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. 79 870 files from 1 769 authors, of which 62 217 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.
Semantic spacing system for Tailwind CSS v4 using block (32px) and tab (4px) units. Use when building UIs with consistent spacing, dashboards, admin panels, design tools, or any interface requiring a harmonious spacing system. Provides patterns for buttons, inputs, cards, toolbars, panels, and layouts.
MUST USE when reviewing ClickHouse schemas, queries, or configurations. Contains 31 rules that MUST be checked before providing recommendations. Always read relevant rule files and cite specific rules in responses.
Guides users through setting up the ClickHouse MCP server connection bundled with this plugin. Use when the user first installs the plugin or has trouble connecting to ClickHouse.
Get an Expo app running on the user's phone from a Tuft machine. Read this at the start of any session where the user asks for an app — including brand-new apps, before scaffolding or picking how to deliver it; assume the deliverable is an installable build on their device, not a web preview, unless they say otherwise, and start that build before feature work so it lands mid-session. Also use for EAS account/Apple/device setup via the Tuft dashboard flow, making development builds with EAS, Expo dev-client iteration, remotely exposing Metro with `tuft host`, generating a dev-client deep link, diagnosing device-only failures with Tuft telemetry, deciding whether a native rebuild is required, and handing a working preview back to the user.
Install a specific Xcode version on a Mac using the xcodes CLI, handing the Apple ID sign-in (password + 2FA) to the user through a shared live terminal. Use when a task needs Xcode/simulators and the machine doesn't have the right version, or the user asks to install/upgrade Xcode.
Plan and review MySQL/InnoDB schema, indexing, query tuning, transactions, and operations. Use when creating or modifying MySQL tables, indexes, or queries; diagnosing slow/locking behavior; planning migrations; or troubleshooting replication and connection issues. Load when using a MySQL database.
PostgreSQL best practices, query optimization, connection troubleshooting, and performance improvement. Load when working with Postgres databases.
Overview and information about Neki, the sharded Postgres product by PlanetScale. Load when working with Neki-related tasks and the need to scale or shard postgres. Load when facing Postgres scaling or sharding issues.
Vitess best practices, query optimization, and connection troubleshooting for PlanetScale Vitess databases. Load when working with Vitess databases, sharding, VSchema configuration, keyspace management, or MySQL scaling issues.
> Investigate a sudden change in web traffic. Use when a user reports a spike or drop in pageviews, sessions, or conversion and wants to find the likely cause (channel, device, referrer, or release) before escalating.
> Create a GitHub pull request for the current branch using the gh CLI. Use when a user wants to open a PR and have the title, body, template, and assignee handled for them, following the repo's conventions and keeping public PRs free of PII.
Compare a validated Next.js reproduction on its reported version and the latest published next@canary under equivalent conditions. Use when determining whether a confirmed vercel/next.js issue still reproduces on canary or appears fixed there.
Turn a validated Next.js reproduction and canary verification into the smallest appropriate regression test in vercel/next.js. Use when a confirmed issue needs a focused test that proves the reported behavior and fits the existing Next.js test suite.
Implement and validate the smallest correct fix for a confirmed issue in vercel/next.js after reproduction, canary verification, and regression-test work. Use when a focused failing Next.js regression test exists and the product code must be corrected without weakening that test.
Locate the introduction or fix boundary for a validated Next.js regression using releases or commits and an executable reproduction. Use after reported-version-versus-canary verification when a vercel/next.js maintainer needs the last unchanged and first changed version or commit.
Reproduce and validate a reported Next.js issue using real code and runtime evidence. Use when investigating whether a vercel/next.js bug report is reproducible, repairing an incomplete reproduction, or creating the smallest standalone reproduction when none is provided.
>- Convert existing projects into Apify Actors - serverless cloud programs. Actorize JavaScript/TypeScript (SDK with Actor.init/exit), Python (async context manager), or any language (CLI wrapper). Use when migrating code to Apify, wrapping CLI tools as Actors, or adding Actor SDK to existing projects.
>- Universal AI-powered web scraper for any platform. Scrape data from Instagram, Facebook, TikTok, YouTube, LinkedIn, X/Twitter, Google Maps, Google Search, Google Trends, Reddit, Airbnb, Yelp, and 15+ more platforms. Use for lead generation, brand monitoring, competitor analysis, influencer discovery, trend research, content analytics, audience analysis, review analysis, SEO intelligence, recruitment, or any data extraction task.
>- Integrate Apify into an existing JavaScript/TypeScript or Python application using the apify-client package. Use when adding web scraping, automation, or data extraction capabilities to an existing app via the Apify API.
>- Develop, debug, and deploy Apify Actors - serverless cloud programs for web scraping, automation, and data processing. Use when creating new Actors, modifying existing ones, or troubleshooting Actor code.
>- Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.
Build, run, debug, and deploy Apify Actors. Use when the user wants to create an Actor, mentions apify create / apify run / apify push, or is debugging an Actor's input schema, storages, or deployment.
Design guide for building Apify integration plugins that expose Apify Actors to an agent/host platform. Use when designing, building, or reviewing a new Apify integration plugin — covers the single universal `apify` tool shape, the discover/start/collect primitives, the two-phase async (start/collect) pattern, batching, external-content wrapping against prompt injection, secret normalization, base-URL allowlisting, the config surface, and the setup/status CLI. Triggers: "new Apify integration", "Apify plugin", "wrap Apify Actors", "agent tool for Apify", "integration plugin design".
Validation harness for the apify-pi-plugin. Build, install, configure, and test the Apify plugin against a running pi agent. Uses pi -p one-shot mode and tmux for interactive commands. Used by the validator agent after the coder agent completes implementation. Triggers: "validate apify plugin", "test pi plugin", "verify apify integration".
Universal AI-powered web scraper for any platform. Scrape data from Instagram, Facebook, TikTok, YouTube, LinkedIn, X/Twitter, Google Maps, Google Search, Google Trends, Reddit, Airbnb, Yelp, and 15+ more platforms. Use for lead generation, brand monitoring, competitor analysis, influencer discovery, trend research, content analytics, audience analysis, review analysis, SEO intelligence, recruitment, or any data extraction task.
Integrate Apify into an existing JavaScript/TypeScript or Python application using the apify-client package. Use when adding web scraping, automation, or data extraction capabilities to an existing app via the Apify API.
Convert existing projects into Apify Actors - serverless cloud programs. Actorize JavaScript/TypeScript (SDK with Actor.init/exit), Python (async context manager), or any language (CLI wrapper). Use when migrating code to Apify, wrapping CLI tools as Actors, or adding Actor SDK to existing projects.
Develop, debug, and deploy Apify Actors - serverless cloud programs for web scraping, automation, and data processing. Use when creating new Actors, modifying existing ones, or troubleshooting Actor code.
Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.
Clerk webhooks for real-time events and data syncing. Always output complete, copy-paste-ready webhook handlers with verifyWebhook(req) verification. Listen for user creation, updates, deletion, and organization events. Build event-driven features like database sync, notifications, integrations.
Add Clerk authentication to any project by following the official quickstart guides.
Advanced Next.js patterns - middleware, Server Actions, caching with Clerk.
Clerk Organizations for B2B SaaS - create multi-tenant apps with org switching, role-based access, verified domains, and enterprise SSO. Use for team workspaces, RBAC, org-based routing, member management.
Clerk Backend REST API explorer and executor. Browse tags, inspect endpoint schemas, and execute authenticated requests. Use when listing users, managing organizations, or calling any Clerk API endpoint.
Clerk authentication router. Use when user asks about adding authentication, setting up Clerk, custom sign-in flows, Swift or native iOS auth, native Android auth, Next.js patterns, React patterns, Vue patterns, Nuxt patterns, Astro patterns, TanStack Start patterns, Expo patterns, React Router patterns, Chrome Extension patterns, organizations, syncing users, or testing. Automatically routes to the specific skill based on their task.
>- Guides Stripe integration decisions — API selection (Checkout Sessions vs PaymentIntents), Connect platform setup (Accounts v2, controller properties), billing/subscriptions, Treasury financial accounts, integration surfaces (Checkout, Payment Element), migrating from deprecated Stripe APIs, and security best practices (API key management, restricted keys, webhooks, OAuth). Use when building, modifying, or reviewing any Stripe integration — including accepting payments, building marketplaces, integrating Stripe, processing payments, setting up subscriptions, creating connected accounts, or implementing secure key handling.
Write, review, or improve SwiftUI code following best practices for state management, view composition, performance, macOS-specific APIs, and iOS 26+ Liquid Glass adoption. Use when building new SwiftUI features, refactoring existing views, reviewing code quality, or adopting modern SwiftUI patterns.
Use when building Android UIs with Jetpack Compose, managing state with remember/mutableStateOf, or implementing declarative UI patterns.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Guardrails for AI agents editing @posthog/warlock, adding or porting rules, proposing new categories, or reviewing Warlock PRs. Load when working in /warlock or on a PR that touches the Warlock source, rules, or docs.
Preview or publish the current Codex coding session as readable Markdown in a configured GitHub repository. Use when the user asks to share, export, publish, or save a Codex session or transcript.
Write and review unsafe Rust `# Safety` documentation and safety comments (e.g., `// safety:` or `/// safety:` in any capitalization) as proof obligations grounded in the Rust Reference, standard library documentation, trusted opted-in dependency contracts, and explicit project invariants.
Active, Socratic teacher for the LangChain Academy Deep Agents course — drives the student through the curriculum with a calibrated interaction density, explain-then-check dialogue at load-bearing moments, and misconception-targeted questioning
Graph database skills for Apache AGE on PostgreSQL. Covers the full lifecycle: deriving an ontology from structured or unstructured data with a human feedback loop, building the graph, and querying it with openCypher, natural language to Cypher, and graph augmented retrieval.
Expert PostgreSQL skills with intelligent routing. Covers both generic PostgreSQL and Azure Database for PostgreSQL.
|- Use this skill when you need to set up validation for AI agent skills in a Dart project for the first time. Adds the linter as a dev_dependency, creates a configuration file, and generates a baseline for legacy repos.
> Validates an in-progress PR or feature branch of skills_lint against known downstream ecosystem consumers. Use when assessing breaking changes across external repositories during PR evaluation, testing migrations against the changelog, or determining necessary backwards compatibility shims.
> Instructions for adding a new validation rule and CLI flag to skills_lint. Use this skill when asked to create a new rule that validates aspects of skills (like frontmatter metadata).
|- Use this skill when you need to validate AI agent skills with skills_lint — running the linter, interpreting failures, fixing violations, and authoring custom rules.
Write and organize unit tests for functions, methods, and classes using `package:test`. Use when creating new logic or fixing bugs to ensure code remains correct and regression-free.
Mandatory checks to run before completing any task that touches md files or dart code in this repository.
Use when implementing any feature or bugfix, before writing implementation code
> How to integrate, update, and configure the skills_lint validation tool within a repository. Make sure to use this skill whenever the user asks to update skills_lint, configure skills validation tests, fix skills linter dependency drifts, verify repository state before editing, optimize lint rules execution, or draft pull request submission commands.
>- De-risk the risky assumptions in a vision by running spikes. Given the revisioner risky-assumptions.yaml, this designs a spike for each open assumption (literature/evidence search, hands-on trial, or a fresh empirical investigation), runs the spikes in parallel isolated workspaces, captures every artifact under .amplifier/revisioner/data/<id>/, and updates each assumption's signed confidence from the evidence gathered. Use when the user wants to validate/invalidate assumptions, "run spikes", gather evidence, or "de-risk" a plan. It never touches the risk (stakes) axis — only confidence.
>- which risky assumptions need the user's attention right now (amend the vision, or unblock a spike), and a drill-down into the evidence behind every verdict. Reads .amplifier/revisioner/ (risky-assumptions.yaml plus each data/<id>/ spike directory), emits a single revision-state.json, and instantiates a bundled React template the user runs with pnpm or npm. Use this whenever someone wants to SEE the state of the "visualize the assumptions", "build a dashboard for this", "is there a UI for the revisioner", "I want to click into the evidence", or after a de-risking pass when a text report is too dense to reason about. Also use it when the user wants to share the current state with someone who will not run the tool themselves.
>- Check in on where a vision stands once its risky assumptions have been found and are being de-risked. Reads the revisioner risky-assumptions.yaml plus the live per-spike status.json files, classifies every assumption (holds / fails / all-clear when the high-risk assumptions hold, ask the user to supply what blocked spikes need (then resume de-risking), or propose a vision update/pivot when evidence has invalidated a high-risk assumption. Use when the user asks "where do we stand", "check in on the vision", "what's blocked", "are we de-risked yet", or after de-risking spikes have run. It reads state and steers; it never sets risk or confidence itself.
>- Surface the risky, unvalidated assumptions hidden inside a vision, product plan, proposal, solution write-up, spec, or design doc, and record any NEW ones in the project's risky-assumptions file. Use this whenever the user has a vision/plan/ proposal artifact and wants to know what could be wrong, what is being taken for granted, what is risky or shaky, or what the author has not fully thought through — or asks to "find/surface/extract risky assumptions", "what are we assuming", "poke holes in this", "pre-mortem this", or to run the ReVisioner assumption pass. Examines the doc from feasibility, viability, and desirability angles (technical, practical, cost, operational, adoption) and appends only assumptions not already captured, without ever changing existing risk levels. This skill only FINDS and records assumptions; de-risking them happens in a separate skill.
Claude Skills — questions
Answers built from the skills we actually parsed.