Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 86 696 files from 2 113 authors, of which 68 413 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
> Connect an arbitrary REST API to the PostHog data warehouse as a Custom source by authoring a JSON manifest, with no per-source code. Use when the user points at an API that has no built-in PostHog connector — "import data from this REST API", "sync my internal API", "connect this API from its docs", "build a custom data warehouse source" — and gives a docs URL or a natural-language description of the endpoints. Walks through drafting the RESTAPIConfig manifest (auth — bearer, API key, HTTP basic, or OAuth2 client credentials / refresh token — pagination, record path, incremental cursor, parent/child fan-out), validating it, test-reading live rows to verify the field mappings, and creating the source. If the API already has a native PostHog connector, use setting-up-a-data-warehouse-source instead — this skill checks the connector registry first and only handles APIs with no native connector.
> Signals scout for the PostHog Conversations (support inbox) product. Watches the `$conversation_*` ticket-lifecycle events for support-delivery regressions — SLA breach-rate steps, first-response latency blowouts, backlog inflow-vs-resolution imbalance, and channel / assignment concentration — and files each dated regression as a report. Complements the per-ticket product-feedback signals the emission pipeline already fires; does not re-surface individual ticket content.
> Signals scout for PostHog distributed tracing (APM / OpenTelemetry spans). Watches RED metrics per (service, operation) — error rate, p95 latency, request volume — for regressions, new error signatures, and traffic cliffs, and files each validated regression as a report in the inbox.
> Focused Signals scout for PostHog projects importing external data into the warehouse. Watches the import side — external data sources, per-table sync schemas, webhook push channels, and materialized views — for the moments an import quietly stops keeping its staleness behind a green Completed status, a broken webhook push channel, a row-volume cliff, and failed materialized views. When armed imports are healthy, switches to the time and read-bytes concentrated on warehouse tables or repeated query shapes, filing materialization candidates and unused matviews as P3 suggestions. Files each validated import contradiction as an inbox report; otherwise writes durable memory and closes out empty.
> Signals scout for PostHog MCP tool calls. Watches $mcp_tool_call telemetry for tools that need improvement — high, broad-reach failure rates, retry/hammering that betrays a confusing schema, slow or context-bloating responses — groups problem tools by $mcp_tool_category (the owning product team) and files one report per problem category listing that category's problem tools each with a fix suggestion; falls back to one report per tool where category coverage is absent. Immediately-actionable reports carry a fix-loop metric (measurement query, baseline, goal) so the auto-started implementation task iterates until the number moves. Otherwise writes durable memory and closes out empty. Adapts to which fields the project actually captures.
> Signals scout over a project's own configured insight alerts. Reads each alert's recent firing history and files a report for the firings a human likely missed — especially ones the standard notification path stayed silent on.
> Signals scout for core product-analytics flows — funnels, retention, lifecycle, stickiness, and paths. Watches the team's saved flows for a derived-rate regression (conversion or retention sliding) while entrants hold, and files it as a report in the inbox.
> Skill-hygiene scout for the team's PostHog skills store, read entirely via the MCP skill tools. vague descriptions, bloated bodies, dead bundled-file links, kitchen-sink scope, committed secrets. Files each non-compliant skill as a report in the inbox, with the copy-ready fix inside.
> (runs failing, clustered by repository and error class, and retry storms) every run, and on a slower rotation demand (recurring asks across human-authored tasks that point at a product gap). Skips the scout fleet's own run rows.
> Focused Signals scout for PostHog projects capturing Core Web Vitals (`$web_vitals`). Watches each page's p75 LCP / INP / CLS / FCP against the absolute Google thresholds poor band, pages crossing a band boundary after a deploy, and sharp in-band regressions. Reads the historical trajectory — not just the moment a value changes — so a page that is steadily slow surfaces even when nothing moved today. Every finding carries a metric-specific cause hypothesis and a concrete remediation, filed as a report in the inbox only above the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.
> Set up the local dev environment, seed data, and API keys to test the staff-only managed migrations MCP tools (managed-migrations-support-list, managed-migrations-support-get) end to end. Use when testing batch import support tooling, debugging MCP tool responses or discovery (tools not appearing), or verifying the support API before deploying.
> Converts engineering analytics (PR / CI) data into saved PostHog insights, dashboards, and subscriptions, and explains what data the product reads so it can be queried directly with SQL. The engineering analytics dashboard and MCP tools run curated HogQL privately over per-team GitHub warehouse tables; this skill teaches discovering those tables via engineering-analytics-sources, replicating the curated column semantics in HogQL, reading the exposed engineering_analytics_* warehouse views where product logic is involved (CI cost, fingerprinted failure lines, commit attribution), saving the query with insight-create, and scheduling delivery with subscriptions-create. Use when asked to "save this as an insight", "put CI health / merge times on a dashboard", "email me PR throughput weekly", "chart CI cost", "track time to first review", "subscribe to these numbers", "alert on CI success rate", or "what data/tables/views does engineering analytics read". For ad-hoc CI and merge questions use diagnosing-ci-and-merge-bottlenecks; to investigate one specific CI failure use investigating-ci-failures.
Write Streamlit app source code that runs well in a PostHog sandbox — the posthog_apps.query() bridge for reading PostHog data, the packages baked into the sandbox image, caching and session state across Streamlit reruns, layout and chart patterns, and single-file app.py structure. Use when authoring or debugging the Python source of a PostHog Streamlit app, when a query inside an app fails, or when asked to "write a streamlit app that shows PostHog data".
Extract design primitives from a public website and generate starter token files for your project.
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' 'consumer behavior,' 'anchoring,' 'social proof,' 'scarcity,' 'loss aversion,' 'framing,' or 'nudge.' Use this whenever someone wants to understand or leverage how people think and make decisions in a marketing context.
| When reviewing, optimizing, or inheriting an LLM agent, systematically identify what's actually load-bearing vs. cruft. Instruments usage, ablates components, without degrading quality?" The goal is the leanest agent that still works.
Generate AI-ready metadata for design system components to enable intelligent UI generation. Analyzes component structure and generates structured metadata that helps AI understand when and how to use components correctly. Useful for teams building AI-consumable design systems.
Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
Build scalable design systems with design tokens, theming infrastructure, and component architecture patterns. Use when creating design tokens, implementing theme switching, building component libraries, or establishing design system foundations.
Use when building Go applications requiring concurrent programming, microservices architecture, or high-performance systems. Invoke for goroutines, channels, Go generics, gRPC integration.
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.
When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," or "building many pages for SEO." For auditing existing SEO issues, see seo-audit.
> (1) writing new Rust code or functions, (2) reviewing or refactoring existing Rust code, (3) deciding between borrowing vs cloning or ownership patterns, (4) implementing error handling with Result types, (5) optimizing Rust code for performance, (6) writing tests or documentation for Rust projects.
Implement SAFe methodology in Jira. Use when creating Epics, Features, Stories with proper hierarchy, acceptance criteria, and parent-child linking.
Orchestrate Jira workflows end-to-end. Use when building stories with approvals, transitioning items through lifecycle states, or syncing task completion with Jira.
HSK4級レベルから流暢さを目指す学習者向け。中国語表現の使用場面・自然さを分析し、作文を「ネイティブらしい流暢な表現」に改善。bilibili等のコンテンツ理解とネイティブとの会話をサポート。実際の用例をWeb検索で提示
Next.js 15 애플리케이션을 위한 프론트엔드 개발 가이드라인. React 19, TypeScript, Shadcn/ui, Tailwind CSS를 사용한 모던 패턴. Server Components, Client Components, App Router, 파일 구조, Shadcn/ui 컴포넌트, 성능 최적화, TypeScript 모범 사례 포함. 컴포넌트, 페이지, 기능 생성, 데이터 페칭, 스타일링, 라우팅, 프론트엔드 코드 작업 시 사용.
Claude Code 스킬, 훅, 에이전트, 명령어를 생성하고 관리하기 위한 메타 스킬. 새 스킬 생성, 스킬 트리거 설정, 훅 설정, Claude Code 인프라 관리 시 사용.
Discover and extract sitemaps from any website using SitemapKit. Use this skill whenever the user wants to find pages on a website, get a list of URLs from a domain, audit a site's structure, crawl a sitemap, check what pages exist on a site, or do anything involving sitemaps or site URL discovery — even if they don't explicitly say "sitemap". Requires the sitemapkit MCP server configured with a valid SITEMAPKIT_API_KEY.
GitHubのプルリクエスト（PR）を作成する際に使用します。変更のコミット、プッシュ、PR作成を含む完全なワークフローを日本語で実行します。「PRを作って」「プルリクエストを作成」「pull requestを作成」などのリクエストで自動的に起動します。
Generate an SVG of a user-requested image or scene
Security intelligence for code analysis. Detects SQL injection, XSS, CSRF, authentication issues, crypto failures, and more. Actions: scan, analyze, fix, audit, check, review, secure, validate, sanitize, protect. Languages: JavaScript, TypeScript, Python, PHP, Java, Go, Ruby. Frameworks: Express, Django, Flask, Laravel, Spring, Rails. Vulnerabilities: SQL injection, XSS, CSRF, authentication bypass, authorization issues, command injection, path traversal, insecure deserialization, weak crypto, sensitive data exposure. Topics: input validation, output encoding, parameterized queries, password hashing, session management, CORS, CSP, security headers, rate limiting, dependency scanning.
Extract readable transcripts from Claude Code and Codex CLI session JSONL files
Creates, updates, or reviews a project's gen-env command for running multiple isolated instances on localhost. Handles instance identity, port allocation, data isolation, browser state separation, and cleanup.
Patterns for running long-lived processes in tmux. Use when starting dev servers, watchers, tilt, or any process expected to outlive the conversation.
Fetches Zig language and standard library documentation via CLI. Activates when needing Zig API details, std lib function signatures, or language reference content that isn't covered in zig-best-practices.
Troubleshoot Claude Code extensions and behavior. Triggers on: debug, troubleshoot, not working, skill not loading, hook not running, agent not found.
Run Claude Code programmatically without interactive UI. Triggers on: headless, CLI automation, --print, output-format, stream-json, CI/CD, scripting.
Claude Code hook system for pre/post tool execution. Triggers on: hooks, PreToolUse, PostToolUse, hook script, tool validation, audit logging.
Enhanced git operations using lazygit, gh (GitHub CLI), and delta. Triggers on: stage changes, create PR, review PR, check issues, git diff, commit interactively, GitHub operations, rebase, stash, bisect.
Model Context Protocol (MCP) server patterns for building integrations with Claude Code. Triggers on: mcp server, model context protocol, tool handler, mcp resource, mcp tool.
Python asyncio patterns for concurrent programming. Triggers on: asyncio, async, await, coroutine, gather, semaphore, TaskGroup, event loop, aiohttp, concurrent.
CLI application patterns for Python. Triggers on: cli, command line, typer, click, argparse, terminal, rich, console, terminal ui.
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.
FastAPI web framework patterns. Triggers on: fastapi, api endpoint, dependency injection, pydantic model, openapi, swagger, starlette, async api, rest api, uvicorn.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.
Python type hints and type safety patterns. Triggers on: type hints, typing, TypeVar, Generic, Protocol, mypy, pyright, type annotation, overload, TypedDict.
Quick reference for RESTful API design patterns, HTTP semantics, caching, and rate limiting. Triggers on: rest api, http methods, status codes, api design, endpoint design, api versioning, rate limiting, caching headers.
Security patterns and OWASP guidelines. Triggers on: security review, OWASP, XSS, SQL injection, CSRF, authentication, authorization, secrets management, input validation, secure coding.
Quick reference for common SQL patterns, CTEs, window functions, and indexing strategies. Triggers on: sql patterns, cte example, window functions, sql join, index strategy, pagination sql.
Quick reference for Tailwind CSS utility patterns, responsive design, and configuration. Triggers on: tailwind, utility classes, responsive design, tailwind config, dark mode css, tw classes.
Cross-language testing strategies and patterns. Triggers on: test pyramid, unit test, integration test, e2e test, TDD, BDD, test coverage, mocking strategy, test doubles, test isolation.
Create or update numbered task docs using repo context, DOC_TEMPLATE.md, and naming conventions.
Create charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.
Persistent project documentation system that maintains context across sessions. Creates structured Memory Bank files to preserve project knowledge, decisions, and progress.
Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
Python FastAPI development with uv package manager, modular project structure, SQLAlchemy ORM, and production-ready patterns.
Claude Skills — questions
Answers built from the skills we actually parsed.