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.
49,492 skills from 2,391 repositories · updated 2 hours ago
49,492 skills
- gke-app-onboardinggoogle/skillsManages 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).OfficialDevOpsNo warnings★ 21.9K↓ —
- polarssickn33/agentic-awesome-skillsExpression-based DataFrame library for Python and Rust. Use for eager or lazy tabular analytics, ETL, and pandas migration; streaming support and memory needs depend on the query.DataNo warnings★ 49.1K↓ —
- moltdjLeoYeAI/agent-master-skillsSoundCloud for AI bots. Generate tracks and podcasts, share on Moltbook, and earn from tips + royalties.OtherNo warnings★ 2.2K↓ —
- grok-aiLeoYeAI/agent-master-skillsHarness grok-ai capabilities inside ClawHub to dissect, annotate, and transform your video content with razor-sharp intelligence. This skill lets you query footage conversationally, extract timestamped insights, and apply context-aware edits driven by Grok's reasoning engine. Whether you're cutting highlight reels or analyzing recorded presentations, grok-ai handles the heavy lifting. Supports mp4, mov, avi, webm, and mkv formats.MediaNo warnings★ 2.2K↓ —
- gke-cluster-autoscalergoogle/skillsTrigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).OfficialPlanningNo warnings★ 21.9K↓ —
- tddNewLeoYeAI/agent-master-skillsRun a red-green-refactor TDD workflow — generate failing tests first, implement to green, then check coverage gaps. Usage: /tdd <generate|coverage|validate> [target]TestingNo warnings★ 2.2K↓ —
- cro-methodologywondelai/skillsAudit websites and landing pages for conversion issues and design evidence-based A/B tests. Use when the user mentions "landing page isnt converting", "conversion rate", "A/B test", "why visitors leave", "objection handling", "bounce rate", "conversion funnel", "increase signups", or "people add to cart but dont buy". Also trigger when diagnosing why signups are low, designing experiment hypotheses, or auditing checkout flows for friction points. Covers funnel mapping, persuasion assets, and objection/counter-objection frameworks. For overall marketing strategy, see one-page-marketing. For usability issues, see ux-heuristics.MarketingNo warnings★ 2.6K↓ —Also in 2 other repos
- kaltstart-triageKlotzkette/claude-fuer-deutsches-rechtFür Robotik-Recht-Kompass: routet Rolle, Frist, Unterlagen und Fachschritt; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- jobs-to-be-donewondelai/skillsDiscover what customers truly need by analyzing the "job" they hire your product to do. Use when the user mentions "customer discovery", "why customers churn", "what job does this solve", "competing against luck", "product-market fit", "switching behavior", "milkshake moment", or "functional vs emotional jobs". Also trigger when investigating why users choose competitors, designing features around real customer needs, or reframing a value proposition. Covers JTBD interviews, competition analysis, and jobs-oriented roadmaps. For product positioning, see obviously-awesome. For rapid validation, see design-sprint. For non-leading interview technique, see mom-test.TestingNo warnings★ 2.6K↓ —Also in 2 other repos
- pybammK-Dense-AI/scientific-agent-skillsSimulates lithium-ion battery charge, discharge and rest experiments with PyBaMM, records parameter-set provenance, checks mesh and solver sensitivity, and compares predicted voltage curves with measured cycling data. Use for SPM or DFN electrochemical battery modeling, C-rate protocols, voltage cutoffs, parameter studies and numerical validation of battery simulations.OtherNo warnings★ 56.1K↓ —Also in 2 other repos
- inspired-productwondelai/skillsBuild empowered product teams using discovery and delivery dual-track. Use when the user mentions "product discovery", "empowered teams", "feature factory", "opportunity assessment", "product vision", "product strategy", "what should we build", or "our roadmap is just a feature list". Also trigger when restructuring teams away from output-driven models, or deciding what to build next based on outcomes. Covers discovery techniques, team structure, opportunity assessment, vision/strategy, and continuous delivery. For customer interviews, see mom-test. For ongoing discovery systems, see continuous-discovery.TestingNo warnings★ 2.6K↓ —Also in 2 other repos
- working-with-legacy-codewondelai/skillsSafely change and test untested codebases using Feathers' "Working Effectively with Legacy Code". Use when the user mentions "legacy code", "no tests", "untested codebase", "how do I test this", "seams", "characterization tests", "golden master", "sprout method", "afraid to change this code", "monster method", "dependency breaking", or "inherited a messy codebase". Also trigger when changing code without tests safely, getting a class under test when constructors, statics, or singletons block it, adding features to tangled modules, or planning incremental test coverage for an old codebase. Covers the legacy-code change algorithm, seams, characterization tests, sprout/wrap, and dependency-breaking techniques. For refactoring code that already has tests, see refactoring-patterns. For day-to-day code quality, see clean-code.EngineeringNo warnings★ 2.6K↓ —Also in 2 other repos
- team-topologieswondelai/skillsOrganize business and technology teams for fast flow using Skelton & Pais's "Team Topologies". Use when the user mentions "team topologies", "Conway's law", "platform team", "stream-aligned team", "team boundaries", "cognitive load", "how should we split teams", "who owns this service", "team dependencies", or "reorg". Also trigger when reorganizing engineering teams, aligning team and service boundaries, splitting a monolith and deciding ownership, reducing cross-team handoffs, or designing an internal platform. Covers the four team types, three interaction modes, the inverse Conway maneuver, and fracture planes. For bounded contexts, see domain-driven-design. For dependency direction in code, see clean-architecture.DesignNo warnings★ 2.6K↓ —Also in 2 other repos
- pragmatic-programmerwondelai/skillsApply meta-principles of software craftsmanship: DRY, orthogonality, tracer bullets, and design by contract. Use when the user mentions "best practices", "pragmatic approach", "broken windows", "tracer bullet", "software craftsmanship", "avoid technical debt", "code ownership", or "how do I become a better developer". Also trigger when evaluating build-vs-buy decisions, designing estimation approaches, or choosing between reversible and irreversible architectural decisions. Covers estimation, domain languages, and reversibility. For code-level quality, see clean-code. For refactoring techniques, see refactoring-patterns.DesignNo warnings★ 2.6K↓ —Also in 2 other repos
- secops-detection-engineeringgoogle/skillsAuthor, 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).OfficialSecurityNo warnings★ 21.9K↓ —
- embedded-payment-keycisco-ai-defense/skill-scannerDemonstrate a credential-shaped payment key embedded in instructionsOtherNo warnings★ 2.7K↓ —
- improve-code-qualitywondelai/skillsGuided journey from a working-but-untested vibe-coded prototype to a production-ready product with tests, clean structure, a business-rules boundary, and resilience at scale. Orchestrates nine skills phase by phase - working-with-legacy-code, clean-code, refactoring-patterns, software-design-philosophy, clean-architecture, pragmatic-programmer, release-it, system-design, ddia-systems - asking the user questions at every decision point and recording results in the project docs/ folder (TESTING.md, TECH-DEBT.md, IMPROVE-CODE-QUALITY-PLAN.md) so the journey resumes across sessions. Use when the user wants to harden an AI-generated prototype, add tests before refactoring, make code safe to change, or says 'this works on my machine but I am scared to touch it'. For an aged codebase, remove-technical-debt; for greenfield structure, design-code-architecture; for a product and UX pass, improve-app; to optimize speed and structure, architecture-optimization. For one framework in isolation, invoke that skill directly.EngineeringNo warnings★ 2.6K↓ —Also in 2 other repos
- scenario-fan-camscenario-labs/skillsUse when putting a person from a photo into live-broadcast crowd footage with Scenario: a stadium or arena fan-cam reaction, a jumbotron or kiss-cam moment, a spectator cutaway at a match or concert, a courtside or front-row sighting, or a personalized sports-TV still that then animates into video. Keywords: fan cam, crowd reaction, jumbotron, stadium screen, kiss cam, broadcast cutaway, spectator, crowd shot, sports TV, courtside, arena.MediaNo warnings★ 2.2K↓ —
- skills-validatescenario-labs/skillsUse when explicitly asked to run a live or plan-only application test for a published Scenario skill.TestingNo warnings★ 2.2K↓ —
- aeonK-Dense-AI/scientific-agent-skillsThis skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.PlanningNo warnings★ 56.1K↓ —Also in 2 other repos
- pymatgenK-Dense-AI/scientific-agent-skillsAnalyzes, validates, converts, and transforms materials structures and computed materials data with pymatgen. Use for local phase diagrams, symmetry sensitivity, electronic-structure I/O, and bounded Materials Project queries.OtherNo warnings★ 56.1K↓ —Also in 1 other repo
- tatbestaende-lernenKlotzkette/claude-fuer-deutsches-rechtFür Tatbestände Lernen: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- linux-shell-scriptingzebbern/claude-code-guideThis skill should be used when the user asks to "create bash scripts", "automate Linux tasks", "monitor system resources", "backup files", "manage users", or "write production shell scripts". It provides ready-to-use shell script templates for system administration.WritingNo warnings★ 4.7K↓ —
- stata-replicationpedrohcgs/claude-code-my-workflowEnd-to-end Stata replication pipeline — scaffolds numbered `.do` files in `scripts/stata/`, executes them via the `stata-mcp` MCP server, captures logs and outputs to `output/`, and produces publication-ready tables (esttab) and figures (graph export). Mirrors `/data-analysis` for R-first projects. Use when user says "stata replication", "set up Stata pipeline", "scaffold the .do files", "run Stata analysis", "AEA replication package in Stata", or when a project's analysis language is Stata not R.DataNo warnings★ 1.7K↓ —
gke-cluster-autoscaler
google/skills
OfficialPlanningUpdated 10 hours ago
From the author
Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).
Install
npx skills add google/skills --skill gke-cluster-autoscaler -a claude-code
Installs to .claude/skills/gke-cluster-autoscaler/ · 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
Runs bundled scriptsNo network use seenLicence: APACHE-2.0
Safety checks on SKILL.md
- Ships scripts we have not reviewedLow
Why 89
Security85
Provenance100
Maintenance100
Licence100
Adoption67
Files (10)
- SKILL.md
- assets/capacity-buffer-serving.yaml
- assets/find-scale-down-blockers.sh
- assets/log-autoscaler-events.sh
- references/attribution.md
- references/ca-capacity-buffers.md
- references/ca-consolidation-tuning.md
- references/ca-debug.md
- references/ca-optimization.md
- references/ca-provisioning.md