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...
anti-slop-fix
by b4r7xRuns the anti-slop audit on source code files and automatically applies fixes for detected issues. Invokes the anti-slop analysis first, then fixes each issue in-place. Use when the user wants to clean up AI slop automatically, fix slop patterns, or asks "fix slop", "clean up code", "auto-fix slop", "anti-slop fix".
react-design-patterns
by b4r7xUse when choosing a React component pattern — custom hooks, control props, compound components, headless components, render props, container/presentational, or other architectural patterns. Includes 13 patterns with decision guide and 2025 popularity ranking.
react-senior-guide
by b4r7xUse when writing or reviewing any React code as a comprehensive reference. Routes to 7 specialized React skills covering hooks, patterns, and anti-patterns. Includes cross-cutting principles and an AI code review checklist.
react-usecallback
by b4r7xUse when writing or reviewing useCallback usage in React components. Covers React Compiler impact, when useCallback is justified, and the most common mistake (useCallback without memo).
react-usecontext
by b4r7xUse when working with React Context — deciding whether to use it, optimizing context value to prevent re-renders, or implementing compound components. Covers context value memoization, alternatives, and the compound components pattern.
react-usememo
by b4r7xUse when writing or reviewing useMemo usage in React components. Covers the 4 valid cases, when to skip it, and the practical heuristic for deciding.
humanize-readme
by b4r7xRewrites a README.md to remove AI slop — buzzwords, generic openers, fake enthusiasm, and formulaic structure — replacing it with direct, honest, human-sounding writing. This skill should be used when the user wants to humanize a README, remove AI-generated writing patterns, make documentation sound less like ChatGPT wrote it, or asks to "fix the README", "humanize readme", "remove AI slop", "make it sound human".
anti-slop
by b4r7xAudits source code files for AI-generated slop patterns — unnecessary comments, over-engineering, defensive over-coding, AI voice markers, dead code, type workarounds, and verbose patterns. Outputs a structured report with line references and severity. Use when the user wants to audit code for AI slop, check code quality, find unnecessary comments, detect over-engineering, or asks "audit this", "check for slop", "anti-slop", "review for AI patterns".
code-audit
by b4r7xComprehensive codebase quality audit using parallel agents. Checks DRY, SRP, anti-slop, naming, file organization, type safety, error handling, patterns, dead code, architecture, and reusability. Produces findings report + fix plan for multi-agent execution. Use when the user wants to audit code quality, review architecture, check for smells, run a quality check, or says "audit", "code audit", "quality check", "review codebase".
deep-plan
by b4r7xTakes a rough, unpolished prompt idea and autonomously turns it into an implementation plan. Researches the project deeply, asks clarifying questions, generates a precise internal prompt, then executes it to produce a structured plan with todos. Designed for plan mode. Use when the user gives a vague feature request, rough idea, or "dirty" prompt and wants a ready-to-execute implementation plan — e.g. "plan this", "deep plan", "turn this into a plan", "zaplanuj to", "zrób plan".
expo-bulletproof-structure
by b4r7xBulletproof Expo project structure pattern for React Native apps. Enforces thin routing layer, feature-based modules, dependency direction rules, and Expo Router conventions. Use when creating files, scaffolding features, or making architectural decisions in any Expo/React Native project.
human-commit
by b4r7xGenerates human-like git commit messages based on staged or unstaged changes. Reads git diff, analyzes what changed, and outputs 3 natural commit message options that sound like they were written by a developer — not AI. This skill should be used when the user wants a commit message, asks "what should I write for commit", "generate commit message", "human like commit", "wiadomość do commita", or just asks for help committing.
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.