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...
yororen-ui-user
by MeowLynxSeaHigh-quality app code generation for end users building Rust desktop GUIs with gpui + Yororen UI (yororen_ui). Use when a user asks to build or modify an application using Yororen UI/gpui (e.g., "build a beautiful counter with yororen ui", "add a modal form", "add i18n", "use TextInput/SearchInput/ComboBox"), or when working in a Rust project that depends on yororen_ui. Not for contributing to yororen-ui itself.
yororen-ui-state-inputs
by MeowLynxSeaState management and input/form best practices for end users building Yororen UI apps with gpui. Use when implementing TextInput/TextArea/SearchInput/ComboBox/Form/Modal, when wiring on_change/on_submit handlers, or when diagnosing typing lag, cursor jumps, or render loops caused by controlled inputs. Not for developing yororen-ui itself.
yororen-ui-recipes
by MeowLynxSeaEnd-to-end recipe patterns for end users building gpui apps with Yororen UI (yororen_ui). Use when the user asks for a complete working example (counter/todolist/file browser/toast), wants a screen layout pattern, or needs guidance composing components, modals, forms, list rendering, keyed state, virtualization, notifications, or i18n. Not for developing yororen-ui itself.
yororen-ui-app-core
by MeowLynxSeaApp bootstrap and core architecture for end users building a gpui desktop app with Yororen UI (yororen_ui). Use when generating or refactoring main.rs, window setup, global theme, global i18n, assets (UiAsset/CompositeAssetSource), or when creating a new Yororen UI app crate. Not for developing yororen-ui itself.
code-review
by MeowLynxSeaReview code for bugs, security issues, and quality improvements
example-skill
by MeowLynxSeaA demonstration skill showing the SKILL.md format
write-tests
by MeowLynxSeaGenerate comprehensive unit tests for code
routine-advisor
by MeowLynxSeaSuggests relevant cron routines based on user context, goals, and observed patterns
web-ui-test
by MeowLynxSeaTest the Steward web UI using the Claude for Chrome browser extension.
review-checklist
by MeowLynxSeaPre-merge review checklist based on recurring AI reviewer feedback patterns
local-test
by MeowLynxSeaBuild, run, and test Steward locally using Docker containers and Chrome MCP browser automation.
steward-workflow-orchestrator
by MeowLynxSeaInstall and operate a full GitHub issue-to-merge workflow in Steward using event-driven and cron routines. Use when setting up or tuning autonomous project orchestration: issue intake, planning, maintainer feedback handling, branch/PR execution, CI/comment follow-up, batched staging review every 8 hours, and memory updates from merge outcomes.
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.