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.
49,361 skills from 2,397 repositories · updated 40 minutes ago
49,361 skills
- writing-workflowinkeep/open-knowledgeHow to work in a Writing Pipeline project (the `writing-pipeline` starter pack): a three-stage drafting flow, ideas → drafts → published. Read when the project has these folders, OR when asked to capture an idea, start or revise a draft, review a piece before it ships, or publish it. Carries the stage flow and review behaviors so that guidance does not live inside template bodies or folder descriptions. Complements the platform `open-knowledge` skill; does not replace it.WritingNo warnings★ 4.8K↓ —
- anndataK-Dense-AI/scientific-agent-skillsHandles annotated matrices in single-cell analysis, .h5ad and Zarr files, and integration with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.OtherNo warnings★ 56.2K↓ —Also in 2 other repos
- speak-summarygithub/awesome-copilotConvert text, markdown, or a summary produced by another skill into a listenable MP3 using local CPU-only neural text-to-speech. Rewrites written prose for the ear before synthesising. Use when the user asks to "read this out", "turn this into audio", "make an MP3", "I want to listen to this", "podcast version", or wants a spoken digest for a commute or breakfast.ProductivityNo warnings★ 40.8K↓ —
- gentle-ai-rdd-defect-workflowGentleman-Programming/gentle-shellTrigger: RDD, receipt-driven development, review authority, receipt/lineage, correction/recovery, delivery gate/kill switch, bounded review defects. Guide work.ProductivityNo warnings★ 1.7K↓ —
- copilot-pr-autopilotgithub/awesome-copilotCopilot left 14 review comments on your PR — half are nits. Hours of fix → reply → resolve → re-request, and each round lands MORE comments. This skill runs loop engineering: auto-triggers Copilot Code Review via GraphQL (no @copilot mention), triages every open thread (Copilot, humans, advanced-security) with a fix / decline / escalate rubric, dispatches parallel fix sub-agents that obey the repo build/test/lint conventions, commits per iteration, replies+resolves citing the pushed SHA, then re-triggers until HEAD is reviewed with zero threads awaiting the agent's reply (remaining open threads are explicit hand-offs to the human — escalated declines, design tradeoffs). You merge a clean PR; the bot runs it. Trigger phrases: "address copilot comments", "run a copilot review loop", "fix this PR", "iterate on copilot feedback". Repo-agnostic, gh CLI + PowerShell. Full autopilot needs repo Triage/Write; external PR authors get single-iteration mode plus manual re-trigger (UI 🔄 or substantive-commit push).SecurityNo warnings★ 40.8K↓ —
- 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★ 49.2K↓ —
- setup-timescaledb-hypertablestimescale/pg-aiguideUse this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.DataNo warnings★ 1.9K↓ —
- gegen-gelb-gleichtKlotzkette/claude-fuer-deutsches-rechtFür Gegen: Dokumentenmatrix, Lückenliste und Nachforderung: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- unterlagen-lueckenKlotzkette/claude-fuer-deutsches-rechtFür Unterlagen und Lücken: ordnet Akte, Belege und Lücken; Ergebnis: Dokumentenmatrix mit Nachforderungsliste. Fachgebiet: Produkthaftung und Produktrecht.OtherNo warnings★ 1.8K↓ —
- sympysickn33/agentic-awesome-skillsSymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations.EngineeringNo warnings★ 49.2K↓ —
- design-code-architecturewondelai/skillsGuided journey from an app idea to a deliberate architecture: boundaries, domain model, data decisions, and resilience, making only the expensive-to-reverse decisions and deferring the rest. Orchestrates eight skills phase by phase - clean-architecture, domain-driven-design, system-design, ddia-systems, software-design-philosophy, release-it, pragmatic-programmer, 37signals-way - asking the user questions at every decision point and recording results in the project docs/ folder (ARCHITECTURE.md, DESIGN-CODE-ARCHITECTURE-PLAN.md) so the journey resumes across sessions. Use when the user wants to design a new app's architecture, choose boundaries and a domain model, decide monolith versus microservices, or says 'how should I structure this app'. If a codebase already exists, use remove-technical-debt (aged), improve-code-quality (fresh prototype), or architecture-optimization (slow but working); if unvalidated, run create-business or create-app first. For one framework in isolation, invoke that skill directly.DesignNo warnings★ 2.6K↓ —Also in 2 other repos
- neuheit-pruefenKlotzkette/claude-fuer-deutsches-rechtFür neuheit-prüfen: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Schnittstellenkarte mit Zuständigkeits- und Nachweisfragen.OtherNo warnings★ 1.8K↓ —
- pufferlibK-Dense-AI/scientific-agent-skillsVersion-aware guidance for PufferLib reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review. Covers the native 5.0 build and environment API, published 3.0.0 Gymnasium/PettingZoo adaptation, and a pinned historical 4.0 profile.EngineeringNo warnings★ 56.2K↓ —Also in 1 other repo
- evomapLeoYeAI/agent-master-skillsConnect to the EvoMap collaborative evolution marketplace. Publish Gene+Capsule bundles, fetch promoted assets, claim bounty tasks, and earn credits via the GEP-A2A protocol. Use when the user mentions EvoMap, evolution assets, A2A protocol, capsule publishing, or agent marketplace.ProductivityNo warnings★ 2.2K↓ —
- dsh-docNewqufei1993/skills-hub创建或维护工程文档、设计决策、事故复盘、归档提案或产品迁移指南时使用,保留事实依据、实际备选方案和准确状态。OtherNo warnings★ 1.9K↓ —
- depmapK-Dense-AI/scientific-agent-skillsRetrieves and analyzes Cancer Dependency Map (DepMap) release data, including CRISPR Chronos gene effects, cancer model annotations, omics biomarkers, and PRISM drug sensitivity. Supports cancer-selective dependency, co-essentiality, and candidate synthetic-lethality analyses with release-aware identifiers and statistical checks.OtherNo warnings★ 56.2K↓ —Also in 2 other repos
- understand-knowledgeEgonex-AI/Understand-AnythingAnalyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.OtherNo warnings★ 90.9K↓ —
- expo-uisickn33/agentic-awesome-skillsBuild native UI with the @expo/ui package: real SwiftUI on iOS and Jetpack Compose on Android rendered from React in an Expo or React Native app.DesignNo warnings★ 49.2K↓ —
- allgemeinKlotzkette/claude-fuer-deutsches-rechtFür Normenkontrollrat-NKR — Einstieg und Routing: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- scenario-caption-studioscenario-labs/skillsUse when a video needs its spoken words on screen through Scenario via MCP: burned-in styled captions for a TikTok, Reels, or Shorts cut, ad captions for sound-off feeds, YouTube subtitles, an SRT sidecar, transcription of a clip's audio, captions translated into another language, karaoke or word-by-word styles, or restyling and correcting an existing transcript. Keywords: captions, subtitles, SRT, transcribe, karaoke, word-by-word, burn in, closed captions, translate video, caption style.MediaNo warnings★ 2.8K↓ —
- skills-validatescenario-labs/skillsUse when explicitly asked to run a live or plan-only application test for a published Scenario skill.TestingNo warnings★ 2.8K↓ —
- astropysickn33/agentic-awesome-skillsAstropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.DataNo warnings★ 49.2K↓ —
- scenario-thumbnailsscenario-labs/skillsUse when creating or revising finished video thumbnails or cover images with Scenario: game trailers, boss reveals, patch updates, devlogs, or community videos needing readable composition, exact headline text and consistent characters. Not for title ideas or critique alone, in-video title cards, or a generic image without a thumbnail deliverable.MediaNo warnings★ 2.8K↓ —
- deep-auditpedrohcgs/claude-code-my-workflowComprehensive adversarial audit of a theory, proof, math/econ paper, codebase, or set of claims — decompose into components, fan out independent skeptics that must return CONCRETE defects, adjudicate every finding with a separate judge, fix all confirmed defects, then re-verify. Use when correctness must be bulletproof and single-pass or round-by-round review is too slow and too shallow. Invoke for "audit this rigorously", "find ALL the bugs/gaps", "make this rock solid", "converge faster on correctness".ScienceNo warnings★ 1.7K↓ —
secops-detection-engineering
google/skills
From the author
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).
Install
npx skills add google/skills --skill secops-detection-engineering -a claude-code
Installs to .claude/skills/secops-detection-engineering/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
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Files (4)
- SKILL.md
- references/attribution.md
- references/yaral_compiler_and_udm_guardrails.md
- scripts/validate_yaral_rule.py