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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vrchat-osc
by zapabobVRChat OSC integration — chatbox messaging, avatar parameter control, and raw OSC via python-osc. Requires VOICEVOX and VRChat running with OSC enabled.
cowork-productivity-assistant
by zapabobClaudeCode Cowork-style productivity automation with file management, data analysis, browser operations, and autonomous task execution. This skill should be used when automating office productivity tasks, organizing files, analyzing data, scraping web content, or managing workflow automation. Use for document processing, data insights, web automation, and general productivity enhancement.
yaraikata-memo
by zapabobRun long tasks with regular simple updates and an easy explanation. Use when work will not finish quickly, the user wants check-ins every 5 minutes, wants a plain summary with an expected time, and wants clear guidance if something goes wrong.
specstory-session-summary
by zapabobSummarize recent SpecStory AI coding sessions in standup format. Use when the user wants to review sessions from .specstory/history, prepare for standups, track work progress, or understand what was accomplished.
qc-optimizer
by zapabob"Before merging to main, orchestrate full-stack QC: run unit/abnormal/comprehensive tests, apply statistical + quantum-inspired optimization heuristics, and log merge decisions."
qc-optimizer
by zapabobBefore merging to main, orchestrate full-stack QC: run unit/abnormal/comprehensive tests, apply statistical + quantum-inspired optimization heuristics, and log merge decisions.
vrchat-dev
by zapabobVRChat world and avatar development skill with UdonSharp, modularavatar, and PhysBones support. Automates VRChat SDK3 development workflows.
vrchat-dev
by zapabobVRChat world and avatar development skill with UdonSharp, modularavatar, and PhysBones support. Automates VRChat SDK3 development workflows.
yukkuri-movie
by zapabobゆっくりMovieMaker (YMM4) video production skill with character animation, scene management, and MIDI integration.
plan-mode-advanced
by zapabobCreate and execute advanced execution plans for complex AI model development incorporating 2024-2026 cutting-edge techniques (DeepSeek GRPO, manifold-constrained architectures, geometric scaling). Use when planning large-scale model training, architecture optimization, or multi-stage development workflows requiring state-of-the-art methodologies.
hypura-harness
by zapabobVRChatアバター自律制御・VOICEVOX音声台本・Pythonコード生成実行・スキル自動生成・ ShinkaEvolve進化ループを持つ汎用Pythonハーネス。 以下の場合に使う: (1)「VRChatで〜して」「アバターを〜」「チャットボックスに〜」→ POST /osc (2)「〜と喋って」「VOICEVOXで〜」「台本を読んで」→ POST /speak (3)「〜するPythonを書いて実行して」「スクリプトを作って動かして」→ POST /run (4)「〜というスキルを作って」「新しいスキルを追加して」→ POST /skill (5)「〜を進化させて」「もっと良くして」→ POST /evolve (6) LoRA カリキュラム生成・学習ジョブ → POST /lora/curriculum/build, POST /lora/train, GET /lora/jobs/{id} OpenClawが汎用エージェントとしてこれらのツールを組み合わせて自律的に動作する。
hypura-voice-io
by zapabobUse Hypura Harness voice input and output tools for local mic, WAV transcription, VOICEVOX playback, and Desktop Companion voice turns.
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.