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...
browser-use
by u7chanbrowser-use CLI の独自ブラウザで localhost などの画面確認を進める
bun-dependency-update
by u7chanUse this when asked to update dependencies in a Bun application, whether the request is a broad non-major refresh or a major-version upgrade for a specific package. It captures the branching workflow for deciding between safe non-major Bun updates and an interactive major-upgrade flow with upstream research, user confirmation, one-package-at-a-time changes, and project validation.
skills-readme-sync
by u7chanUse this when adding, renaming, removing, or materially changing skills in this repository and the README may need to be updated. It synchronizes the README's Available Skills table with the current skill set.
codex-skills-link-from-claude
by u7chanUse this when asked to make Claude Code skills available to Codex by creating a `.codex/skills` symlink that points to `.claude/skills`. It captures the exact symlink pattern that works in this repo and avoids the relative-path mistake that breaks editor discovery.
git-branch-create
by u7chanBranch naming and creation workflow
git-commit-message
by u7chanCommit message suggestion workflow
git-worktree-create
by u7chanCreate an isolated git worktree for coding-agent work, including base branch resolution, branch naming, collision checks, and the handoff path for implementation.
github-implement-pr
by u7chanGitHub Issue や実装タスクの対応を依頼され、作業領域の準備、コード変更、 検証、コミット、push、PR 作成までを既存スキルを参照しながら一連で進める時に使う。
github-issue-create-from-plan
by u7chanユーザーから設計やプラン作成を求められ、その合意後にGitHub Issueを作成する時に使う。 まずプランを作成して提示し、現在モードが plan の場合は切替案内を出し、edit または auto の場合はそのまま `gh issue create` を実行する。 Issue作成後は人間向けHTMLも作成するか確認し、承諾された場合だけ `html-artifact-format` に従ってHTMLを生成する。
github-pr-comment-reply
by u7chanUse this when asked to reply to an existing GitHub Pull Request review comment or PR conversation comment. Trigger this skill when the user provides a comment URL or comment ID, or asks to find a comment on the current PR and post a reply on GitHub.
github-pr-create
by u7chanUse when asked to create or open a GitHub PR, including casual requests like "PRまでお願い", "PRまで", "pushしてPR", "PR作って", or "PR open". Handles PR body generation and gh-based PR creation.
github-pr-feedback-address
by u7chanUse this when asked to address feedback on an existing GitHub Pull Request, including casual requests like "PRのFBおねがい", "PRのコメントみて", "レビューコメント対応して", "FB対応して", or requests made after a PR has been opened and review comments are expected. Finds the current PR from the provided PR URL/number or current branch, inspects unresolved review feedback, implements fixes, validates, commits, pushes, and replies to the addressed feedback comments.
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.