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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missing-tools
by OJII3Resolves missing CLI tools. Use when a command is unavailable, a shell reports command not found, or a tool must be run without installing it globally.
empirical-prompt-tuning
by OJII3agent 向けテキスト指示（skill / slash command / task プロンプト / CLAUDE.md 節 / コード生成プロンプト）を、バイアスを排した実行者に動かしてもらい、両面（実行者の自己申告 + 指示側メトリクス）で評価して反復改善する手法。改善が頭打ちになるまで回す。プロンプトや skill を新規作成・大幅改訂した直後、またはエージェントの挙動が期待通りにならない原因を指示側の曖昧さに求めたいときに使う。
using-flake-parts
by OJII3Expert guidance for using flake-parts framework in Nix flakes. Use when converting flakes to flake-parts, organizing modular flake configurations, working with perSystem, creating reusable flake modules, handling overlays, or debugging flake-parts issues.
self-add-skill
by OJII3home-manager 管理環境で新しいスキルを追加するためのガイド。ユーザーが新しい▽ゆースキルを作成したいとき、~/.claude/skills や ~/.codex/skills に直接作成する代わりに、dotfiles リポジトリ内の modules/home/ai/skills/ に作成し、home-manager switch で適用する必要がある環境、つまりユーザー単位向け。dotfiles でない場合、基本的にプロジェクト単位で追加するため、このスキルは使用しない。
debug
by OJII3Use when debugging failures, unexpected behavior, or test breakages — before attempting any fix.
review-pr
by OJII3Use when reviewing changed code — fix trivial issues directly and create GitHub Issues for larger concerns.
delegate-to-shell-worker
by OJII3Delegate to shell-worker whenever a Discord user asks for work that benefits from a real shell workspace or a capable background worker: running commands or code, compiling, testing, installing packages, inspecting files, generating or editing files, data conversion, calculations, web/API checks, longer technical investigation, or preparing attachments. Prefer using this skill and tasking shell-worker instead of answering from memory when shell, files, packages, or verification would help.
minecraft
by OJII3Use when a Discord user asks about Minecraft status, Minecraft session control, or wants the Minecraft agent to do work in-world.
self-update
by OJII3Use when a Discord user asks to modify ふあ's own behavior, configuration, prompts, or skills. Covers skill additions ('freeeのMCPスキル追加して' 'これスキルにして'), skill modifications ('このスキルの動作変えて' '〇〇スキルの説明更新して'), agent config/prompt changes ('プロンプト修正して' 'エージェントの設定変えて'), self-improvement requests ('こういうときの動作改善して'), and bug fixes to agent behavior ('〇〇のときの挙動おかしいから直して'). Orchestrates self-modification by delegating repository work to shell-worker: create a branch, follow AGENTS.md, validate, commit, push, open a PR, and auto-merge only when guardrails pass. Do NOT trigger for plain chit-chat, one-off questions, or regular project/app code changes unrelated to ふあ's own behavior.
minecraft-agent-playbook
by OJII3Use on every Minecraft brain turn when choosing the next in-world action, processing Discord commands or reactive-layer events, planning goals, updating progress, recovering from stuck states, handling night/food/safety decisions, recording Minecraft world skills, or deciding whether to report back to Discord.
auto-triage
by OJII3GitHub Issue を自動で選んで取り組み、レビュー・マージまで行う自律ワークフロー
brainstorm
by OJII3Use when exploring new ideas or concepts before specification — feature proposals, architectural explorations, or research.
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.