.
364. cicd-automation-workflow-automate sickn33/antigravity-awesome-skills · score 71/100 (grade C)
     You are a workflow automation expert specializing in creating efficient CI/CD pipelines, GitHub Actions workflows, and automated development processes. Design and implement automation that reduces manual work, improves consistency, and accelerates delivery while maintaining quality and security.
365. cloud-k8s sickn33/antigravity-awesome-skills · score 71/100 (grade C)
     Authorized cloud, container, and Kubernetes security assessment: metadata SSRF, IAM misconfiguration, container escape paths, and cluster RBAC review.
366. cs-fullstack-engineer alirezarezvani/claude-skills · score 71/100 (grade C)
     Fullstack-engineering orchestrator. Walks the Matt Pocock 7-question forcing-question grill, runs the deterministic profile picker, then forks into the POWERFUL-tier specialists (api-design-reviewer, ci-cd-pipeline-builder, database-designer, performance-profiler, slo-architect — listed alphabetically; workflow order is dependency-driven) rather than reimplementing their scope. Forks own context so heavy ingestion does not pollute parent thread. Invoke via /cs-fullstack-review or Agent({subagent_type:"cs-fullstack-engineer",...}).
367. docker-patterns affaan-m/ECC · score 71/100 (grade C)
     Docker and Docker Compose patterns for local development, hardened CLI installer harnesses, container security, networking, volumes, and multi-service orchestration. Use when creating or reviewing Dockerfiles and Compose services, testing installers across Linux distributions, or planning accurate native macOS and Windows validation.
368. docker-patterns affaan-m/everything-claude-code · score 71/100 (grade C)
     Docker and Docker Compose patterns for local development, hardened CLI installer harnesses, container security, networking, volumes, and multi-service orchestration. Use when creating or reviewing Dockerfiles and Compose services, testing installers across Linux distributions, or planning accurate native macOS and Windows validation.
369. gke-workload-security google/skills · score 71/100 (grade C)
     Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running security audits (`audit_cluster.sh`), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (`gVisor`), enforcing Pod Security Standards (`restricted` labeling) and pod securityContext, and mounting Secret Manager secrets via CSI (`SecretProviderClass`). Use when auditing workload security posture, isolating namespaces, applying pod security standards, or configuring network policies and secret volume mounts. Don't use for Workload Identity (use gke-workload-identity), cluster-wide control plane security, RBAC hardening, Binary Authorization, Shielded Nodes, or enabling platform-level GKE add-ons (use gke-platform-security instead).
370. kubernetes-architect sickn33/antigravity-awesome-skills · score 71/100 (grade C)
     Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration.
371. kubernetes-patterns affaan-m/ECC · score 71/100 (grade C)
     Kubernetes workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap/Secret handling, and kubectl debugging for production-grade deployments. Use when writing or reviewing Kubernetes manifests, or debugging probes, RBAC, autoscaling, or resource limits.
372. kubernetes-patterns affaan-m/everything-claude-code · score 71/100 (grade C)
     Kubernetes workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap/Secret handling, and kubectl debugging for production-grade deployments. Use when writing or reviewing Kubernetes manifests, or debugging probes, RBAC, autoscaling, or resource limits.
373. makepad-deployment sickn33/antigravity-awesome-skills · score 71/100 (grade C)
     CRITICAL: Use for Makepad packaging and deployment. Triggers on: deploy, package, APK, IPA, 打包, 部署, cargo-packager, cargo-makepad, WASM, Android, iOS, distribution, installer, .deb, .dmg, .nsis, GitHub Actions, CI, action, marketplace
374. python-azure-iot-edge-modules github/awesome-copilot · score 71/100 (grade C)
     Build and operate Python Azure IoT Edge modules with robust messaging, deployment manifests, observability, and production readiness checks.
375. senior-data-engineer alirezarezvani/claude-skills · score 71/100 (grade C)
     Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
376. setup-pre-commit mattpocock/skills · score 71/100 (grade C)
     Set up Husky pre-commit hooks with lint-staged (Prettier), type checking, and tests in the current repo. Use when user wants to add pre-commit hooks, set up Husky, configure lint-staged, or add commit-time formatting/typechecking/testing.
377. video-editing affaan-m/ECC · score 71/100 (grade C)
     AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.
378. video-editing affaan-m/everything-claude-code · score 71/100 (grade C)
     AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.
379. aegisops-ai sickn33/antigravity-awesome-skills · score 70/100 (grade C)
     Autonomous DevSecOps & FinOps Guardrails. Guide to reviewing Linux kernel patches, Terraform cost changes and Kubernetes policy with a separately reviewed upstream tool and configured model.
380. agent-decision-receipts alirezarezvani/claude-skills · score 70/100 (grade C)
     Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
381. agent-platform-eval-flywheel google/skills · score 70/100 (grade C)
     Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.
382. agent-security-hardening affaan-m/ECC · score 70/100 (grade C)
     Security hardening guidance for AI agent frameworks that process untrusted content, invoke tools, write workspace files, manage runtime identifiers, or handle credentials. Use when building or reviewing an agent runtime, autonomous worker, tool gateway, memory service, or multi-tenant agent deployment. Do not use for general web application security or offensive testing.
383. agent-security-hardening affaan-m/everything-claude-code · score 70/100 (grade C)
     Security hardening guidance for AI agent frameworks that process untrusted content, invoke tools, write workspace files, manage runtime identifiers, or handle credentials. Use when building or reviewing an agent runtime, autonomous worker, tool gateway, memory service, or multi-tenant agent deployment. Do not use for general web application security or offensive testing.
384. ai-act-readiness alirezarezvani/claude-skills · score 70/100 (grade C)
     /compliance-os:ai-act-readiness [system] — EU AI Act 6-question forcing interrogation. Use during AI-system intake, before EU deployment, or during annual compliance refresh as Article 113 obligations phase in (2025-02-02 / 2025-08-02 / 2026-08-02 / 2027-08-02).
385. assuredworkloads-frameworks-navigator google/skills · score 70/100 (grade C)
     Acts as a Data Boundary Compliance Analyst that maps user compliance queries against Data Boundary Frameworks and cloud controls, validates matched resources, and recommends appropriate Frameworks or cloud controls. Use when mapping direct natural language workload requirements to Assured Workloads / Data Boundary Frameworks and cloud controls, browsing built-in Data Boundary frameworks, or identifying major requirement gaps. Don't use for non-Data-Boundary Compliance Manager posture assessments or direct Terraform deployment execution without framework selection.
386. bigquery-pipeline-audit github/awesome-copilot · score 70/100 (grade C)
     Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.
387. bio-reporting-rmarkdown-reports GPTomics/bioSkills · score 70/100 (grade C)
     Creates reproducible R Markdown analysis reports (HTML, PDF, Word) with knitr, covering the render pipeline, the interactive-vs-knit session trap, cache invalidation, bookdown cross-references, parameterization, and environment pinning. Use when generating an R-based analysis report, debugging a report that knits differently than it runs interactively, or fixing caching or cross-references.
388. bio-retrosynthesis GPTomics/bioSkills · score 70/100 (grade C)
     Performs retrosynthetic planning using AiZynthFinder (template-based MCTS), maintained or version-pinned template-free models, ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, configurable MCTS rewards, building-block availability, and forward-prediction checks. Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.
389. bio-workflows-clinical-trial-pipeline GPTomics/bioSkills · score 70/100 (grade C)
     End-to-end clinical trial analysis workflow from CDISC SDTM/ADaM loading through ICH E9(R1) estimand-driven primary analysis to CONSORT 2025 regulatory-compliant reporting. Covers data preparation, FDA 2023 marginal vs conditional logistic regression, categorical tests with Boschloo, modern HTE/subgroup methods, missing-data sensitivity (MMRM, reference-based MI, Permutt tipping point), graphical multiplicity (Bretz-Maurer), survival analysis (Cox/RMST/competing risks) when applicable, and Table 1. Use when performing a complete analysis of clinical trial data.
390. bio-workflows-clip-pipeline GPTomics/bioSkills · score 70/100 (grade C)
     End-to-end CLIP-seq pipeline from FASTQ to ENCODE-compliant binding sites, single-nucleotide crosslink maps, annotation, motifs, and (optionally) differential binding. Use when running the full Yeo lab eCLIP / iCLIP / iCLIP2 / iCLIP3 / irCLIP / PAR-CLIP analysis with SMInput control, protocol-specific UMI extraction, ENCODE STAR parameters, CLIPper or Skipper peak calling with stringent log2 FC and -log10 p thresholds, IDR rescue and self-consistency QC, and downstream motif registration with mCross or PEKA.
391. bio-workflows-fastq-to-variants GPTomics/bioSkills · score 70/100 (grade C)
     Orchestrates the end-to-end germline short-variant pipeline from FASTQ to a filtered, normalized, benchmarked VCF, chaining QC/trim, BWA-MEM2 alignment, duplicate marking, optional BQSR, calling (bcftools/GATK HaplotypeCaller/DeepVariant/DRAGEN), normalization, site+genotype filtering, annotation, and hap.py/vcfeval benchmarking. Use when deciding the pipeline-wide reference-genome commitment (GRCh38 analysis set vs T2T, ALT/decoy handling), sequencing the steps in the defensible order (normalize BEFORE annotate, filter site- then genotype-level), choosing the calling engine and single-sample vs cohort joint-calling, picking a filtering strategy by cohort size, or benchmarking stratified within GIAB confident regions. Hands off mechanism to the variant-calling and read-alignment component skills; not a re-teach of any single step.
392. bio-workflows-gwas-pipeline GPTomics/bioSkills · score 70/100 (grade C)
     Orchestrates the GWAS pipeline from genotypes to association results, chaining PLINK2 QC (variant-then-sample missingness, controls-only HWE, KING relatedness), panel harmonization + joint phasing/imputation to dosages, long-range-LD-excluded PCA, and an engine chosen by sample structure (PLINK2-GLM / regenie / SAIGE / BOLT-LMM), with LDSC-intercept diagnostics. Use when committing the genome build + ancestry-matched imputation panel once (ancestry match > panel size), running the strand/allele harmonization gate (drop intermediate-frequency palindromes), imputing cases+controls TOGETHER on dosages, excluding long-range-LD regions before PCA, choosing an LMM when relatedness/structure is present (PCs cannot remove a covariance), or separating polygenicity from confounding via the LDSC intercept. Hands mechanism to the population-genetics and phasing-imputation component skills; not a re-teach of any single step.
393. bio-workflows-hic-pipeline GPTomics/bioSkills · score 70/100 (grade C)
     End-to-end Hi-C analysis workflow from FASTQ to compartments, TADs, and loops, with the decision of WHICH features the sequencing depth can support. Covers pairtools read-pair processing and library QC, cooler matrices, ICE balancing and distance-decay expected, A/B compartments, TAD boundaries, loop calling, and the routing of HiChIP/PLAC-seq/Capture Hi-C to protein-directed loop callers. Use when processing Hi-C data end to end, deciding a resolution for a given depth, or choosing between bulk-Hi-C and protein-directed loop calling.
394. bio-workflows-scrnaseq-pipeline GPTomics/bioSkills · score 70/100 (grade C)
     Orchestrates the end-to-end single-cell RNA-seq pipeline from 10x Cell Ranger output to annotated cell types, chaining ambient-RNA removal, doublet detection, MAD-adaptive QC, normalization, integration, clustering, marker annotation, and (separately) pseudobulk DE + differential abundance. Use when honoring the made-once counting commitments (reference build/Ensembl vintage, --include-introns, cell-calling, feature namespace), ordering correct-then-detect-then-normalize (ambient before doublet before normalize), running per-sample QC before merge, integrating-then-clustering (never testing on integrated values), aggregating to pseudobulk for condition DE instead of cells-as-replicates, or pairing DE with differential abundance. Hands mechanism to the single-cell component skills; not a re-teach of any single step.
395. bio-workflows-somatic-variant-pipeline GPTomics/bioSkills · score 70/100 (grade C)
     Chains a somatic (tumor-normal) SNV/indel and structural-variant pipeline end to end with GATK Mutect2 (or Strelka2), wiring the somatic-specific machinery - panel-of-normals and gnomAD germline-resource priors, GetPileupSummaries/CalculateContamination, and LearnReadOrientationModel FFPE/oxoG orientation-bias filtering fed into FilterMutectCalls. Use when calling somatic mutations from a tumor-normal pair (or tumor-only with PoN caveats), deciding which artifact filter removes which class of false positive, reasoning about VAF/purity/ploidy and clonal-vs-subclonal detection, adding somatic SV/CNV or TMB/MSI/signatures, or routing variants to AMP/ASCO/CAP tier and oncogenicity interpretation (never germline ACMG).
396. canary garrytan/gstack · score 70/100 (grade C)
     Post-deploy canary monitoring. (gstack)
397. canary-watch affaan-m/ECC · score 70/100 (grade C)
     Use this skill to monitor and verify a deployed URL after releases — checks HTTP endpoints, SSE streams, static assets, console errors, and performance regressions after deploys, merges, or dependency upgrades. Smoke / canary / post-deploy verification.
398. canary-watch affaan-m/everything-claude-code · score 70/100 (grade C)
     Use this skill to monitor and verify a deployed URL after releases — checks HTTP endpoints, SSE streams, static assets, console errors, and performance regressions after deploys, merges, or dependency upgrades. Smoke / canary / post-deploy verification.
399. cloud-monitoring-notification-channels-configuration google/skills · score 70/100 (grade C)
     Generates Terraform HCL configurations for Cloud Monitoring notification channels (`google_monitoring_notification_channel`) and adds notification channels to alerting policies (`google_monitoring_alert_policy`) in Google Cloud projects. Use when creating or configuring email, SMS, Slack, PagerDuty, Pub/Sub, or webhook notification channels, verifying Pub/Sub topic compatibility (including cross-project topics, message storage policies, and in-transit enforcement) and publishing permissions for the Cloud Monitoring Service Agent, or adding notification channels to existing alert policies. Don't use for authoring or editing alerting policy conditions, selecting metric descriptors, or writing PromQL or Monitoring Filter queries.
400. cloud-network-insights google/skills · score 70/100 (grade C)
     Guides and automates setup, onboarding, and synthetic monitoring operations for Cloud Network Insights in Google Cloud and Broadcom AppNeta. Use when enabling Cloud Network Insights, deploying or troubleshooting Monitoring Points across cloud and on-premises environments, configuring Network or Web monitoring policies and AppNeta alarm rules, configuring alert policies, querying path alarms and performance metrics, or verifying synthetic path reachability. Don't use for generic network observability, VPC Flow Logs, Cloud NAT, or VPC firewall debugging.
401. computer-vision-expert sickn33/antigravity-awesome-skills · score 70/100 (grade C)
     Computer vision pipeline design and evaluation. Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
402. containerize-aspnetcore github/awesome-copilot · score 70/100 (grade C)
     Containerize an ASP.NET Core project by creating Dockerfile and .dockerfile files customized for the project.
403. convex sickn33/antigravity-awesome-skills · score 70/100 (grade C)
     Convex reactive backend expert: schema design, TypeScript functions, real-time subscriptions, auth, file storage, scheduling, and deployment.
404. cs-senior-engineer alirezarezvani/claude-skills · score 70/100 (grade C)
     Senior Engineer agent for architecture decisions, code review, DevOps, and API design. Orchestrates engineering and engineering-team skills for technical implementation work. Spawn when users need system design, code quality review, CI/CD pipeline setup, or infrastructure decisions.
405. dbt-sf-to-bq-translator google/skills · score 70/100 (grade C)
     Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to BigQuery on Google Cloud. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.
406. deep-research Imbad0202/academic-research-skills · score 70/100 (grade C)
     Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 8 modes: full research, quick brief, paper review, lit-review, fact-check, three-way literature scan, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, WHY HOW WHAT papers, 3W literature scan, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 三段式文獻掃描, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題, 심층 연구, 문헌 조사, 체계적 문헌고찰, 메타분석, 사실 확인, 연구 방향을 잡아줘, 연구 주제 정하는 것을 도와줘, revisión de literatura, metaanálisis
407. foundry-hosted-agent-copilotkit github/awesome-copilot · score 70/100 (grade C)
     Build and evolve agentic web apps that pair a CopilotKit React frontend with a Microsoft Agent Framework agent running as a Microsoft Foundry hosted agent, connected over the AG-UI protocol. Covers choosing the wiring, using Foundry-native primitives instead of hand-rolled plumbing, agent tools, human-in-the-loop approvals, generative UI and shared state, local run and azd deploy, debugging the event stream, and safe upgrades.
408. fp-async sickn33/antigravity-awesome-skills · score 70/100 (grade C)
     Practical async patterns using TaskEither - clean pipelines instead of try/catch hell, with real API examples
409. github-actions-runtime-upgrade-conventions github/awesome-copilot · score 70/100 (grade C)
     Upgrade GitHub Actions to supported runtimes by selecting safe action versions, preserving workflow behavior, and validating post-upgrade execution.
410. gke-ai-troubleshooting-tpu-mxla-hang google/skills · score 70/100 (grade C)
     Diagnose GKE Cloud TPU multi-slice training hangs (Megascale `HANG_DETECTED` logs and hang monitored events) using the ML Diagnostics `Megascale XLA (MXLA) Hang Analyzer` (`gcloud alpha mldiagnostics monitored-events`) and 1-minute Cloud Monitoring multi-slice latency metrics (`kubernetes.io/container/multislice/*`). Distinguishes XLA compiler/HLO launch divergence and host data-input stalls from TPU chip, SparseCore, ICI, or network fabric faults. Use when multi-slice TPU training jobs freeze without progressing steps, emit `HANG_DETECTED`, or stall in collective operations. Don't use for gradual step-time throughput drops without hangs (use gke-ai-troubleshooting-tpu-performance-degradation) or pod preemption/eviction restarts (use gke-ai-troubleshooting-jobset-interruption).
411. google-cloud-slo-alert-configuration google/skills · score 70/100 (grade C)
     Configures Cloud Monitoring PromQL-based Service Level Objective (SLO) alerting policies on Google Cloud for resources registered in App Hub or individually specified. Generates Terraform output. Use when the user asks to configure an SLO or Service Level Objective. Don't use for standard alerting policies.
412. google-cloud-solution-agentic-ai-data-science-workflow google/skills · score 70/100 (grade C)
     Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.
413. java-development github/awesome-copilot · score 70/100 (grade C)
     Day-to-day plain Java development without a framework: Maven and Gradle usage and wrappers, project layout, dependency and vulnerability management, modern Java idioms by JDK version (records, sealed types, pattern matching, virtual threads), SLF4J logging, error and null handling, immutability, concurrency basics, static analysis and formatting (Spotless, Error Prone, -Xlint), packaging (jar, jlink, jpackage) and GitHub Actions CI. Use when writing, building, reviewing or maintaining ordinary Java code.
414. local-llm-expert sickn33/antigravity-awesome-skills · score 70/100 (grade C)
     Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.
415. mcp-deploy-manage-agents github/awesome-copilot · score 70/100 (grade C)
     Skill converted from mcp-deploy-manage-agents.prompt.md
416. orch-pipeline affaan-m/ECC · score 70/100 (grade C)
     Shared orchestration engine behind the orch-* skill family — the gated Research-Plan-TDD-Review-Commit pipeline, size classifier, agent and command map, and two human gates (plan approval, commit confirmation) that orch-* operation skills delegate to. Use indirectly via orch-add-feature, orch-fix-defect, orch-change-feature, orch-refine-code, or orch-build-mvp; read directly only when adding an orch operation or tuning shared phases.
417. orch-pipeline affaan-m/everything-claude-code · score 70/100 (grade C)
     Shared orchestration engine behind the orch-* skill family — the gated Research-Plan-TDD-Review-Commit pipeline, size classifier, agent and command map, and two human gates (plan approval, commit confirmation) that orch-* operation skills delegate to. Use indirectly via orch-add-feature, orch-fix-defect, orch-change-feature, orch-refine-code, or orch-build-mvp; read directly only when adding an orch operation or tuning shared phases.
418. react-native-dev MiniMax-AI/skills · score 70/100 (grade C)
     React Native and Expo development guide covering components, styling, animations, navigation, state management, forms, networking, performance optimization, testing, native capabilities, and engineering (project structure, deployment, SDK upgrades, CI/CD). Use when: building React Native or Expo apps, implementing animations or native UI, managing state, fetching data, writing tests, optimizing performance, deploying to App Store/Play Store, setting up CI/CD, upgrading Expo SDK, or configuring Tailwind/NativeWind.
419. recursive-decision-ledger affaan-m/ECC · score 70/100 (grade C)
     Run repeated rollouts ("Prime Gauss" style recursive prompting) while keeping an append-only decision ledger of trials, marks, coherence checks, and promotion gates, so recursive confidence never auto-approves live trading, deploy, or destructive actions. Use when the user asks for repeated rollouts, marked decision processes, high-dimensional search, stochastic optimization, local-optima exploration, ensemble comparison, or recursive reasoning with a visible evidence trail.
420. recursive-decision-ledger affaan-m/everything-claude-code · score 70/100 (grade C)
     Run repeated rollouts ("Prime Gauss" style recursive prompting) while keeping an append-only decision ledger of trials, marks, coherence checks, and promotion gates, so recursive confidence never auto-approves live trading, deploy, or destructive actions. Use when the user asks for repeated rollouts, marked decision processes, high-dimensional search, stochastic optimization, local-optima exploration, ensemble comparison, or recursive reasoning with a visible evidence trail.
421. salesforce-apex-quality github/awesome-copilot · score 70/100 (grade C)
     Apex code quality guardrails for Salesforce development. Enforces bulk-safety rules (no SOQL/DML in loops), sharing model requirements, CRUD/FLS security, SOQL injection prevention, PNB test coverage (Positive / Negative / Bulk), and modern Apex idioms. Use this skill when reviewing or generating Apex classes, trigger handlers, batch jobs, or test classes to catch governor limit risks, security gaps, and quality issues before deployment.
422. terraform-aws-modules sickn33/antigravity-awesome-skills · score 70/100 (grade C)
     Terraform module creation for AWS — reusable modules, state management, and HCL best practices. Use when building or reviewing Terraform AWS infrastructure.
423. wrap-up alirezarezvani/claude-skills · score 70/100 (grade C)
     Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says "wrap up", "close this out", "what do I own now", "give me the summary", "recap the agent", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained ./my-agent/agent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.
424. xlsx anthropics/skills · score 70/100 (grade C)
     Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
425. agent-designer alirezarezvani/claude-skills · score 69/100 (grade C)
     Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).
426. agent-launcher-orchestrator alirezarezvani/claude-skills · score 69/100 (grade C)
     Use when a user wants to build, launch, grade, or schedule a Claude Managed Agent (CMA) in their own Anthropic account — "build me an agent", "launch this as a managed agent", "run this on a schedule", "grade my agent against a rubric", "set up a nightly worker". Reads the per-session goal (./my-agent/goal.json), routes deterministically to one of five phase sub-skills (interview → stage-launch → grade-iterate → run-without-you → wrap-up) via goal_router.py, and compiles the goal+phase into an execution shape (single-pass workflow / bounded grade→iterate loop / recurring cron deployment loop) via loop_compiler.py. Forks context so heavy intake (build sheets, payloads, eval cases) stays out of the parent thread. All launches are emitted as BYOK curl the user runs with their own key; no tool makes API calls. Inspired by anthropics/launch-your-agent (Apache-2.0). Distinct from engineering/agent-harness (generic domain loop) and engineering/write-a-skill (authors Claude Code skills, not CMAs).
427. agentic-eval github/awesome-copilot · score 69/100 (grade C)
     Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
428. azure-deployment-preflight github/awesome-copilot · score 69/100 (grade C)
     Performs comprehensive preflight validation of Bicep deployments to Azure, including template syntax validation, what-if analysis, and permission checks. Use this skill before any deployment to Azure to preview changes, identify potential issues, and ensure the deployment will succeed. Activate when users mention deploying to Azure, validating Bicep files, checking deployment permissions, previewing infrastructure changes, running what-if, or preparing for azd provision.
429. bio-workflow-management-cwl-workflows GPTomics/bioSkills · score 69/100 (grade C)
     Authors portable, strongly-typed bioinformatics pipelines in the Common Workflow Language (CWL v1.2) as CommandLineTool/Workflow/ExpressionTool documents, validated with cwltool and run at scale on Toil/Arvados/Calrissian. Use when deciding CWL (portability/provenance/regulated) vs Nextflow/WDL/Snakemake; declaring secondaryFiles for indexed companions (.bai/.fai/.dict/.tbi and the caret rule); putting resources/containers under requirements (must-hold) vs hints (advisory) to avoid silent OOM; choosing scatterMethod (dotproduct vs flat_/nested_crossproduct); preferring $(...) parameter refs over ${...} JavaScript for portability; pinning DockerRequirement images; or emitting a CWLProv provenance object for audited/clinical settings.
430. bio-workflow-management-snakemake-workflows GPTomics/bioSkills · score 69/100 (grade C)
     Authors reproducible bioinformatics pipelines with Snakemake - rules wired by output-file pattern, wildcards and expand() for sample fan-out, checkpoints for runtime-unknown outputs, resource/retry escalation, and conda/container software deployment on HPC and cloud. Use when deciding rule-based (Snakemake) vs channel/dataflow (Nextflow) authoring; wiring rules by OUTPUT-file pattern rather than imperative order; using wildcards + expand() for sample fan-out and constraining them to stop silent mis-routing; adding checkpoints when the set of outputs is unknown until a step runs (dynamic DAG); diagnosing why a job reran (or did not) under the mtime-plus-provenance trigger set; escalating memory on retry for OOM-killed jobs; and porting a Snakemake 7 `--cluster`/remote-provider command to the Snakemake 8+ executor-plugin and storage-plugin model (snakemake-executor-plugin-slurm) with `--software-deployment-method`.
431. bio-workflows-atacseq-pipeline GPTomics/bioSkills · score 69/100 (grade C)
     Orchestrates the end-to-end bulk ATAC-seq pipeline from FASTQ to differential accessibility and TF footprints, chaining Nextera-aware fastp QC, Bowtie2 alignment, chrM removal, dedup, a single Tn5 +4/-5 shift, MACS3 peak calling, Corces fixed-width consensus, DiffBind/csaw differential accessibility, and TOBIAS footprinting. Use when committing the reference build + blacklist once, recognizing ATAC has NO input control (the shift-extend model IS the background), applying the Tn5 shift exactly once (never combining -f BAMPE with --shift/--extsize), removing chrM before calling, building a fixed-width consensus so per-sample counts are comparable, or choosing MACS3 vs Genrich vs HMMRATAC. Hands mechanism to the atac-seq component skills; not a re-teach of any single step.
432. bio-workflows-causal-genomics-pipeline GPTomics/bioSkills · score 69/100 (grade C)
     End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.
433. bio-workflows-spatial-pipeline GPTomics/bioSkills · score 69/100 (grade C)
     Orchestrates the end-to-end spatial transcriptomics pipeline from Space Ranger / vendor output to spatial domains and statistics, branching FIRST on platform class (imaging in-situ Xenium/MERFISH/CosMx vs sequencing/capture Visium/Visium HD/Slide-seq). Use when deciding segmentation-vs-deconvolution and the QC floors from the platform class, committing the coordinate/image-registration frame and panel identity, deconvolving multi-cell spots against an annotated scRNA reference (never relabeling spot clusters as cell types), building the spatial neighbor graph on PHYSICAL not expression space, gating spatially-variable genes on FDR, or using a real domain method (BANKSY/BayesSpace/STAGATE) rather than clustering the spatial graph alone. Hands off deconvolution and cell-cell communication to the component skills; not a re-teach of any single step.
434. business-growth-skills alirezarezvani/claude-skills · score 69/100 (grade C)
     Router/index for the 4 business & growth skills bundled in this plugin: customer-success-manager (health scoring, churn risk, expansion), sales-engineer (RFP analysis, competitive matrices, PoC planning), revenue-operations (pipeline, forecast accuracy, GTM efficiency), and contract-and-proposal-writer. Use when a growth/revenue request doesn't obviously match one skill and you need to pick the right one (e.g., 'which accounts are at risk', 'should we bid on this RFP').
435. codeql github/awesome-copilot · score 69/100 (grade C)
     Comprehensive guide for setting up and configuring CodeQL code scanning via GitHub Actions workflows and the CodeQL CLI. This skill should be used when users need help with code scanning configuration, CodeQL workflow files, CodeQL CLI commands, SARIF output, security analysis setup, or troubleshooting CodeQL analysis.
436. content-management-systems github/awesome-copilot · score 69/100 (grade C)
     Workflow for building and modifying content management systems across WordPress, Shopify, Wix, Squarespace, Drupal, WooCommerce, Joomla, HubSpot CMS Hub, Webflow, Adobe Experience Manager, and similar platforms. Use when working on CMS themes, plugins, apps, modules, admin panels, media uploads, content models, editors, markdown pipelines, or static export workflows.
437. dependabot github/awesome-copilot · score 69/100 (grade C)
     Comprehensive guide for configuring and managing GitHub Dependabot. Use this skill when users ask about creating or optimizing dependabot.yml files, managing Dependabot pull requests, configuring dependency update strategies, setting up grouped updates, monorepo patterns, multi-ecosystem groups, security update configuration, auto-triage rules, or any GitHub Advanced Security (GHAS) supply chain security topic related to Dependabot. For pre-commit dependency vulnerability scanning in AI coding agents via the GitHub MCP Server, this skill references the Advanced Security plugin (`advanced-security@copilot-plugins`). Use this skill when an agent needs to scan dependencies for known vulnerabilities before committing.
438. github-actions-hardening github/awesome-copilot · score 69/100 (grade C)
     Security hardening reviewer for GitHub Actions workflow files (.github/workflows/*.yml). Reasons about the Actions threat model that pattern matchers and general code linters miss — untrusted-input script injection, privileged triggers running fork code, mutable action references, and over-scoped tokens. Use this skill when asked to review, audit, harden, or secure a GitHub Actions workflow, when writing a new workflow, or for any request like "is this workflow safe?", "review my CI for security issues", "why is pull_request_target dangerous here?", "pin my actions", or "lock down GITHUB_TOKEN permissions". Covers script injection via ${{ }} interpolation, pull_request_target / workflow_run privilege escalation, SHA-pinning of third-party actions, least-privilege permissions, GITHUB_ENV/GITHUB_OUTPUT injection, secret exposure, OIDC over long-lived credentials, and self-hosted runner exposure on public repositories.
439. gke-ai-troubleshooting-jobset-interruption google/skills · score 69/100 (grade C)
     Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.
440. gke-batch-hpc google/skills · score 69/100 (grade C)
     Runs batch and HPC workloads on GKE, utilizing job queues and parallel processing. Use when running GKE batch jobs, configuring GKE HPC, or setting up GKE job queues. Don't use for standard web application deployments (use gke-app-onboarding instead).
441. gke-workload-scaling-troubleshooting google/skills · score 69/100 (grade C)
     Diagnoses GKE HorizontalPodAutoscaler (HPA) failures — metrics showing as <unknown>, FailedGetResourceMetric / FailedGetScale / FailedComputeMetricsReplicas events, missing Pod resource requests, custom/external metrics-pipeline breakage (FailedGetExternalMetric / FailedGetCustomMetric, unavailable metrics adapter, control-plane firewall blocking the adapter), HPA that won't scale up or down (tolerance / stabilization window / unavailable rate metrics), scale-to/from-zero problems, and slow HPA reaction on large clusters. Use when an HPA isn't scaling a workload as expected or reports metric errors. Don't use for configuring or authoring new HPA/VPA objects or scaling best practices (see the gke-workload-scaling skill), or for Cluster Autoscaler / node-pool sizing.
442. google-cloud-solution-architecture google/skills · score 69/100 (grade C)
     Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud. Use when designing end-to-end cloud solutions, selecting and integrating Google Cloud services, generating architecture diagrams, or conducting requirements discovery for new cloud workloads or migrations. Don't use for single-product tasks (use product-specific skills), initial onboarding or authentication (use google-cloud-recipe-*), Well-Architected Framework reviews or audits (use google-cloud-waf-*), or workloads covered by specialized solution skills.
443. google-cloud-solution-multi-agent-security google/skills · score 69/100 (grade C)
     Designs, deploys, and secures Google Cloud Agent Gateway solutions. Use when the user needs to configure multi-agent security, ingress (CLIENT_TO_AGENT), or egress (AGENT_TO_ANYWHERE) patterns involving Model Armor, IAP, and Agent Registry. Don't use for general Cloud Load Balancing or basic VPC setup not related to Agent Gateways.
444. interview-system-designer alirezarezvani/claude-skills · score 69/100 (grade C)
     This skill should be used when the user asks to "design interview processes", "create hiring pipelines", "calibrate interview loops", "generate interview questions", "design competency matrices", "analyze interviewer bias", "create scoring rubrics", "build question banks", or "optimize hiring systems". Use for designing role-specific interview loops, competency assessments, and hiring calibration systems.
445. laravel-security affaan-m/ECC · score 69/100 (grade C)
     Laravel security best practices — authentication, authorization, Eloquent safety, CSRF, XSS prevention, API security, and secure deployment configurations. Use when reviewing Laravel auth, Eloquent safety, CSRF, XSS, API security, or deployment configuration.
446. laravel-security affaan-m/everything-claude-code · score 69/100 (grade C)
     Laravel security best practices — authentication, authorization, Eloquent safety, CSRF, XSS prevention, API security, and secure deployment configurations. Use when reviewing Laravel auth, Eloquent safety, CSRF, XSS, API security, or deployment configuration.
447. lead-intelligence affaan-m/ECC · score 69/100 (grade C)
     AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.
448. lead-intelligence affaan-m/everything-claude-code · score 69/100 (grade C)
     AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.
449. manim-video browser-use/video-use · score 69/100 (grade C)
     Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.
450. pr-screenshots github/awesome-copilot · score 69/100 (grade C)
     Embed before/after screenshots and annotated images in pull request descriptions. Covers PR description patterns, image upload for Azure DevOps and GitHub, and sizing best practices.
451. process-mapper alirezarezvani/claude-skills · score 69/100 (grade C)
     Use when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, customer-onboarding, claims adjudication) in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time. Pairs Lean / Six Sigma / Theory-of-Constraints canon with deterministic stdlib-only Python tools to produce a process map, a ranked bottleneck list (with severity + root-cause hypothesis), and a cycle-time analysis (P50, P90, value-add ratio, Little's-Law throughput). Distinct from sales-pipeline, system-reliability (SLO), and strategic-OKR work — this is tactical process documentation for internal operations.
452. run-without-you alirezarezvani/claude-skills · score 69/100 (grade C)
     Phase 4 of building a Claude Managed Agent — make it run without you. Turn a graded agent into a recurring scheduled deployment (POSIX-cron), an event-driven curl trigger, or confirmed on-demand use, then finalize the versioned roadmap. Use when the user says "run it every morning", "put it on a schedule", "nightly", "weekly", "automate this", "make it recurring", or when the orchestrator routes phase=run-without-you. deployment_builder.py builds the POST /v1/deployments payload (initial_events must include user.message; optionally nests a user.define_outcome so each firing self-grades); cron_validator.py validates the 5-field cron + IANA timezone and prints the wall-clock DST note; next_directions_writer.py writes NEXT-DIRECTIONS.md. No tool makes API calls — the deployment is created via BYOK curl. Distinct from grade-iterate (the in-session loop) and wrap-up (closeout).
453. salesforce-flow-design github/awesome-copilot · score 69/100 (grade C)
     Salesforce Flow architecture decisions, flow type selection, bulk safety validation, and fault handling standards. Use this skill when designing or reviewing Record-Triggered, Screen, Autolaunched, Scheduled, or Platform Event flows to ensure correct type selection, no DML/Get Records in loops, proper fault connectors on all data-changing elements, and appropriate automation density checks before deployment.
454. senior-computer-vision alirezarezvani/claude-skills · score 69/100 (grade C)
     Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.
455. senior-ml-engineer alirezarezvani/claude-skills · score 69/100 (grade C)
     ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
456. stage-launch alirezarezvani/claude-skills · score 69/100 (grade C)
     Phase 2 of building a Claude Managed Agent — turn a validated build sheet into exact API payloads and a resumable BYOK curl launch script, then launch (environment → agent → session → kickoff) using the founder's OWN Anthropic key. Use when the user says "launch it", "deploy the agent", "create the agent now", or when the orchestrator routes phase=stage-launch. payload_generator.py emits the four ordered payloads; launch_script_writer.py writes launch.sh that reads $ANTHROPIC_API_KEY at runtime and never embeds it; payload_validator.py runs a pre-launch check including an API-key-leak scan. No tool in this skill makes network calls — the user runs launch.sh themselves. Distinct from interview (planning) and grade-iterate (the outcome loop).
457. terraform-skill sickn33/antigravity-awesome-skills · score 69/100 (grade C)
     Terraform infrastructure as code best practices
458. test-automator sickn33/antigravity-awesome-skills · score 69/100 (grade C)
     Master AI-powered test automation with modern frameworks, self-healing tests, and comprehensive quality engineering. Build scalable testing strategies with advanced CI/CD integration.
459. transloadit-media-processing github/awesome-copilot · score 69/100 (grade C)
     Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS/MP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.
460. vcpkg github/awesome-copilot · score 69/100 (grade C)
     Guide for setting up vcpkg in C++ projects, managing dependency versions, and cross-compiling. Covers manifest initialization, CMake and Visual Studio integration, classic-to-manifest migration, version pinning, baselines, overrides, triplets, and cross-compilation. Use when a user is working with vcpkg project setup, installation, version management, or cross-platform builds. For specialized tasks, additional references cover custom registries and overlay ports (references/registries.md), CI/CD and binary caching (references/ci.md), and troubleshooting and dependency lifecycle (references/troubleshooting.md).
461. vercel-deploy bytedance/deer-flow · score 69/100 (grade C)
     Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.
462. video coreyhaines31/marketingskills · score 69/100 (grade C)
     When the user wants to create, generate, or produce video content using AI tools or programmatic frameworks. Also use when the user mentions 'video production,' 'AI video,' 'Remotion,' 'Hyperframes,' 'HeyGen,' 'Synthesia,' 'Veo,' 'Sora,' 'Runway,' 'Kling,' 'Seedance,' 'Hailuo,' 'MiniMax,' 'Pika,' 'Hunyuan,' 'Wan,' 'video generation,' 'AI avatar,' 'talking head video,' 'programmatic video,' 'video template,' 'explainer video,' 'product demo video,' 'record a product demo,' 'feature demo video,' 'in-app demo,' 'video pipeline,' 'copy this edit,' 'match this video style,' 'reverse-engineer this video,' 'edit like this reference,' or 'make me a video.' Use this for video creation, generation, and production workflows. For video content strategy and what to post, see social. For paid video ad creative, see ad-creative.
463. academic-paper Imbad0202/academic-research-skills · score 68/100 (grade C)
     12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, I got reviewer comments, revision roadmap, should we push back, conference rebuttal, grant panel response, audit my rebuttal, check my response draft, AI disclosure, check citations, citation check, check my references, verify references, look over the refs, 寫論文, 學術論文, 引導我寫論文, 審查意見, 我收到審查意見, 修訂路線圖, 評估回覆, 檢查引用, 引用檢查, 檢查參考文獻, 核對文獻, 논문 작성, 초록 작성, 논문 수정, 논문 계획을 도와줘, 심사 의견을 받았어, 심사 의견 반영, 답변서 점검, AI 사용 고지, 인용 확인, 인용 형식 검사, enmendar mi artículo, redactar artículo, guía mi artículo, analizar reseñas, auditar mi respuesta, verificar borrador de respuesta, verificar citas, divulgación de IA.
464. agent-platform-inference google/skills · score 68/100 (grade C)
     Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.
465. agent-platform-tuning google/skills · score 68/100 (grade C)
     Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).
466. bio-clip-seq-ago-clip-mirna-targets GPTomics/bioSkills · score 68/100 (grade C)
     Identify direct miRNA-target interactions from AGO HITS-CLIP, AGO-CLEAR-CLIP (chimeric reads), HEAP (Halo-Ago2 mouse), chimeric eCLIP / miR-eCLIP (deep miRNA-target profiling), or CLASH using chimeric-read processing pipelines, seed-pairing analysis, and 3' auxiliary pairing rules. Use when distinguishing direct miRNA targets from indirect, integrating CLIP-derived target maps with TargetScan / miRDB / DIANA predictions, applying canonical 7mer-8mer seed matching with 3' UTR context, or recovering miRNA-mRNA chimeras at scale.
467. bio-clip-seq-stamp-antibody-free GPTomics/bioSkills · score 68/100 (grade C)
     Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
468. bio-epitranscriptomics-m6anet-analysis GPTomics/bioSkills · score 68/100 (grade C)
     Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.
469. bio-expression-matrix-sparse-handling GPTomics/bioSkills · score 68/100 (grade C)
     Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.
470. bio-molecular-standardization GPTomics/bioSkills · score 68/100 (grade C)
     Standardizes molecular structures using the ChEMBL structure pipeline for normalization and parent selection plus RDKit rdMolStandardize for explicit custom steps such as tautomer canonicalization, salt/solvent stripping, charge handling, stereochemistry handling, mixture selection, and isotope normalization. Explicitly compares ChEMBL, canSARchem, RDKit, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
471. bio-workflow-management-wdl-workflows GPTomics/bioSkills · score 68/100 (grade C)
     Authors bioinformatics pipelines in WDL (Workflow Description Language) run by Cromwell or miniwdl, targeting the GATK/Broad and Terra/AnVIL/BioData Catalyst cloud ecosystem, with tasks, workflows, scatter-gather parallelism, structs, and a runtime block that sizes the cloud VM. Use when deciding to target Terra/AnVIL/GATK/WARP (chosen for the ecosystem, not the language); sizing runtime disks dynamically for a fresh-per-task cloud VM (ceil(size(f)*factor)+buffer); choosing preemptible vs on-demand VMs by task length and idempotency; picking Cromwell (production, cloud, call-caching) vs miniwdl (local dev, miniwdl check linting, readable errors); enabling and debugging call-caching silent-miss modes; pinning Docker by digest for reproducibility and cache stability; or scattering an array for parallel fan-out.
472. bio-workflows-grn-pipeline GPTomics/bioSkills · score 68/100 (grade C)
     Orchestrates gene regulatory network inference from processed single-cell data to regulons and in-silico perturbation, via pySCENIC (RNA-only GRNBoost2 -> cisTarget -> AUCell), SCENIC+ (multiome cisTopic -> pycistarget -> eGRN), and CellOracle perturbation. Use when recognizing that an inferred GRN is UNDIRECTED by default and reporting only the evidence tier delivered (co-expression vs motif-pruned vs enhancer-resolved vs perturbation), matching species/assembly/namespace across the TF-list + cisTarget DB + motif2TF annotation, feeding RAW counts of the cleaned/doublet-free/batch-controlled cells (never imputed/batch-corrected values), running the cisTarget pruning that buys directionality (modules are not regulons without it), or choosing the RNA-only vs multiome path. Hands mechanism to the gene-regulatory-networks component skills; not a re-teach of any single step.
473. bio-workflows-microbiome-pipeline GPTomics/bioSkills · score 68/100 (grade C)
     End-to-end 16S/ITS amplicon workflow from demultiplexed FASTQ to a consensus differential-abundance result, orchestrating cutadapt primer removal, per-run DADA2 ASV inference (learnErrors/mergeSequenceTables/removeBimeraDenovo), region-matched taxonomy assignment, a SEPP/Greengenes2 tree, alpha/beta diversity at a declared sampling depth (phyloseq/vegan, adonis2 paired with betadisper), compositional DA as a consensus of >=2 tools (ALDEx2/ANCOM-BC2) on unrarefied counts, and optional PICRUSt2 functional prediction gated on NSTI. Covers the stage-ordering decisions (primers before truncation, per-run error model, rarefy for diversity not DA, predicted potential not activity) and defers each per-step choice to the six microbiome skills. Use when staging an amplicon study end to end or chaining ASV inference, taxonomy, diversity, and differential abundance. For shotgun reads see workflows/metagenomics-pipeline.
474. bio-workflows-multi-omics-pipeline GPTomics/bioSkills · score 68/100 (grade C)
     Orchestrates VERTICAL bulk multi-omics integration (RNA + protein + methylation on the SAME samples) from harmonization to a validated result, routing to MOFA2 (shared factors), mixOmics/DIABLO (predictive signature), or SNF (patient subtypes). Use when confirming the correspondence is vertical (not horizontal same-features-different-cohorts), joining on a stable sample primary key rather than cbind on assumed row order, normalizing each omic in its OWN space and equalizing block variance BEFORE stacking (or the widest omic hijacks every shared factor), correcting batch ONCE in one place, and validating in a HELD-OUT cohort because in-cohort CV at n<<p is optimistically biased. Hands mechanism to the multi-omics-integration component skills; not a re-teach of any single step.
475. bio-workflows-neoantigen-pipeline GPTomics/bioSkills · score 68/100 (grade C)
     Orchestrates neoantigen discovery from somatic variants to ranked vaccine candidates, chaining HLA typing (OptiType/arcasHLA + LOHHLA), VEP annotation (Wildtype+Frameshift plugins) + expression/readcount annotation, proximal-variant phasing, pVACseq MHC-I/II binding, CCF/clonality, and immunogenicity/quality ranking. Use when recognizing that binding is single-digit PPV and the critical steps are downstream (full-resolution HLA + LOH gating, proximal-variant phasing, clonality from purity+CN not raw VAF, expression), sequencing normalize+annotate -> phase -> HLA -> binding -> quality in the defensible order, dropping candidates on LOH-lost alleles, supplying --phased-proximal-variants-vcf so the mutant peptide is real, or ranking WITHIN patient rather than a fixed IC50 threshold. Hands mechanism to the immunoinformatics component skills; not a re-teach of any single step.
476. d365-solution-blueprint github/awesome-copilot · score 68/100 (grade C)
     Authors a Dynamics 365 Finance and Supply Chain Management Solution Blueprint from scratch through a structured, section-by-section architect interview, establishing scope, target operating model, application and data architecture, integration landscape, migration strategy, security model, ALM, testing, deployment, and support approach, with a decision log capturing rationale and rejected alternatives. Use when the user wants to create D365 implementation architecture documentation, start a D365 implementation, design the architecture, prepare a Solution Blueprint, or identify the architectural decisions the programme must make. Do not use for critique of an existing design; that is a review task rather than blueprint authoring.
477. gke-alert-configuration google/skills · score 68/100 (grade C)
     Configures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.
478. gke-basics google/skills · score 68/100 (grade C)
     Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, or deciding between Autopilot and Standard modes. Don't use for Workload Identity (use gke-workload-identity), GKE networking (use gke-networking), security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).
479. gke-manifest-generation google/skills · score 68/100 (grade C)
     Generates and updates secure, production-ready Kubernetes YAML manifests optimized for GKE Autopilot and GKE Standard clusters. Use when creating or modifying GKE deployment manifests, configuring container security contexts, setting CPU/memory resource limits, defining readiness/liveness/startup probes, mounting secrets and volumes, configuring GKE Gateway API routes, targeting Spot VMs, or deploying AI model inference workloads (vLLM, TGI, Gemma). Don't use for live cluster operations, pod troubleshooting (use gke-workload-troubleshooting), or cluster infrastructure provisioning (use gke-cluster-creation).
480. google-agents-cli-onboarding google/skills · score 68/100 (grade C)
     Onboarding entrypoint for agents-cli in Agent Platform. It should be used when the user wants to "create a new agent", "develop an agent", "build an agent using ADK", "run the agent locally", "debug agent code", "test an agent", "evaluate an agent", "deploy an agent", "publish an agent", "monitor an agent", or needs the ADK (Agent Development Kit) development lifecycle.
481. google-cloud-solution-build-deploy-agents google/skills · score 68/100 (grade C)
     Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.
482. google-cloud-solution-n-tier-serverless-web-app google/skills · score 68/100 (grade C)
     Assists in designing and implementing secure n-tier serverless web applications and microservices on Google Cloud. Use when users need architecture designs, security checklists, Terraform code, or deployment guidance for multi-tier serverless apps, regional data residency / European sovereignty compliance, zero-trust private VPC networking, or Private Service Connect. Don't use for VM, GKE, or non-Google Cloud architectures.
483. mantis-launch google/mantis · score 68/100 (grade C)
     Launches automated vulnerability review campaigns on target files or repositories. Use to initiate Mantis vulnerability review pipelines with automated preflight checks, environment auto-configuration, and runtime overrides (sandboxes, models, endpoints). Don't use for configuring settings without scanning or for manual single-stage reviews.
484. mantis-pipeline-adapter google/mantis · score 68/100 (grade C)
     Interactively guides the design and implementation of custom deterministic orchestrator harnesses. Use when a user wants to build their own pipeline to wrap and run Mantis skills reliably. Don't use for executing the default pipeline directly.
485. opensource-pipeline affaan-m/ECC · score 68/100 (grade C)
     Open-source pipeline: fork, sanitize, and package private projects for safe public release. Chains 3 agents (forker, sanitizer, packager). Triggers: '/opensource', 'open source this', 'make this public', 'prepare for open source'. Use when a private project must be forked, stripped of secrets, and packaged for public release.
486. opensource-pipeline affaan-m/everything-claude-code · score 68/100 (grade C)
     Open-source pipeline: fork, sanitize, and package private projects for safe public release. Chains 3 agents (forker, sanitizer, packager). Triggers: '/opensource', 'open source this', 'make this public', 'prepare for open source'. Use when a private project must be forked, stripped of secrets, and packaged for public release.
487. regex-vs-llm-structured-text affaan-m/ECC · score 68/100 (grade C)
     Decision framework for parsing structured text (quizzes, forms, invoices, receipts, tables) with a hybrid regex-first pipeline — regex extraction handles 95%+ cheaply, a confidence scorer flags low-confidence items, and an LLM validator fixes only the edge cases. Use when choosing between regex and LLM for text extraction, building a cheap document parser, or optimizing extraction cost and accuracy.
488. regex-vs-llm-structured-text affaan-m/everything-claude-code · score 68/100 (grade C)
     Decision framework for parsing structured text (quizzes, forms, invoices, receipts, tables) with a hybrid regex-first pipeline — regex extraction handles 95%+ cheaply, a confidence scorer flags low-confidence items, and an LLM validator fixes only the edge cases. Use when choosing between regex and LLM for text extraction, building a cheap document parser, or optimizing extraction cost and accuracy.
489. universal-scraping-architect alirezarezvani/claude-skills · score 68/100 (grade C)
     Use for web scraping, crawling, document extraction, API parsing, or building validation-heavy data pipelines using Firecrawl or local Python scripts.
490. antithesis-launch antithesishq/antithesis-skills · score 67/100 (grade C)
     Launch an Antithesis run with snouty by discovering the harness layout, building the right Docker Compose config, running `snouty validate`, bailing on validation failure, and then submitting `snouty launch` with sane metadata. Use when the user wants to send, submit, or launch an Antithesis run. This skill takes duration in minutes as input.
491. bio-fragment-analysis GPTomics/bioSkills · score 67/100 (grade C)
     Extracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and tissue-of-origin from plasma WGS. Centers on the nuclease-footprint reframe (every feature re-reads one nucleosome object), the mandatory GC correction, and the cross-protocol non-comparability that breaks naive classifiers. Runs FinaleToolkit (real CLI/Python, MIT) and the Griffin Snakemake pipeline; DELFI is a method, not a package. Use when deriving fragment-based signal from cfDNA, choosing a feature family for detection vs subtyping, or diagnosing why a fragmentomic model failed validation.
492. bio-workflow-management-nextflow-pipelines GPTomics/bioSkills · score 67/100 (grade C)
     Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.
493. bio-workflows-edna-pipeline GPTomics/bioSkills · score 67/100 (grade C)
     End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal (mandatory before DADA2 filterAndTrim), denoising with OBITools3 v3 (obi stats plural; DMS-based) or DADA2 ASVs (Callahan 2017), decontam combined method as screening-not-classifier (Davis 2018), tag-jumping (Schnell 2015) with a platform-dependent baseline (NovaSeq patterned flow cells ~10x MiSeq), Hill-number effective species counts with coverage-based rarefaction (Jost 2006; Chao & Jost 2012; doubling rule), beta-diversity decomposition with MANDATORY PERMANOVA + PERMDISP pair (Anderson & Walsh 2013), constrained ordination, and the read-counts-not-abundance critique (Lamb 2019). Use when processing eDNA samples for biodiversity assessment, deciding ASV vs OTU, configuring OBITools3 v3, interpreting decontam screening, or reporting community comparisons with the dispersion confound check.
494. bio-workflows-longread-sv-pipeline GPTomics/bioSkills · score 67/100 (grade C)
     Orchestrates an end-to-end long-read structural-variant pipeline - basecalling to minimap2 alignment (platform-matched preset) to Sniffles2/cuteSV/pbsv calling to optional assembly-based calling (dipcall/PAV) to two-step .snf cohort merging to Truvari benchmarking - chaining ONT and PacBio HiFi runs while handing the SV signal mechanism off to the component skills. Use when running a long-read SV workflow from reads to a benchmarked callset, choosing the minimap2 preset and SV caller by platform and goal, deciding when long reads are worth it for the insertions and repeat-mediated SVs short reads physically miss, building a joint-genotyped cohort with the two-step .snf design, or parameterizing a Truvari benchmark against GIAB HG002 Tier 1 plus CMRG. Not for the SV signal mechanism itself (see variant-calling/structural-variant-calling) or short-read SV.
495. bio-workflows-multiome-pipeline GPTomics/bioSkills · score 67/100 (grade C)
     Orchestrates the end-to-end 10x Multiome (paired scRNA + scATAC) pipeline from Cell Ranger ARC output to a jointly-embedded, annotated object, chaining per-modality QC, AMULET fragment-based ATAC doublet detection, per-modality normalization (RNA SCT/PCA; ATAC TF-IDF/LSI), WNN (or MultiVI) integration, joint clustering, RNA-based annotation, and LinkPeaks peak-to-gene linking. Use when enforcing the shared cell-barcode intersection between modalities (cellranger-ARC not -atac), keeping per-modality QC/doublets before the joint embedding, dropping the depth-correlated LSI component, annotating identity from RNA (ATAC is regulatory state), treating peak-to-gene links as correlational hypotheses, or aggregating to pseudobulk for cross-condition DE. Hands mechanism to the single-cell and atac-seq component skills; not a re-teach of any single step.
496. bio-workflows-outbreak-pipeline GPTomics/bioSkills · score 67/100 (grade C)
     Orchestrates genomic-epidemiology outbreak investigation from pathogen isolates to transmission networks, forking bacterial (snippy -> Gubbins recombination-masking -> IQ-TREE -> TreeTime -> TransPhylo) vs viral (Nextstrain/augur), with parallel MLST typing (cgMLST delegated to epidemiological-genomics/pathogen-typing) and AMR surveillance. Use when committing ONE reference genome for SNP calling (every isolate and distance inherits its coordinates), applying MANDATORY Gubbins recombination-masking on core.full.aln before the tree for recombining bacteria (skipping it inflates the clock 2-5x), gating time-scaling on a temporal-signal test (TempEst R2 >= 0.3), using a pathogen- AND population-specific cluster threshold rather than a universal SNP cutoff, or pinning pangolin-data/Nextclade/Freyja versions for the viral route. Hands mechanism to the epidemiological-genomics component skills; not a re-teach of any single step.
497. bio-workflows-tcr-pipeline GPTomics/bioSkills · score 67/100 (grade C)
     Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks. Use when deciding bulk vs single-cell (bulk amplicon/RNA-seq -> MiXCR analyze preset -> VDJtools/immunarch depth-normalized diversity and overlap -> figures; 10x paired VDJ -> MiXCR 10x preset or Cell Ranger -> scirpy gene-expression integration, chain QC, clonotype clusters); and TCR vs BCR (TCR -> exact CDR3-nt+V/J clonotypes, VDJtools diversity is fine; BCR -> somatic hypermutation makes exact clonotypes wrong -> Immcantation distToNearest/findThreshold clonal clustering, germline reconstruction, SHM, Dowser lineages); selecting the MiXCR 4.x preset by chemistry and activating its license; downsampling to equal depth before comparing diversity or overlap; and optionally annotating antigen specificity.
498. bio-workflows-timecourse-pipeline GPTomics/bioSkills · score 67/100 (grade C)
     End-to-end bulk time-course analysis from an expression matrix to temporal gene modules and per-cluster pathway enrichment. Orchestrates temporal DE (limma splines or DESeq2 LRT), Mfuzz/tslearn soft clustering of expression-profile shapes, GAM trajectory fitting, per-cluster GO enrichment against a temporal-gene background, and an OPTIONAL circadian rhythm-detection branch (MetaCycle/CosinorPy) that runs only when the design covers >=2 full cycles with >=6-8 evenly spaced samples per cycle. Use when analyzing a bulk time-series expression experiment from any omics platform and deciding limma-splines vs DESeq2-LRT for temporal DE, soft vs hard clustering, whether the sampling design even licenses rhythm detection, and which background to use for enrichment. Not for single-cell pseudotime (see temporal-genomics/trajectory-modeling for the bulk-vs-pseudotime boundary) or unknown-period discovery (see temporal-genomics/periodicity-detection).
499. competitive-report-structure affaan-m/ECC · score 67/100 (grade C)
     Assemble scored competitor profile cards (from benchmark-methodology) into a decision-grade competitive report with landscape map, competitor tiers, benchmarking matrix, white-space analysis, strategic recommendations, and team alignment trigger questions. Use when presenting competitive findings to leadership or a board, writing a competitive landscape report, or as the final step of the competitive analysis pipeline.
500. competitive-report-structure affaan-m/everything-claude-code · score 67/100 (grade C)
     Assemble scored competitor profile cards (from benchmark-methodology) into a decision-grade competitive report with landscape map, competitor tiers, benchmarking matrix, white-space analysis, strategic recommendations, and team alignment trigger questions. Use when presenting competitive findings to leadership or a board, writing a competitive landscape report, or as the final step of the competitive analysis pipeline.
501. DevOps Engineer alirezarezvani/claude-skills · score 67/100 (grade C)
     Builds infrastructure that scales without babysitting. Automates everything worth automating. Monitors before it breaks. Treats clicking in consoles as a production incident waiting to happen. Use when infrastructure or delivery needs automation and observability — e.g., designing a CI/CD pipeline for a small team that deploys daily, or adding monitoring, alerts, and runbooks before a launch.
502. django-security affaan-m/ECC · score 67/100 (grade C)
     Django security best practices, authentication, authorization, CSRF protection, SQL injection prevention, XSS prevention, and secure deployment configurations. Use when reviewing Django authentication, authorization, input handling, or deployment settings.
503. django-security affaan-m/everything-claude-code · score 67/100 (grade C)
     Django security best practices, authentication, authorization, CSRF protection, SQL injection prevention, XSS prevention, and secure deployment configurations. Use when reviewing Django authentication, authorization, input handling, or deployment settings.
504. dotnet-mcp-builder github/awesome-copilot · score 67/100 (grade C)
     Build Model Context Protocol (MCP) servers in C#/.NET against the current ModelContextProtocol 2.x NuGet packages. Helps with cases the model gets wrong without guidance — stale versions (0.x preview or 1.x-era defaults), the v2 stateless-by-default HTTP flip, the 2026-07-28 spec deprecations (roots/sampling/logging), MCP Apps and Tasks extension packages, elicitation URL mode, per-session HTTP wiring, OAuth and reverse-proxy deploy specifics, and debugging MapMcp / STDIO / Streamable-HTTP errors. Also covers STDIO and Streamable HTTP transports (SSE is deprecated), tools, prompts, resources, completions, and a basic .NET MCP client. Trigger when the user says or implies any .NET MCP server work: ModelContextProtocol, McpServerTool, MapMcp, WithStdioServerTransport, "MCP server in C#", "MCP tool in dotnet", "expose this as MCP", or names a primitive (prompt/resource/elicitation/MCP App) in a .NET context. Skip for MCP work in other languages.
505. ecc-recipes affaan-m/ECC · score 67/100 (grade C)
     Map a described workflow to the right ECC command group with run-order and stop condition, or browse all command-group recipe families read live from the commands directory. Advisory only — never executes. Use when asked which commands run a workflow, the command sequence for a task, or to list ECC pipelines; not for executing the task (route to the command itself), single-command docs (use ecc-guide), or prompt rewrites (use prompt-optimizer).
506. ecc-recipes affaan-m/everything-claude-code · score 67/100 (grade C)
     Map a described workflow to the right ECC command group with run-order and stop condition, or browse all command-group recipe families read live from the commands directory. Advisory only — never executes. Use when asked which commands run a workflow, the command sequence for a task, or to list ECC pipelines; not for executing the task (route to the command itself), single-command docs (use ecc-guide), or prompt rewrites (use prompt-optimizer).
507. gdpr-compliant github/awesome-copilot · score 67/100 (grade C)
     Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35.
508. gke-multitenancy google/skills · score 67/100 (grade C)
     Plans and configures multi-tenancy on GKE. Covers namespace isolation, RBAC planning for teams, resource quotas, LimitRanges, network isolation, and cost allocation. Use when designing GKE multi-tenancy, configuring GKE namespaces, setting up resource quotas, or isolating GKE teams. Don't use for single-tenant cluster configuration or general deployment instructions (use gke-basics or gke-app-onboarding instead).
509. google-cloud-solution-agentic-ai-borderless-data-lakehouse google/skills · score 67/100 (grade C)
     Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
510. google-cloud-solution-guided-gke-ai-migration google/skills · score 67/100 (grade C)
     Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to self-hosted inference on GKE, or asks follow-up questions during such a migration (hardware sizing, model staging, manifest generation, validation, traffic cutover). DO NOT use for brand new GKE inference deployments with no existing workload to migrate (use gke-inference instead). DO NOT use if the user intends to automate the migration via the Gemini Cloud Assist MCP server.
511. google-cloud-solution-hybrid-search-alloydb google/skills · score 67/100 (grade C)
     Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.
512. google-cloud-storage-fuse google/skills · score 67/100 (grade C)
     Mounts Cloud Storage buckets on Google Cloud as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when interacting with gcsfuse or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config files, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and configs, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration without a mount (google-cloud-storage-basics) or POSIX shared file systems (Filestore, Managed Lustre; use gke-storage).
513. python-pypi-package-builder github/awesome-copilot · score 67/100 (grade C)
     End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI. Covers all four build backends (setuptools+setuptools_scm, hatchling, flit, poetry), PEP 440 versioning, semantic versioning, dynamic git-tag versioning, OOP/SOLID design, type hints (PEP 484/526/544/561), Trusted Publishing (OIDC), and the full PyPA packaging flow. Use for: creating Python packages, pip-installable SDKs, CLI tools, framework plugins, pyproject.toml setup, py.typed, setuptools_scm, semver, mypy, pre-commit, GitHub Actions CI/CD, or PyPI publishing.
514. slang-shader-engineer github/awesome-copilot · score 67/100 (grade C)
     Use when working with Slang shaders, shader modules, HLSL-compatible GPU code, graphics pipelines, compute shaders, tessellation, ray tracing, parameter blocks, generics, interfaces, capabilities, cross-compilation, shader optimization, shader review, or C++ engine integration for Slang. Trigger on any mention of Slang, .slang files, slangc, SPIR-V from Slang, Slang modules, [shader("compute")], [shader("vertex")], or requests to write/review/refactor shader code with modern language features. Also trigger for Slang-to-HLSL/GLSL/Metal/CUDA cross-compile questions, or when the user says "shader" alongside "generics", "interfaces", "parameter blocks", "autodiff", or "capabilities".
515. aeo alirezarezvani/claude-skills · score 66/100 (grade C)
     Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
516. bio-read-alignment-bwa-alignment GPTomics/bioSkills · score 66/100 (grade C)
     Aligns DNA short reads (paired- or single-end) to a reference genome with bwa-mem2, the maintained successor to BWA-MEM, for WGS/WES and germline/somatic variant-calling pipelines; covers index build, read-group injection, the collate/fixmate/sort/markdup ordering, soft-clipping for SV split reads, ALT/decoy-aware mapping on GRCh38, -K determinism, and streaming straight to a sorted BAM. Use when mapping DNA short reads to a reference for variant calling, coverage, ChIP/ATAC (alongside bowtie2-alignment), or SV detection. RNA-seq spliced alignment is star-alignment/hisat2-alignment; BAM sort/dedup/stats, the QC gate, and the cross-tool MAPQ scale are alignment-files; read trimming is read-qc; counting reads over features is rna-quantification.
517. bio-workflow-management-nf-core-pipelines GPTomics/bioSkills · score 66/100 (grade C)
     Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.
518. bio-workflows-chipseq-pipeline GPTomics/bioSkills · score 66/100 (grade C)
     Orchestrates the end-to-end ChIP-seq pipeline from FASTQ to blacklist-filtered, annotated peaks, chaining fastp QC, Bowtie2 alignment, pre-dedup library-complexity QC (NRF/PBC), duplicate removal, chrM + ENCODE-blacklist filtering, MACS3 peak calling against a matched input, IDR/consensus reproducibility, deepTools signal tracks, and ChIPseeker annotation. Use when committing the reference build + blacklist version + effective genome size once, pairing each IP with its matched control, computing complexity metrics BEFORE dedup, choosing narrow vs broad and MACS3 vs SEACR/Genrich, keeping per-replicate peaks for IDR, or avoiding depth-normalization that erases a spike-in global shift. Hands mechanism to the chip-seq component skills; not a re-teach of any single step.
519. bio-workflows-crispr-editing-pipeline GPTomics/bioSkills · score 66/100 (grade C)
     Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering skills.
520. bio-workflows-crispr-screen-pipeline GPTomics/bioSkills · score 66/100 (grade C)
     End-to-end pooled and single-cell CRISPR screen analysis from FASTQ to hit genes. Orchestrates library design QC, guide counting, six-stage screen QC (plasmid Gini, replicate Pearson, CEGv2 PR-AUC, copy-number artifact), method-appropriate hit calling across MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, and Chronos, cancer-cell-line copy-number correction (CRISPRcleanR / Chronos), batch correction for multi-batch screens, and the specialized branches for combinatorial paralog screens, single-cell Perturb-seq, base-editor variant-function screens, prime-editor screens, and in vivo bottleneck-aware screens. Use when analyzing any pooled CRISPR screen end-to-end, matching the hit-calling method to the experimental design, integrating copy-number correction into the pipeline, or branching the workflow for single-cell, combinatorial, base-editor, prime-editor, or in vivo variants.
521. bio-workflows-cytometry-pipeline GPTomics/bioSkills · score 66/100 (grade C)
     End-to-end flow, spectral, and mass cytometry (CyTOF) pipeline from raw FCS files to differentially abundant/expressed cell populations. Orchestrates the read -> compensate/unmix -> transform -> QC -> doublet-removal -> cluster-or-gate -> annotate -> diffcyt DA/DS chain with flowCore/CATALYST/diffcyt, branching on instrument type and on clustering-vs-gating. Use when processing a cytometry experiment end-to-end, deciding the pipeline path for an instrument, or wiring the flow-cytometry component skills into one analysis with valid sample-level statistics.
522. bio-workflows-genome-annotation-pipeline GPTomics/bioSkills · score 66/100 (grade C)
     Orchestrates genome annotation from assembled contigs to functional annotation, forking prokaryotic (Bakta one-step, genetic-code table from GTDB-Tk) vs eukaryotic (RepeatMask -> BRAKER3 -> functional -> ncRNA), then eggNOG/InterProScan functional assignment and Infernal/tRNAscan ncRNA. Use when committing the pro-vs-eukaryotic path and the genetic-code table from taxonomy (never guessing), annotating ONLY a decontaminated QC-passed assembly (CheckM2 before prokaryotic annotation is non-negotiable), committing the evidence set (RNA-seq + protein drives BRAKER3 training), soft-masking with a curated repeat library before gene prediction, or pinning the tool + DB version for any pangenome comparison. Hands mechanism to the genome-annotation component skills; not a re-teach of any single step.
523. bio-workflows-genome-assembly-pipeline GPTomics/bioSkills · score 66/100 (grade C)
     Orchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it. Profiles the genome first (k-mer spectrum -> size, heterozygosity, ploidy), QCs reads, chooses an assembly path by data type (SPAdes for Illumina, Flye for noisy long reads, hifiasm for HiFi, metaFlye for communities), polishes only when needed, decontaminates, scaffolds with Hi-C, and finishes with three-axis QC (contiguity + completeness + correctness). Use when assembling a genome from raw reads and deciding which assembler, whether to polish, and how to prove the result is good.
524. bio-workflows-imc-pipeline GPTomics/bioSkills · score 66/100 (grade C)
     Orchestrates imaging mass cytometry from raw MCD acquisitions to patient-level spatial analysis, chaining steinbock preprocessing, Mesmer/Cellpose segmentation, single-cell quantification, phenotyping, and squidpy spatial statistics. Use when committing the panel + segmentation frame + pixel size (every per-cell number is a mask-bounded pixel average), compensating channel spillover on PIXELS before segmentation but running REDSEA lateral-spillover on the per-cell table AFTER segmentation, using arcsinh cofactor 1 (not the suspension-CyTOF 5), and aggregating to the PATIENT before any cross-condition test (cells and ROIs from one patient are not independent replicates). Hands mechanism to the imaging-mass-cytometry component skills; not a re-teach of any single step.
525. bio-workflows-metabolic-modeling-pipeline GPTomics/bioSkills · score 66/100 (grade C)
     Orchestrates genome-scale metabolic modeling from a protein FASTA to flux predictions, chaining CarveMe/gapseq reconstruction, memote QC, gap-filling, media-constrained FBA/FVA, gene essentiality, and context-specific models. Use when committing the reconstruction tool (which locks the identifier NAMESPACE forever - BiGG vs ModelSEED vs KEGG, no automatic translation), setting the medium BEFORE FBA (the exchange bounds ARE the medium; essentiality and gap-fill are computed relative to it), curating iteratively (stoichiometric-consistency first, then mass/charge, then directionality, then GPR) with energy-generating-cycle removal, and reading a MEMOTE score as well-formedness NOT correctness. Hands mechanism to the systems-biology component skills; not a re-teach of any single step.
526. commercial-forecaster alirezarezvani/claude-skills · score 66/100 (grade C)
     Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit/best-case/pipe-only board number — especially when the CRO needs to walk the board through funnel math + cohort ARR + per-stage conversion assumptions without the theatre of a single undefended number. Decomposes pipeline into commit, best-case, and pipe-only tiers; projects cohort-level NRR/GRR to surface leaky cohorts before they show up in the consolidated number; scores per-stage funnel confidence so soft-floor stages get treated differently from high-confidence ones. Every output explicitly names the conversion rate used, the data window, and the weighting choice. For Head of Commercial, RevOps, VP Sales, and CRO at quarterly forecast or board prep. NOT financial close (see finance/financial-analysis). NOT strategic CRO hiring/territory (see c-level-advisor/cro-advisor). NOT pricing (see sibling pricing-strategist).
527. cost-aware-llm-pipeline affaan-m/ECC · score 66/100 (grade C)
     Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching. Use when LLM spend needs to come down, or when routing tasks across model tiers and budgets.
528. cost-aware-llm-pipeline affaan-m/everything-claude-code · score 66/100 (grade C)
     Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching. Use when LLM spend needs to come down, or when routing tasks across model tiers and budgets.
529. cro-review alirezarezvani/claude-skills · score 66/100 (grade C)
     /c-level-agents:cro-review [plan] — Pipeline-paranoid interrogation of revenue, win rate, NRR, and ramp time. Use when the forecast misses pipeline coverage, win rates drop, or before scaling the sales team.
530. database-migrations affaan-m/ECC · score 66/100 (grade C)
     Safe, reversible database migration patterns: forward-only production changes, expand-contract zero-downtime renames, concurrent indexes, batched backfills, and per-tool workflows for PostgreSQL, Prisma, Drizzle, Kysely, Django, and golang-migrate. Use when writing a schema or data migration, adding a column or index to a large table, planning a rollback, or preparing a zero-downtime deploy.
531. database-migrations affaan-m/everything-claude-code · score 66/100 (grade C)
     Safe, reversible database migration patterns: forward-only production changes, expand-contract zero-downtime renames, concurrent indexes, batched backfills, and per-tool workflows for PostgreSQL, Prisma, Drizzle, Kysely, Django, and golang-migrate. Use when writing a schema or data migration, adding a column or index to a large table, planning a rollback, or preparing a zero-downtime deploy.
532. detection-engineering-coverage-evaluation google/skills · score 66/100 (grade C)
     Automates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, and deploying gap-closing rules. Don't use for standalone YARA-L 2.0 rule authoring/tuning (use secops-detection-engineering), threat hunting, or SOC investigation.
533. gke-platform-security google/skills · score 66/100 (grade C)
     Plans, configures, and hardens platform-level Google Kubernetes Engine (GKE) cluster security. Covers cluster add-ons (Secret Manager enablement), RBAC hardening (disabling insecure bindings, audit tools), Binary Authorization, Secrets Encryption (--database-encryption-key), Security Posture (--security-posture), enabling Shielded Nodes, GKE Sandbox cluster enablement, GKE IAM roles, and cross-service authentication IAM patterns. Use when securing cluster control planes, hardening GKE RBAC, enabling Shielded Nodes, enabling GKE Sandbox runtime, enabling cluster-wide security add-ons, or managing GKE IAM roles. Don't use for Workload Identity (use gke-workload-identity) or workload-level security (SecretProviderClass, PSS, NetPol, gVisor pod runtimeClassName; use gke-workload-security).
534. publish-to-pages github/awesome-copilot · score 66/100 (grade C)
     Publish presentations and web content to GitHub Pages. Converts PPTX, PDF, HTML, or Google Slides to a live GitHub Pages URL. Handles repo creation, file conversion, Pages enablement, and returns the live URL. Use when the user wants to publish, deploy, or share a presentation or HTML file via GitHub Pages.
535. pytorch-patterns affaan-m/ECC · score 66/100 (grade C)
     PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading. Use when writing or reviewing PyTorch training loops, model architectures, or data loading, or when a run will not reproduce.
536. pytorch-patterns affaan-m/everything-claude-code · score 66/100 (grade C)
     PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading. Use when writing or reviewing PyTorch training loops, model architectures, or data loading, or when a run will not reproduce.
537. rag-architect alirezarezvani/claude-skills · score 66/100 (grade C)
     Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).
538. senior-data-scientist alirezarezvani/claude-skills · score 66/100 (grade C)
     World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.
539. threejs-aaa-graphics-builder majidmanzarpour/threejs-game-skills · score 66/100 (grade C)
     Upgrade Three.js games from prototype visuals to premium browser graphics: art-direction critique, procedural model building, material and texture libraries, world prop kits, shaders, VFX, lighting and render pipeline, LOD and instancing, render budgets, and a 10-category visual scorecard. Use when screenshots still look basic or the user asks for premium, AAA, high-fidelity, showcase, or less-basic graphics.
540. uncloud affaan-m/ECC · score 66/100 (grade C)
     Use when managing an Uncloud cluster — deploying services, configuring Caddy ingress, adding static proxy routes for non-cluster devices, publishing ports, scaling, inspecting logs, or managing machines and volumes with the `uc` CLI.
541. uncloud affaan-m/everything-claude-code · score 66/100 (grade C)
     Use when managing an Uncloud cluster — deploying services, configuring Caddy ingress, adding static proxy routes for non-cluster devices, publishing ports, scaling, inspecting logs, or managing machines and volumes with the `uc` CLI.
542. animation-performance emilkowalski/skills · score 65/100 (grade C)
     Keep web animations inside the frame budget — animate composite-only properties, keep motion off the main thread when the page is busy, and find the cause when an animation drops frames. Covers the layout/paint/composite pipeline, cheaper property swaps, CSS vs WAAPI vs requestAnimationFrame, Framer Motion's hardware-accelerated path, React re-rendering per frame, inherited CSS variable recalculation, blur cost, and when `will-change` actually helps. Use when an animation is janky, choppy, stutters, or drops frames; when choosing which property to animate; when deciding between CSS and JS animation on performance grounds; or when reaching for `will-change` or GPU layers. For choosing easing, duration, or whether to animate at all use animate; for reviewing motion as a whole use review-animations.
543. bio-data-visualization-flow-and-transition-plots GPTomics/bioSkills · score 65/100 (grade C)
     Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
544. bio-immunoinformatics-neoantigen-prediction GPTomics/bioSkills · score 65/100 (grade C)
     Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tiers. Use when nominating vaccine targets, ranking neoantigens, or building a tumor-to-candidate pipeline. Binding details in mhc-binding-prediction; ranking in immunogenicity-scoring.
545. bio-long-read-sequencing-isoseq-analysis GPTomics/bioSkills · score 65/100 (grade C)
     Discovers, classifies, filters, and quantifies full-length transcript isoforms from PacBio Iso-Seq/Kinnex (HiFi) and Oxford Nanopore (cDNA/direct-RNA) long reads, using the isoseq+pigeon pipeline, SQANTI3, and ONT tools (IsoQuant, FLAIR, Bambu, StringTie2). Covers why a novel isoform is an artifact until proven otherwise (RT template-switching, intra-priming, and 5' degradation manufacture junctions and truncations), the SQANTI3 structural categories and their trust order, the Kinnex skera-split step, orthogonal CAGE/poly-A/short-read-junction validation, and why long-read isoform quantification needs EM. Use when building a full-length isoform catalog, classifying/filtering long-read transcripts, running Iso-Seq or ONT cDNA/dRNA analysis, or judging novel-isoform reliability.
546. bio-microbiome-qiime2-workflow GPTomics/bioSkills · score 65/100 (grade C)
     Operates the QIIME2 framework as the glue for an amplicon analysis - the .qza/.qzv artifact model, semantic types (FeatureTable[Frequency], SampleData[PairedEndSequencesWithQuality], Phylogeny[Rooted], FeatureData[Taxonomy]), embedded provenance plus provenance replay, import (Casava/manifest/EMP/BIOM), export, the Metadata object, and the q2cli vs Artifact API interfaces. Covers why a .qza is data-plus-executable-history not a file, why export drops provenance, why a .qzv is terminal, why classifier .qza are version-pinned, and the 2026 distribution/rachis rename. Use when importing reads, choosing a manifest/Casava/EMP/BIOM path, reading or replaying provenance, exporting to BIOM/phyloseq, fixing semantic-type or Phred or sklearn-version errors, or orchestrating the pipeline. Denoising -> amplicon-processing; classifier/DB -> taxonomy-assignment; diversity metric/depth -> diversity-analysis; DA tool -> differential-abundance; PICRUSt2 -> functional-prediction; shotgun moshpit -> metagenomics.
547. bio-reporting-automated-qc-reports GPTomics/bioSkills · score 65/100 (grade C)
     Aggregates per-tool QC metrics (FastQC, fastp, alignment, quantification, variant calling, single-cell) into one interactive MultiQC report, and guides module scoping, sample-name resolution, large-cohort behavior, and turning the report into an actual QC gate. Use when summarizing QC across many samples, building a shareable quality report, or wiring automated QC into a pipeline.
548. bio-variant-calling-deepvariant GPTomics/bioSkills · score 65/100 (grade C)
     Calls germline SNPs and indels with Google DeepVariant, which reframes variant calling as CNN image classification over multi-channel pileup tensors. Covers platform-specific model selection (WGS, WES, PACBIO, ONT_R104, HYBRID_PACBIO_ILLUMINA), one-shot run_deepvariant vs the three-stage make_examples/call_variants/postprocess_variants pipeline, GPU acceleration of call_variants, DeepTrio for family/trio and de-novo calling, and joint genotyping of gVCFs with GLnexus (not GenotypeGVCFs). Use when deciding DeepVariant vs GATK vs DRAGEN, picking the right --model_type for a sequencing platform, avoiding post-hoc GATK hard filters or BQSR that degrade CNN calls, calling de-novo variants in a trio, merging a DeepVariant cohort, or weighing GIAB-trained benchmark accuracy before clinical deployment.
549. bio-workflows-metagenomics-pipeline GPTomics/bioSkills · score 65/100 (grade C)
     End-to-end shotgun metagenomics workflow from FASTQ to taxonomic and functional profiles, orchestrating controls/host depletion, Kraken2+Bracken classification, MetaPhlAn marker profiling, and HUMAnN functional profiling. Covers the controls-first ordering, why Kraken2 read counts are not abundances and MetaPhlAn cell fractions do not equal Bracken read fractions, and the consistent-pipeline framing. Use when profiling shotgun metagenomic samples end to end, or chaining classification, abundance, and function. For resistome see metagenomics/amr-detection; for strains see metagenomics/strain-tracking; for assembly see genome-assembly/metagenome-assembly.
550. custom-product-pages Eronred/aso-skills · score 65/100 (grade C)
     When the user wants to design, deploy, or measure Apple Custom Product Pages (CPP) — the alternate App Store product pages with different screenshots, preview videos, and promo text shown to users coming from specific URLs (typically ad campaigns or social posts). Use when the user mentions "Custom Product Page", "CPP", "alternate product page", "App Store URL variant", "ASA CPP", "campaign-specific landing page", "product page per audience", "App Store Connect CPP", "ppoUrl", "?cpp=" parameter, or "show different screenshots to different ad audiences". For App Store A/B tests on the default page, see ab-test-store-listing. For paid ad campaigns that route to CPPs, see apple-search-ads or ua-campaign.
551. data-scraper-agent affaan-m/ECC · score 65/100 (grade C)
     Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Runs on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions. Use when the user wants to monitor, collect, or track any public data automatically.
552. data-scraper-agent affaan-m/everything-claude-code · score 65/100 (grade C)
     Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Runs on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions. Use when the user wants to monitor, collect, or track any public data automatically.
553. gke-ai-troubleshooting-tpu-performance-degradation google/skills · score 65/100 (grade C)
     Diagnose GKE Cloud TPU training throughput drops and step-time regressions (15%+ TPU duty-cycle drop) using ML Diagnostics Workload Monitoring (`gcloud alpha mldiagnostics monitored-events` / `hypercomputecluster.googleapis.com/v1alpha`) and 1-minute Cloud Monitoring system metrics (`kubernetes.io/node/accelerator/*`). Distinguishes hardware and network fabric throttling from workload resource bottlenecks (HBM capacity, host memory, or host CPU saturation). Use when TPU training throughput or duty cycle drops without crashing pods, when `PERFORMANCE_DEGRADATION` monitored events fire, or when triaging slow multi-slice training steps. Don't use for complete multi-slice XLA execution stalls with `HANG_DETECTED` logs (use gke-ai-troubleshooting-tpu-mxla-hang) or pod eviction/interruption restarts (use gke-ai-troubleshooting-jobset-interruption).
554. google-cloud-solution-rag-enterprise-search-gke-sqldb google/skills · score 65/100 (grade C)
     Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.
555. project-guidelines-example affaan-m/ECC · score 65/100 (grade C)
     Project-specific skill template covering architecture, patterns, testing, and deployment guidance.
556. project-guidelines-example affaan-m/everything-claude-code · score 65/100 (grade C)
     Project-specific skill template covering architecture, patterns, testing, and deployment guidance.
557. ralphinho-rfc-pipeline affaan-m/ECC · score 65/100 (grade C)
     Split an RFC into a multi-agent execution DAG — decompose into work units with dependencies and acceptance tests, run research, plan, implement, test, and review per unit, then merge through a queue with re-based branches and final system verification. Use when a feature is too large for a single agent pass, orchestrating RFC-driven multi-agent execution, or managing merge queues across agent-built units.
558. ralphinho-rfc-pipeline affaan-m/everything-claude-code · score 65/100 (grade C)
     Split an RFC into a multi-agent execution DAG — decompose into work units with dependencies and acceptance tests, run research, plan, implement, test, and review per unit, then merge through a queue with re-based branches and final system verification. Use when a feature is too large for a single agent pass, orchestrating RFC-driven multi-agent execution, or managing merge queues across agent-built units.
559. threejs-asset-pipeline cloudai-x/threejs-skills · score 65/100 (grade C)
     Shrinking glTF assets for three.js with gltf-transform: inspect, validate and optimize commands, Draco, meshopt and quantization, KTX2, WebP and AVIF textures, the loader setup each output needs, and gltfjsx for React. Use when compressing, optimizing or validating 3D models before loading them.
560. attribution coreyhaines31/marketingskills · score 64/100 (grade D)
     When the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions "attribution," "attribution model," "first-touch vs last-touch," "multi-touch," "which channel drives revenue," "what's my real CAC," "my dashboards disagree," "Google/Meta says X but GA says Y," "media mix model," "MMM," "incrementality," "geo lift," "holdout test," "how did you hear about us," "self-reported attribution," "dark social," or wants to instrument attribution themselves — "stitch my bookings to their source," "SavvyCal/Calendly attribution," "close the identify gap," "track conversions on a third-party domain," "first-party / self-hosted attribution." For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI and conversion tracking, see conversion-tracking. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo.
561. bio-copy-number-gatk-cnv GPTomics/bioSkills · score 64/100 (grade D)
     Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.
562. bio-genome-annotation-annotation-qc GPTomics/bioSkills · score 64/100 (grade D)
     Assesses the quality and completeness of a genome annotation with BUSCO (conserved single-copy ortholog recovery), OMArk (proteome completeness, consistency, and contamination), CheckM2 (prokaryotic completeness/contamination), and a gene-set sanity panel (gene count, mono-exonic fraction, protein-length distribution, mRNA:gene ratio, coding density). Covers the assembly-BUSCO-vs-proteome-BUSCO diagnostic, what BUSCO-Duplicated really means, why gene count is a vanity metric, and the QC of transferred annotations. Use when judging whether an annotation is good enough to publish or submit, diagnosing a suspect annotation, or comparing annotation completeness across pipelines.
563. bio-workflows-merip-pipeline GPTomics/bioSkills · score 64/100 (grade D)
     Orchestrates an end-to-end MeRIP-seq / m6A-seq analysis from raw FASTQ to differential m6A peak calls and metagene plots, chaining fastp adapter trimming, STAR splice-aware alignment (NO deduplication for non-UMI MeRIP), deepTools replicate-concordance + IP-enrichment QC, PreSeq saturation curves, exomePeak2 (transcript-aware, GC-bias-aware negative-binomial GLM) peak calling, optional MACS3 broad-peak cross-check, DRACH motif confirmation as a sanity check (NOT a per-peak filter), exomePeak2 differential calling via the four-BAM-vector interface (bam_ip + bam_input control; bam_treated_ip + bam_treated_input treatment), ChIPseeker annotation, and the canonical Guitar metagene with stop-codon enrichment as the biological QC anchor. Use when running a complete MeRIP analysis from raw reads, when chaining the constituent epitranscriptomics skills (merip-preprocessing -> m6a-peak-calling -> m6a-differential -> modification-visualization), or when wrapping the pipeline in Snakemake / Nextflow.
564. cloud-design-patterns github/awesome-copilot · score 64/100 (grade D)
     Cloud design patterns for distributed systems architecture covering 42 industry-standard patterns across reliability, performance, messaging, security, and deployment categories. Use when designing, reviewing, or implementing distributed system architectures.
565. cloud-run-alert-configuration google/skills · score 64/100 (grade D)
     Configures best-practice, high-signal alerting policies for Cloud Run resources on Google Cloud (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.
566. competitive-platform-analysis affaan-m/ECC · score 64/100 (grade D)
     Use when scoping a competitive landscape — identifying, categorising, and score-filtering a competitor set before any benchmarking begins. Decides who counts as a competitor, which tier they belong to, and which sources to mine. First step in the three-skill competitive pipeline; precedes benchmark-methodology.
567. competitive-platform-analysis affaan-m/everything-claude-code · score 64/100 (grade D)
     Use when scoping a competitive landscape — identifying, categorising, and score-filtering a competitor set before any benchmarking begins. Decides who counts as a competitor, which tier they belong to, and which sources to mine. First step in the three-skill competitive pipeline; precedes benchmark-methodology.
568. data-throughput-accelerator affaan-m/ECC · score 64/100 (grade D)
     Diagnose and accelerate large data movement — ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table synchronization — by isolating the true bottleneck, benchmarking variants, and codifying the fastest path with a hard accounting block proving rows and timestamps cohere. Use when a pipeline or backfill is too slow and must get faster without losing data correctness.
569. data-throughput-accelerator affaan-m/everything-claude-code · score 64/100 (grade D)
     Diagnose and accelerate large data movement — ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table synchronization — by isolating the true bottleneck, benchmarking variants, and codifying the fastest path with a hard accounting block proving rows and timestamps cohere. Use when a pipeline or backfill is too slow and must get faster without losing data correctness.
570. gem-devops-guidelines github/awesome-copilot · score 64/100 (grade D)
     Design or review infrastructure, deployment, CI/CD, Docker, Kubernetes, health checks, rollback, feature flags, production readiness, and mobile release workflows. Use for DevOps, platform, container, pipeline, or release tasks.
571. multi-stage-dockerfile github/awesome-copilot · score 64/100 (grade D)
     Create optimized multi-stage Dockerfiles for any language or framework
572. qdrant-clients-sdk github/awesome-copilot · score 64/100 (grade D)
     Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
573. qdrant-deployment-options github/awesome-copilot · score 64/100 (grade D)
     Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which deployment option', 'self-hosted vs cloud', or 'need lowest latency deployment'. Also use when choosing between deployment types for a new project.
574. recsys-pipeline-architect affaan-m/ECC · score 64/100 (grade D)
     Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced For You algorithm. Use this skill whenever the user is building any system that picks "the top K items for a (user, context)" — social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.
575. recsys-pipeline-architect affaan-m/everything-claude-code · score 64/100 (grade D)
     Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced For You algorithm. Use this skill whenever the user is building any system that picks "the top K items for a (user, context)" — social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.
576. ai-readiness anthropics/financial-services · score 63/100 (grade D)
     Scan the portfolio for the highest-leverage AI opportunities and rank where to deploy operating-partner time. Ingests quarterly updates and financials across multiple portfolio companies, identifies quick wins at each, and stacks them into a single ranked action list. Use during quarterly portfolio reviews, annual planning, or when deciding which companies get AI investment first. Triggers on "AI readiness", "AI opportunity scan", "where should we deploy AI", "AI across the portfolio", "AI quick wins", or "which portcos are ready for AI".
577. bio-comparative-genomics-whole-genome-alignment GPTomics/bioSkills · score 63/100 (grade D)
     Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.
578. bio-ecological-genomics-edna-metabarcoding GPTomics/bioSkills · score 63/100 (grade D)
     Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).
579. bio-phasing-imputation-genotype-imputation GPTomics/bioSkills · score 63/100 (grade D)
     Imputes untyped genotypes against a phased reference panel with Beagle, Minimac4, or IMPUTE5 (array data) or from genotype likelihoods with GLIMPSE2, QUILT2, or STITCH (low-coverage WGS), producing per-variant dosages (DS) with a self-estimated quality (Beagle DR2, Minimac R2, IMPUTE INFO). Covers why the honest output is a dosage posterior not a hard call, why GWAS regresses on DS, why the quality metric is an ESTIMATE of r2 from posterior spread (not validation against truth), the DS/GP/HDS fields, the phasing prerequisite, chunking, chrX ploidy, the Michigan/TOPMed servers (the only access to HRC/TOPMed), and low-coverage WGS as the modern array replacement. Use when increasing variant density for GWAS, harmonizing arrays, inferring untyped variants, or imputing low-coverage sequence. Phase first with haplotype-phasing; prepare the panel with reference-panels; filter with imputation-qc; the GWAS test is population-genetics/association-testing; end-to-end orchestration is workflows/gwas-pipeline.
580. bio-population-genetics-scikit-allel-analysis GPTomics/bioSkills · score 63/100 (grade D)
     In-memory Python population genetics with scikit-allel - GenotypeArray/HaplotypeArray/AlleleCountsArray, diversity (pi, theta, Tajima's D), SFS, FST (Weir-Cockerham, Hudson, Patterson), f3/D admixture stats, LD pruning, PCA, and selection scans (iHS, XP-EHH, nSL, Garud H). Nearly every statistic is a ratio or density with one silent denominator bug in two faces: omit is_accessible= and per-base pi/theta divide by total span not accessible bp (deflated 2-5x); average per-SNP FST instead of sum(a)/(sum(a)+sum(b)+sum(c)) and the estimate is rare-variant-biased - scikit-allel returns the (a,b,c) and (num,den) components on purpose to force ratio-of-sums. to_n_alt default fill=0 imputes missing to reference; sfs() is unfolded and wants derived not alt counts; iHS/XP-EHH need phased data and standardization. Use when computing population-genetics statistics in Python, scanning for selection, or building array pipelines. For PLINK QC see plink-basics; for VCF input see variant-calling/vcf-basics.
581. bio-workflows-metabolomics-pipeline GPTomics/bioSkills · score 63/100 (grade D)
     Orchestrates the untargeted LC-MS metabolomics pipeline end-to-end (xcms 4.x feature extraction, QC/drift/normalization, confidence-stratified annotation, permutation-validated statistics, background-aware pathway mapping), naming what each stage decides and where it silently fails. Use when running a full LC-MS metabolomics study from raw mzML to enriched pathways and needing the honest handoffs between stages. Each stage defers to its component skill for parameters and traps; for stable-isotope flux (a separate branch, not this untargeted flow) see metabolomics/isotope-tracing.
582. bio-workflows-proteomics-pipeline GPTomics/bioSkills · score 63/100 (grade D)
     Orchestrates bottom-up proteomics from a search engine's output (MaxQuant/FragPipe/DIA-NN) to differential protein abundance with limma/DEqMS/MSstats. Use when committing the search database + acquisition mode (DDA vs DIA) up front, re-controlling FDR at PSM AND peptide AND protein-group level (not just PSM), removing contaminant/reverse rows and inspecting RAW distributions before normalizing, bridging cross-plex TMT with an IRS reference channel, modeling MNAR missingness rather than downshift-imputing on/off proteins, batching as a covariate (not pre-subtracted), and testing with treat()/DEqMS. Hands mechanism to the proteomics component skills; not a re-teach of any single step.
583. bio-workflows-smrna-pipeline GPTomics/bioSkills · score 63/100 (grade D)
     Orchestrates the end-to-end small RNA-seq pipeline from FASTQ to differential miRNAs and expression-filtered targets, chaining kit-aware cutadapt trimming (adapter on every read, UMI/4N handling), miRge3 known+isomiR quantification or miRDeep2 novel discovery, compositionally-aware DESeq2, and miRanda target prediction. Use when committing the library-kit adapter/UMI handling once, choosing the NORMALIZER (which drives which miRNAs are called DE more than the DE model does), deciding known quantification vs novel discovery, handling biofluid/plasma libraries that lack a trustworthy endogenous normalizer, routing tRF/piRNA reads to their own profiling, or feeding RAW (not RPM) counts with size-factor inspection into DE. Hands mechanism to the small-rna-seq component skills; not a re-teach of any single step.
584. safety-guard affaan-m/ECC · score 63/100 (grade D)
     Guard against destructive operations with three modes: Careful intercepts dangerous commands (rm -rf, git push --force, DROP TABLE) for confirmation, Freeze locks writes to one directory, and Guard combines both. Advisory: the agent applies these rules itself; no hook ships with it. Use when working on production systems, running agents autonomously, restricting edits to a directory, or during migrations, deploys, and data changes.
585. safety-guard affaan-m/everything-claude-code · score 63/100 (grade D)
     Guard against destructive operations with three modes: Careful intercepts dangerous commands (rm -rf, git push --force, DROP TABLE) for confirmation, Freeze locks writes to one directory, and Guard combines both. Advisory: the agent applies these rules itself; no hook ships with it. Use when working on production systems, running agents autonomously, restricting edits to a directory, or during migrations, deploys, and data changes.
586. security-guidance alirezarezvani/claude-skills · score 63/100 (grade D)
     PreToolUse security-anti-pattern hook for Claude Code. Catches 12 common security risks (command injection, XSS, SQL injection, unsafe deserialization, GitHub Actions workflow injection, eval/new Function code injection) BEFORE the Edit/Write/MultiEdit operation completes. Session-state caching prevents duplicate warnings on the same file+rule combo. Stdlib only — no dependencies. Use when you want a safety net during Claude Code sessions that touch security-sensitive code (auth, payments, user input handling, IaC). Disable with ENABLE_SECURITY_REMINDER=0 if you need to perform a verified-safe operation that would otherwise trip a pattern. Triggers — "add security hook", "block unsafe code", "detect command injection before write", "prevent SQL injection patterns", "security warning hook".
587. gke-ai-troubleshooting-handle-disruption-gpu-tpu google/skills · score 62/100 (grade D)
     Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU/TPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy configuration, or non-disruption workload deployment.
588. Mistral AI Automation ComposioHQ/awesome-claude-skills · score 62/100 (grade D)
     Automate Mistral AI operations -- manage files and libraries, upload documents for fine-tuning, batch processing, and OCR, track fine-tuning jobs, and build RAG pipelines via the Composio MCP integration.
589. asc-shots-pipeline rudrankriyam/app-store-connect-cli-skills · score 61/100 (grade D)
     Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload pipelines.
590. bio-copy-number-focal-amplification-ecdna GPTomics/bioSkills · score 61/100 (grade D)
     Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.
591. google-cloud-global-frontend-configuration google/skills · score 61/100 (grade D)
     Guides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).
592. video-content-strategist alirezarezvani/claude-skills · score 61/100 (grade D)
     Use when planning video content strategy, writing video scripts, optimizing YouTube channels, building short-form video pipelines (Reels, TikTok, Shorts), or repurposing long-form content into video. Triggers: 'start a YouTube channel', 'video content strategy', 'write a video script', 'repurpose into video', 'YouTube SEO', 'short-form video'. NOT for written blog content (use content-production). NOT for social captions without video (use social-media-manager).
593. bio-genome-annotation-eukaryotic-gene-prediction GPTomics/bioSkills · score 60/100 (grade D)
     Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bias against orphan genes. Use when annotating a newly assembled eukaryotic genome, choosing a gene-prediction pipeline based on available evidence, or diagnosing a poor annotation.
594. Docker Hub Automation ComposioHQ/awesome-claude-skills · score 60/100 (grade D)
     Automate Docker Hub operations -- manage organizations, repositories, teams, members, and webhooks via the Composio MCP integration.
595. events coreyhaines31/marketingskills · score 60/100 (grade D)
     When the user wants to plan, run, sponsor, speak at, or get pipeline from events — webinars, conferences, trade shows, meetups, dinners, workshops, virtual summits, or user conferences. Also use when the user mentions 'event marketing,' 'field marketing,' 'run a webinar,' 'webinar funnel,' 'show-up rate,' 'should we sponsor,' 'sponsor a conference,' 'trade show booth,' 'booth strategy,' 'event ROI,' 'badge scans,' 'event follow-up,' 'speaking slot,' 'CFP,' 'conference talk,' 'host a dinner,' 'user conference,' or 'virtual summit.' Covers all four roles: hosting, sponsoring/exhibiting, speaking, and attending. For product launch moments, see launch. For the partnership side of joint webinars, see co-marketing. For ongoing community programs, see community-marketing. For podcast appearances, see public-relations. For the email sequences themselves, see emails.
596. operator-audit alirezarezvani/claude-skills · score 60/100 (grade D)
     Run the full Kubernetes Operator audit (CRD + reconcile + capability) on the current repo
597. bio-clinical-databases-variant-prioritization GPTomics/bioSkills · score 59/100 (grade D)
     Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.
598. bio-epidemiological-genomics-amr-surveillance GPTomics/bioSkills · score 59/100 (grade D)
     Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.
599. bio-epidemiological-genomics-variant-surveillance GPTomics/bioSkills · score 59/100 (grade D)
     Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.
600. bio-population-genetics-population-structure GPTomics/bioSkills · score 59/100 (grade D)
     Infers and describes population structure with PCA (plink2 --pca, smartpca/EIGENSOFT, FlashPCA2), model-based clustering (ADMIXTURE, fastSTRUCTURE), FST estimators (Weir-Cockerham vs Hudson), and f-statistics (f3/f4/D via AdmixTools/admixr), plus Python plotting of PCs and Q barplots. Every output is a model-conditioned description of variance, not truth: PCs conflate ancestry with LD/inversions/relatedness/batch, ADMIXTURE Q-values are panel- and K-dependent artifacts, and CV-minimum K is a guide not the true population count. FST must combine SNPs as a ratio of averages (sum numerators / sum denominators), never an average of per-SNP FST; negative per-SNP FST is normal and must not be clamped. f3/f4/D need a block jackknife or the significance is fake. Use when running PCA, ADMIXTURE, FST, or f-statistics on QC'd genotypes. For QC and KING relatedness see plink-basics; for LD pruning see linkage-disequilibrium; for array-based Python pipelines see scikit-allel-analysis.
601. brief alirezarezvani/claude-skills · score 59/100 (grade D)
     /c-level-agents:brief [topic] — Generate a one-page strategy brief from an office-hours intake. First step in the strategic sprint pipeline. Use when a strategic question needs to be framed before boardroom deliberation — e.g. locking options, assumptions, and success criteria for a pricing change or a market-entry decision.
602. cs-growth-strategist alirezarezvani/claude-skills · score 59/100 (grade D)
     Growth Strategist agent for revenue operations, sales engineering, customer success, and business development. Orchestrates business-growth skills. Spawn when users need pipeline analysis, churn prevention, expansion scoring, sales demos, or proposal writing.
603. enterprise-agent-ops affaan-m/ECC · score 59/100 (grade D)
     Operational controls for long-lived or cloud-hosted agent systems — runtime lifecycle (start, pause, stop, restart), observability (logs, metrics, traces), least-privilege safety scopes and kill switches, and rollout/rollback change management with audit logs and success/cost metrics. Use when running production agent fleets on PM2, systemd, or containers that need monitoring, incident response, or deployment gates.
604. enterprise-agent-ops affaan-m/everything-claude-code · score 59/100 (grade D)
     Operational controls for long-lived or cloud-hosted agent systems — runtime lifecycle (start, pause, stop, restart), observability (logs, metrics, traces), least-privilege safety scopes and kill switches, and rollout/rollback change management with audit logs and success/cost metrics. Use when running production agent fleets on PM2, systemd, or containers that need monitoring, incident response, or deployment gates.
605. bio-spatial-transcriptomics-spatial-proteomics GPTomics/bioSkills · score 58/100 (grade D)
     Analyzes multiplexed antibody-imaging data (CODEX/PhenoCycler, MIBI-TOF, IMC, CyCIF, Opal/Vectra mIF) as continuous protein intensity rather than transcript counts, using scimap and squidpy. Use when choosing an intensity transform/normalization (arcsinh cofactor vs z-score vs percentile -- NOT log1p-of-counts) and correcting channel spillover and antibody-batch effects; deciding whether to phenotype by gating or by clustering on intensities; recognizing that a bounded antibody panel makes marker absence uninformative; treating whole-cell segmentation (Mesmer) as the dominant error source; and knowing which platform applies and when to defer to the imaging-mass-cytometry skills for the IMC pipeline.
606. deal-tracker anthropics/financial-services · score 58/100 (grade D)
     Track multiple live deals with milestones, deadlines, action items, and status updates. Maintains a deal pipeline view and surfaces upcoming deadlines and overdue items. Use when managing a book of business, tracking process milestones, or preparing for weekly deal reviews. Triggers on "deal tracker", "deal status", "where are we on", "process update", "deal pipeline", or "weekly deal review".
607. Kommo Automation ComposioHQ/awesome-claude-skills · score 58/100 (grade D)
     Automate Kommo CRM operations -- manage leads, pipelines, pipeline stages, tasks, and custom fields -- using natural language through the Composio MCP integration.
608. Lever Automation ComposioHQ/awesome-claude-skills · score 58/100 (grade D)
     Automate recruiting workflows in Lever ATS -- manage opportunities, job postings, requisitions, pipeline stages, and candidate tags through the Composio Lever integration.
609. llm-cost-optimizer alirezarezvani/claude-skills · score 57/100 (grade D)
     Use proactively whenever LLM API costs come up -- or should. Triggers include: 'my AI costs are too high', 'optimize token usage', 'which model should I use', 'LLM spend is out of control', 'implement prompt caching', 'we're about to launch an AI feature', 'build me an AI endpoint'. Don't wait for an explicit cost complaint -- if someone is building an AI feature, designing an LLM endpoint, or choosing between models, cost architecture belongs in the conversation. Apply immediately when any of these are true: a system prompt appears that exceeds a few hundred tokens, all requests are hitting the same model, max_tokens is not set, or no per-feature cost logging exists. NOT for RAG pipeline design (use rag-architect). NOT for improving prompt quality or effectiveness (use senior-prompt-engineer).
610. pipeline alirezarezvani/claude-skills · score 57/100 (grade D)
     Detect stack and generate CI/CD pipeline configs. Usage: /pipeline <detect|generate> [options]
611. qdrant-performance-optimization github/awesome-copilot · score 57/100 (grade D)
     Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.
612. threejs-postprocessing cloudai-x/threejs-skills · score 57/100 (grade D)
     Three.js post-processing: composer pass chains, built-in renderer effects, and the WebGPU/TSL node pipeline — bloom, depth of field, ambient occlusion, custom screen effects. Use when adding visual effects, color grading, blur, glow, or screen-space shaders, on WebGL or with WebGPU and TSL.
613. datanalysis-credit-risk github/awesome-copilot · score 56/100 (grade D)
     Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
614. post-mortem alirezarezvani/claude-skills · score 55/100 (grade D)
     /c-level-agents:post-mortem [decision] — Honest retrospective on an executed decision, scored against original assumptions and dissent. Closes the strategic sprint loop. Use when a decision hits its 90-day review checkpoint or its kill criteria trigger — e.g. scoring last quarter's pricing change against its pre-committed success metrics.
615. taste affaan-m/ECC · score 54/100 (grade D)
     Creative-direction layer for music videos and short-form edits in the angelcore / cloud-trance / hyperpop family — a named-genre aesthetic vocabulary, mood + color + light system, beat-synced editing grammar, and a pipeline chaining ECC's video skills from b-roll generation to distribution. Use when a video must feel intentional rather than merely functional — music videos, fancams, moodboard-driven reels, or giving AI-generated b-roll a coherent visual direction.
616. taste affaan-m/everything-claude-code · score 54/100 (grade D)
     Creative-direction layer for music videos and short-form edits in the angelcore / cloud-trance / hyperpop family — a named-genre aesthetic vocabulary, mood + color + light system, beat-synced editing grammar, and a pipeline chaining ECC's video skills from b-roll generation to distribution. Use when a video must feel intentional rather than merely functional — music videos, fancams, moodboard-driven reels, or giving AI-generated b-roll a coherent visual direction.