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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beads
by Cowboy-59Issue tracking and workflow management with beads (bd). Use this skill when you need command reference, workflow examples, session recovery protocols, or dependency management guidance. Invoke when working with beads issues or when the slim hook reference is insufficient.
gitpro
by Cowboy-59ALWAYS use this skill for ALL git operations. NEVER run git commit, git add, git push, git merge, git checkout -b, or git branch -m directly. This skill automates git workflows with conventional commits, automatic changelog updates, semantic version bumping, and consistent formatting. Use when user requests checkpoint, commit, rename branch, merge, sync, pull, refresh, or create new branch operations.
mfing-bible-of-tanstack
by Cowboy-59The definitive TanStack guide covering Start, Router, and Query. Server functions with { data: params } pattern, route integration with loaderDeps, TanStack Query cache management with unified queryOptions, ServerOnly authentication, public API routes, webhooks. Use when creating routes, server functions, query hooks, mutations, implementing authentication, building webhooks, debugging data fetching, fixing loading spinners, or any TanStack Start/Router/Query work.
gitpro
by Cowboy-59ALWAYS use this skill for ALL git operations. NEVER run git commit, git add, git push, git merge, git checkout -b, or git branch -m directly. This skill automates git workflows with conventional commits, automatic changelog updates, semantic version bumping, and consistent formatting. Use when user requests checkpoint, commit, rename branch, merge, or create new branch operations.
code-review
by Cowboy-59Structured code review using parallel reviewer personas tuned for the wxKanban stack (TypeScript, Express, React, Drizzle ORM, PostgreSQL, Pino). Covers correctness, security, compliance, performance, maintainability, and stack conventions. Use before creating a PR or after completing an implementation task.
compound-refresh
by Cowboy-59Review and maintain the learnings knowledge base. Check compound documents against current code, update stale references, merge overlapping docs, and remove obsolete ones. Run periodically or before a compliance audit.
compound
by Cowboy-59Document a recently solved problem, decision, or pattern while context is fresh. Pushes to wxKanban DB (projectdocuments) and writes to specs/{scope}/learnings/. Creates searchable, compliance-ready audit evidence. Use after completing a scope, fixing a hard bug, or making a significant architectural decision.
conversion-analyst
by Cowboy-59Use this skill when reverse-engineering legacy/pre-conversion source code (WinDev/WLanguage, VB6, FoxPro, Delphi, COBOL, PowerBuilder, ColdFusion, Classic ASP, Access VBA, etc.) into Scope-of-Project documents. Triggers when the user asks to analyze, document, scope, or "build a spec from" existing code that is being rewritten in a new stack — including the `/AnalyzeConversion` command. Holds the senior-analyst persona, code-reading heuristics, and BuildScope-style question discipline used to translate legacy implementations into business-language scope docs.
diagnose
by Cowboy-59Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
tdd
by Cowboy-59Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.
triage
by Cowboy-59Triage incoming bugs and feature requests through a state machine. Use when user wants to process a bug report, triage issues, review incoming requests, prepare work for an agent, or manage issue workflow.
wxica
by Cowboy-59wxKanban (ICA) Improve Codebase Architecture — runs a mechanical drift audit (dangling references, cross-package source imports, build-mode coverage, spec-interaction conflicts) and then surfaces deepening opportunities. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, audit recent refactors for missed surfaces, or make a codebase more testable and AI-navigable.
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.