Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
linear-todo-sync
by qdhenryThis skill fetches open tasks assigned to the user from the Linear API and generates a markdown todo list file in the project root. This skill should be used when the user asks about their work items, wants to see what they need to work on, or requests to load/sync their Linear tasks. Requires Python 3.7+, requests, mdutils, and python-dotenv packages. Requires LINEAR_API_KEY in .env file.
elevenlabs-transcribe
by qdhenryTranscribes audio/video files using ElevenLabs Scribe v2 API. Use when transcribing audio files, generating transcripts, or converting speech to text.
cloudflare-manager
by qdhenryComprehensive Cloudflare account management for deploying Workers, KV Storage, R2, Pages, DNS, and Routes. Use when deploying cloudflare services, managing worker containers, configuring KV/R2 storage, or setting up DNS/routing. Requires CLOUDFLARE_API_KEY in .env and Bun runtime with dependencies installed.
bigcommerce-api
by qdhenryBigCommerce API expert for building integrations, apps, headless storefronts, and automations. Full lifecycle - REST APIs, GraphQL Storefront, webhooks, authentication, app development, and multi-storefront. Use when working with BigCommerce platform APIs.
remove-dead-code
by qdhenrySafely identifies and removes dead code in TypeScript/JavaScript projects using multi-agent analysis with automatic backup branches. Use when cleaning up unused exports, orphaned files, dead imports, unreachable functions, or unused dependencies.
setup-agent-tail
by qdhenryConfigure agent-tail log aggregation for the current project. Auto-detects framework (Vite, Next.js, plain Node, monorepo) and sets up CLI runner, browser log plugins, and output destinations. Use when setting up agent-tail, configuring dev server logging, or piping logs for AI agent consumption.
setup-portless
by qdhenrySets up Portless for a project to replace port numbers with stable named .localhost URLs. Use when configuring local development routing, fixing port conflicts, or setting up monorepo dev environments.
audit-env-variables
by qdhenryAnalyze environment variables in JavaScript/TypeScript projects. Identifies unused variables, infers permission scopes, detects specific services (Stripe, AWS, Supabase), and documents code paths. Includes optional cleanup of unused variables with regression detection. Use when auditing .env files, reviewing security, or documenting project configuration.
file-watcher
by qdhenryChokidar-based file watcher that triggers `claude -p` on changes. Useful for automated AI reactions to file changes — design sync, code validation, config regeneration, etc.
extract-video-frames
by qdhenryExtracts frames and timestamped audio segments from video files (GIF, MP4, MOV) at configurable intervals and stores them in a directory with a manifest file. Use when analyzing video content, preparing frames for visual review, extracting audio for transcription, or creating frame+audio sequences for another agent to process.
eslint-migrate-options
by qdhenryGuide for implementing ESLint-to-Biome rule option migrators inside `biome migrate eslint`. Use whenever you add or update a Biome lint rule that has an ESLint source rule with configurable options, need to deserialize plugin-specific ESLint options, or need custom migration logic beyond the auto-generated severity mapping.
generate-storybook
by qdhenryInitializes Storybook for the Foundry codebase, discovers all components and pages, then deploys parallel subagents to generate comprehensive stories with visual, interaction, accessibility, and responsive coverage. Use when setting up Storybook or generating stories for components.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.