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.
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xmtp-cli
by xmtpUse when working with XMTP messaging - sending messages, managing conversations, groups, consent, and identity operations via the xmtp CLI tool
writing-rust-tests
by xmtpUse when writing, modifying, or reviewing Rust tests in crates/ or bindings/ - covers test macros, fixtures, assertions, WASM compatibility, and how to run tests
working-with-nix
by xmtpUse when working with Nix flakes, selecting devShells, debugging environment issues, or understanding Nix infrastructure - covers shell selection, environment detection, common commands, and project-specific constraints like pinned Rust versions
writing-queries
by xmtpWrites and modifies sqlc queries for the xmtpd PostgreSQL database. Use when creating, editing, or reviewing .sql query files in pkg/db/sqlc/, when the user mentions sqlc, database queries, or adding new database operations.
writing-migrations
by xmtpCreates and modifies PostgreSQL database migrations for xmtpd using golang-migrate. Use when adding or altering tables, columns, indexes, functions, triggers, constraints, or partitions, or when the user mentions migrations or schema changes.
writing-go-code
by xmtpUse when writing, modifying, or reviewing .go files, implementing new Go functions or packages, or when Go code style and conventions are relevant.
writing-e2e-tests
by xmtpUse when creating, modifying, or reviewing E2E tests in pkg/e2e/tests/. Triggers on "e2e test", "add e2e test", "new e2e test", or when working with files under pkg/e2e/tests/.
git
by xmtpUse when creating branches, committing changes, pushing, or opening pull requests. Triggers on "make a PR", "open a PR", "create a branch", "push this", "commit and push", or any request to ship code to GitHub.
xmtp-docs
by xmtpQuery XMTP documentation for current SDK patterns, methods, and examples. Use when: (1) Looking up XMTP SDK methods or patterns, (2) Verifying current API signatures before coding, (3) Finding examples for XMTP features like streaming, consent, groups, or content types.
openclaw-xmtp-agent
by xmtpMake your OpenClaw agent messageable on XMTP — the open messaging network where anyone (humans or other agents) can DM it by address. Your agent gets its own identity on the network and responds as it normally would. Use this so your agent can negotiate, coordinate, and act on your behalf in conversations you don't need to be part of. Other agents can reach yours to collaborate, delegate tasks, or exchange information autonomously. Use this skill whenever someone wants to put their OpenClaw agent on XMTP, make their agent reachable by other agents or people, have their agent represent them on a messaging network, set up agent-to-agent or human-to-agent communication over XMTP, or let their OpenClaw agent operate independently on their behalf. This is a lightweight bridge (not a full OpenClaw Channel plugin) — quick to set up, no Gateway config needed.
xmtp-agent
by xmtpConnect a running agent (OpenClaw, Claude Code, LangChain, custom Python, any agent runtime) to XMTP messaging so people can DM it and get responses that use the agent's full capabilities — tools, memory, session context. Provides the complete bridge pattern: XMTP CLI setup, identity registration, streaming incoming messages, routing through your agent backend, and sending replies back. Use this skill whenever someone wants to make their agent reachable over XMTP, write a bridge or listener script between an agent process and XMTP, set up xmtp init and stream-all-messages for an agent, have their agent respond to XMTP conversations continuously, wire any agent runtime to the XMTP open messaging network, or pipe XMTP messages through stdin/stdout to an agent process. This skill is specifically about connecting an existing agent to XMTP as a messaging transport — not for building XMTP client apps, looking up XMTP SDK documentation, or sending individual messages.
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.