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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debug
by stagewise-ioUse logging calls for in-depth debugging via local log files
figma
by stagewise-ioComplete guide for the Figma plugin — REST API access, real-time selection monitoring via CDP, and the figma-app interactive UI. Read this IMMEDIATELY when the user asks to work with Figma.
github
by stagewise-ioComplete guide for the GitHub plugin — REST API access for repositories, issues, pull requests, actions, releases, and search using a GitHub Personal Access Token.
history-compression
by stagewise-ioHow stagewise's agent history compression pipeline works — boundary selection, recency bias, chained compressions, and the SQLite-backed test harness for replaying real compressions in LLM playgrounds. Use when debugging, tuning, or extending history compression, when investigating context-window overflow, or when the user wants to probe compression quality against real chat histories.
javascript-sandbox
by stagewise-ioBest practices for using the stagewise built-in JavaScript sandbox. Explains how to access APIs for browser debugging/interaction, use external dependencies, file system access, running mini-apps, etc.
karton-best-practices
by stagewise-ioPerformance-focused guidelines for writing React code that consumes Karton state. Use when creating or reviewing components that use useKartonState, useKartonProcedure, useComparingSelector, or any Karton React hooks. Covers selector patterns, re-render prevention, structural sharing, and edge cases.
implement
by stagewise-ioImplement the most recent plan
vercel
by stagewise-ioComplete guide for the Vercel plugin — REST API access for deployments, logs, projects, and environment variables using a Vercel Personal Access Token.
mini-apps
by stagewise-ioGuide for building custom interactive web apps ("mini apps") displayed in browser tabs — scaffolding, iframe constraints, bidirectional messaging with the sandbox, and iteration workflows.
supabase
by stagewise-ioComplete guide for the Supabase plugin — Management API access for running SQL queries, listing projects, managing edge functions, secrets, migrations, and inspecting project health.
posthog
by stagewise-ioComplete guide for the PostHog plugin — REST API access for querying analytics with HogQL, managing feature flags, inspecting events and persons, reading insights, experiments, cohorts, surveys, and more.
prompt-optimization
by stagewise-ioGuides building, reviewing, and optimizing system prompts and prompt templates for LLMs. Use when creating system prompts, writing prompt templates, optimizing prompt structure, reducing prompt token usage, compressing prompts, improving prompt clarity, reviewing prompts for safety and bias, or making prompts more token-efficient. Also use when the user says "write a prompt," "optimize this prompt," "system prompt," "prompt engineering," "make this prompt better," "reduce tokens," or "compress this prompt."
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.