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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changelog-generation
by zeroclaw-labsChangelog generation skill for ZeroClaw releases. Use this skill when the user wants to generate a changelog, prepare release notes, or summarize what changed between versions. Trigger on: 'generate changelog', 'changelog for v0.7.x', 'prepare release notes', 'what changed since <tag>', 'write the changelog', 'CHANGELOG-next', 'release notes for the next release'.
wit-breaking-change-check
by zeroclaw-labsClassify WIT interface changes as breaking or non-breaking against frozen version markers. Use this skill when the user wants to check WIT breaking changes, review a WIT diff, verify whether a WIT change is breaking, or run a WIT compat check. Trigger on: 'check WIT breaking changes', 'review WIT diff', 'is this WIT change breaking', 'WIT compat check'.
github-issue-triage
by zeroclaw-labsIssue triage and lifecycle management agent for ZeroClaw. Use this skill whenever the user wants to: triage open issues, close stale/duplicate/fixed issues, apply labels, run a backlog sweep, enforce the RFC stale policy, or handle a specific issue. Trigger on: 'triage issues', 'issue triage', 'sweep issues', 'close stale issues', 'handle issue #N', 'backlog sweep', 'label issues', 'stale pass', 'wont-fix pass', 'issue accounting', 'how many issues', 'backlog health', or any request involving issue lifecycle management for the ZeroClaw project.
zeroclaw
by zeroclaw-labsHelp users operate and interact with their ZeroClaw agent instance — through both the CLI (`zeroclaw` commands) and the REST/WebSocket gateway API. Use this skill whenever the user wants to: send messages to ZeroClaw, manage memory or cron jobs, check system status, configure channels or providers, hit the gateway API, troubleshoot their ZeroClaw setup, build from source, or do anything involving the `zeroclaw` binary or its HTTP endpoints. Trigger this even if the user just says things like 'check my agent status', 'schedule a reminder', 'store this in memory', 'list my cron jobs', 'send a message to my bot', 'set up Telegram', 'build zeroclaw', or 'my bot is broken' — these are all ZeroClaw operations.
github-pr-review-session
by zeroclaw-labsHuman-reviewer co-pilot for ZeroClaw PR reviews. Use this skill when the user wants to review a specific PR as themselves, re-review a PR after author changes, work through a queue of PRs, check what's still open on a PR, or post a formal review verdict. Trigger on: 'review 1234', 'can you look at PR #1234', 're-review 1234', 'check 1234', 'what's still open on 1234', 'go through the queue', 'next PR', 'review the open PRs'. This skill posts reviews in the voice of the active `gh` account holder using gh CLI.
discord-moderator
by zeroclaw-labsAutomated Discord moderation with configurable rules and auto-responses. Enforces server rules, manages disruptive behavior, detects raids, and maintains a healthy community environment. Use when the user needs Discord moderation, rule enforcement, or raid protection.
email-responder
by zeroclaw-labsDraft and send email replies with context-aware tone matching. Reads incoming emails, understands context and tone, and drafts appropriate replies that match the sender's communication style. Use when the user wants to draft email replies, respond to messages, or compose professional correspondence.
inboxapi
by zeroclaw-labsOperate an InboxAPI mailbox from ZeroClaw through the official InboxAPI CLI. Use when the user wants the agent to search mail, read messages or threads, send new email, forward mail, inspect attachments, or reply in-thread while preserving InboxAPI as the source of truth for email delivery and threading.
auto-coder
by zeroclaw-labsAutonomous code generation agent. Reads context, writes code, runs tests. Takes a task description and produces working, production-quality code changes. Use when the user wants to implement features, write code, or make code changes autonomously.
git-assistant
by zeroclaw-labsSmart git operations — interactive rebase, conflict resolution, changelog generation. Helps users perform git workflows safely and efficiently, from everyday operations to complex history manipulation. Use when the user needs help with git commands, merge conflicts, rebasing, or changelog generation.
knowledge-base
by zeroclaw-labsBuild and query a private RAG knowledge base from your documents. Ingests documents, indexes them with embeddings, and answers questions grounded in retrieved context with citations. Use when the user wants to search their own documents, build a knowledge base, or get answers from private content.
multi-agent-router
by zeroclaw-labsRoute tasks to specialized sub-agents based on intent classification. Classifies incoming tasks and dispatches them to the most appropriate agent. Use when the user has a task that needs to be analyzed and routed to the right specialist agent.
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.