Agent skills you can check before you install
Public SKILL.md skills from GitHub repositories with 50+ stars and from the vendors themselves, scanned and scored. Install any of them with one command in Claude Code, Cursor, Codex, GitHub Copilot or Gemini CLI.
45,604 skills from 2,205 repositories · updated 7 hours ago
45,602 skills
- loopysickn33/agentic-awesome-skillsDiscover, find, compare, audit, repair, adapt, craft, run, debrief, and prepare repeatable AI-agent loops for publication.OtherNo warnings★ 48.5K↓ —Also in 1 other repo
- fp-reactsickn33/agentic-awesome-skillsPractical patterns for using fp-ts with React - hooks, state, forms, data fetching. Includes version-specific React examples; verify framework/client boundaries.EngineeringNo warnings★ 48.5K↓ —
- grill-mesickn33/agentic-awesome-skillsA relentless interview to sharpen a plan or design.DesignNo warnings★ 48.5K↓ —
- hunt-atosickn33/agentic-awesome-skillsHunt account takeover taxonomyOtherNo warnings★ 48.5K↓ —
- seo-geosickn33/agentic-awesome-skillsOptimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.MarketingNo warnings★ 48.5K↓ —
- google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skillsDiscovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse instead).OfficialDataNo warnings★ 21.6K↓ —
- archify-reviewtt-a1i/archifyReview Archify issues, PRs, or code through value, cost, and impact to support evidence-based maintenance decisions. Use for issue triage, change reviews, and code quality assessments.EngineeringNo warnings★ 89K↓ —
- java-prosickn33/agentic-awesome-skillsMaster Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns.OtherNo warnings★ 48.5K↓ —
- markdown-mermaid-writingK-Dense-AI/scientific-agent-skillsWrites scientific Markdown documentation and Mermaid diagrams for workflows, relationships, timelines, and schemas. Provides syntax references, document templates, accessibility guidance, and version-aware rendering checks. Use when a user requests Markdown, Mermaid, or a text-based structural diagram; quantitative scientific figures require suitable plotting tools.WritingNo warnings★ 54.5K↓ —
- google-cloud-recipe-authgoogle/skillsProvides expert guidance on Identity and Access Management (IAM) and authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.OfficialOtherNo warnings★ 21.6K↓ —Also in 1 other repo
- gke-ai-troubleshooting-tpu-performance-degradationgoogle/skillsDiagnose 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).OfficialDevOpsNo warnings★ 21.6K↓ —
- google-cloud-solution-rag-enterprise-search-gke-sqldbgoogle/skillsDiscovers 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.OfficialDevOpsNo warnings★ 21.6K↓ —
- google-cloud-solution-agentic-ai-borderless-data-lakehousegoogle/skillsDiscovers 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).OfficialDevOpsNo warnings★ 21.6K↓ —
- shortsickn33/agentic-awesome-skillsRewrite the previous response more briefly while preserving the substance.OtherNo warnings★ 48.5K↓ —Also in 1 other repo
- google-cloud-solution-guided-gke-ai-migrationgoogle/skillsGuides 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.OfficialDevOpsNo warnings★ 21.6K↓ —
- incident-postmortemgithub/awesome-copilotUse when an outage, production incident, or significant service degradation has occurred and the team needs to write a structured blameless post-mortem. Triggers on phrases like "write a post-mortem", "incident review", "what went wrong", "outage report", "root cause analysis", or "RCA". Covers timeline reconstruction, contributing factor analysis, impact quantification, and action item generation with owners.WritingNo warnings★ 40.4K↓ —
- gke-ai-troubleshooting-handle-disruption-gpu-tpugoogle/skillsDiagnoses, 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.OfficialDevOpsNo warnings★ 21.6K↓ —
- gem-devops-guidelinesgithub/awesome-copilotDesign 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.DevOpsNo warnings★ 40.4K↓ —
- ruby-prosickn33/agentic-awesome-skillsWrite idiomatic Ruby code with metaprogramming, Rails patterns, and performance optimization. Specializes in Ruby on Rails, gem development, and testing frameworks.TestingNo warnings★ 48.5K↓ —
- gke-ai-troubleshooting-node-unresponsive-timeoutgoogle/skillsDiagnose and mitigate GKE TPU or GPU nodes stuck in NotReady / NodeStatusUnknown ("Kubelet stopped posting node status") due to host kernel panics, hardware lockups, or disabled node auto-repair. Use when nodes stop heartbeating beyond the node auto-repair threshold and pods remain stuck in Terminating. Don't use for healthy nodes, pod-only application crashes, or routine GKE upgrades.OfficialOtherNo warnings★ 21.6K↓ —
- consciousness-councilK-Dense-AI/scientific-agent-skillsStructures a multi-perspective council exercise for decisions, research trade-offs, and creative challenges. Simulates thinking archetypes, separates evidence from assumptions and values, and synthesizes a conditional recommendation. Use when the user requests a council, panel, devil's advocate analysis, "mind council", or deliberate comparison of perspectives on a difficult choice.ScienceNo warnings★ 54.5K↓ —
- benchling-integrationK-Dense-AI/scientific-agent-skillsBenchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.EngineeringNo warnings★ 54.5K↓ —
- gtm-0-to-1-launchgithub/awesome-copilotLaunch new products from idea to first customers. Use when launching products, finding early adopters, building launch week playbooks, diagnosing why adoption stalls, or learning that press coverage does not equal growth. Includes the three-layer diagnosis, the 2-week experiment cycle, and the launch that got 50K impressions and 12 signups.MarketingNo warnings★ 40.4K↓ —
- supabasesickn33/agentic-awesome-skillsUse when doing ANY task involving Supabase.ProductivityNo warnings★ 48.5K↓ —
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
google/skills
OfficialDataUpdated 2 days ago
From the author
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse instead).
Install
npx skills add google/skills --skill google-cloud-solution-agentic-analytics-spark-knowledge-catalog -a claude-code
Installs to .claude/skills/google-cloud-solution-agentic-analytics-spark-knowledge-catalog/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Download .zip
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
What it can touch
Instructions onlyUses the networkLicence: APACHE-2.0
Safety checks on SKILL.md
No warnings.
Why 95
Security100
Provenance100
Maintenance100
Licence100
Adoption67
Files (6)
- SKILL.md
- assets/output-template.md
- references/attribution.md
- references/design-recommendations.md
- references/knowledge-catalog-documentation.md
- references/product-selection-guidance.md