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...
mcp-scaffold
by timothywarner-orgScaffold production-ready Python MCP servers using FastMCP. Use when creating new MCP servers, initializing MCP projects, generating server boilerplate, or setting up MCP development environments. Supports all MCP primitives (tools, resources, prompts) with Pydantic validation, async patterns, and proper project structure.
review-changes
by timothywarner-orgReview uncommitted local changes in the current git working tree for bugs, smells, missing tests, and CLAUDE.md voice violations. Use when the user asks to "review my changes", "look at my diff", "check my staged changes", "code review my working tree", or wants a sanity check before committing or opening a PR. Do NOT use for already-open pull requests on GitHub (that's the separate `review` command).
claude-md-audit
by timothywarner-orgAudit the CLAUDE.md hierarchy in a repo for drift between what each CLAUDE.md claims and what's actually on disk. Validates that referenced file paths still exist, flags voice violations (em dashes, "ask" as a noun), and checks the ground-truth facts block for stale tokens (MCP spec date, transports, model lineup). Use when the user asks to audit, validate, check, or verify CLAUDE.md files, says "is my CLAUDE.md still accurate", flags possible doc drift, or wants to sanity-check the docs before a course recording or release.
gh300-study-planner
by timothywarner-orgGenerates a personalized GH-300 study plan based on the user's self-assessed confidence across exam skill areas, prioritizing weak areas with estimated hours and Microsoft Learn links. Use when the user asks for a study plan, is unsure what to study, or wants exam prep guidance.
gh300-item-creator
by timothywarner-orgGenerate GH-300 practice questions that feel like the real exam without copying it. Every item is grounded in current Microsoft Learn content, uses modern Copilot terminology, and follows Microsoft-style exam item rules (scenario-first, plausible distractors, no trick wording). Use when the user asks for practice questions, quiz items, or exam prep.
gh300-lab-creator
by timothywarner-orgCreate short GH-300 practice exercises (10-20 minutes) that are executable and self-validating. Every exercise includes prerequisites, exact tasks, validation steps, expected outcomes, and rollback. Use when the user asks for a hands-on exercise, practice flow, or guided walkthrough.
ab100-item-creator
by timothywarner-orgGenerate AB-100 practice questions that feel like the real exam without copying it. Every item is grounded in current Microsoft Learn content, uses modern Microsoft product names (Microsoft Foundry, Copilot Studio, Microsoft Entra ID), and follows the Microsoft Worldwide Learning Exam Writing Style Guide (WWL). Use when the user asks for practice questions, quiz items, or exam prep.
ab100-study-planner
by timothywarner-orgGenerates a personalized AB-100 study plan based on the user's self-assessed confidence across the three AB-100 domains, prioritizing weak areas with estimated hours and Microsoft Learn module links. Use when the user asks for a study plan, is unsure what to study, or wants exam prep guidance.
ab100-lab-creator
by timothywarner-orgCreate short AB-100 practice labs (15-25 minutes) that are executable and self-validating. Every lab includes prerequisites, exact tasks, validation steps, expected outputs, and cleanup. Scope covers agent design walkthroughs, Copilot Studio authoring, Microsoft Foundry tool configuration, ALM patterns, and governance controls. Use when the user asks for a hands-on lab, practice exercise, or guided walkthrough.
hr-pipeline-reviewer
by timothywarner-orgPipeline review and architectural validation skill for the Contoso HR Agent project. Covers LangGraph + CrewAI + FastMCP 2 + Pydantic v2 patterns and the rules specific to this codebase. Use when reviewing any change to src/contoso_hr/ or when adding new pipeline components.
azure-principal-architect
by timothywarner-orgExpert Azure Principal Architect providing guidance using Azure Well-Architected Framework (WAF) principles and Microsoft best practices. Use for cloud architecture decisions, Azure service selection, infrastructure design, and WAF pillar assessments.
warnerco-schematica
by timothywarner-orgDevelop and extend the WARNERCO Robotics Schematica system - an agentic RAG application with FastAPI, FastMCP, LangGraph orchestration, and 3-tier memory (JSON/Chroma/Azure AI Search). Use when working on the schematica backend, adding schematics, modifying the LangGraph flow, updating dashboards, or deploying to Azure.
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.