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...
cursor-sdk
by haruto-yamada1Cursor TypeScript SDK（`@cursor/sdk`）上で app、script、CI pipeline、automation を構築するガイド。Cursor SDK の統合・インストール・コード記述、`Agent.create`、`Agent.prompt`、`Agent.resume`、`agent.send`、`run.stream`、`CursorAgentError`、`@cursor/sdk` の言及、Cursor IDE 外（script、CI/CD pipeline、GitHub Action、backend service など）から Cursor agent を programmatic に実行、local/cloud runtime 選択、SDK agent 用 MCP server 設定、streaming・cancellation・error 処理、automation/bot/REST `/v1/agents` migration への Cursor 組み込み時に使います。記憶に頼らず積極的に使ってください。SDK surface は進化し、この skill が external package の source of truth です。
github-issue-implementation
by haruto-yamada1GitHub issue URL を渡され、issue の内容を起点に repository 確認、専用 worktree 作成、実装、検証、handoff まで進める依頼で使います。issue 番号だけでなく URL、関連 PR、コメント確認、並行作業用の隔離 worktree が必要なときに発火します。
grill-with-docs
by haruto-yamada1計画をTABBINのドメインモデルと照合し、用語を磨き、決定が固まったらドキュメント（CONTEXT.md、ADR）を逐次更新する「グリリング」セッション。プロジェクトの言語と記録された決定に対して計画をストレステストしたいときに使います。
harness-evaluator
by haruto-yamada1TABBIN のハーネス run を fresh-context Evaluator として評価し、evaluator.json に approved / changes_requested / blocked を記録するときに使います。
harness-generator
by haruto-yamada1TABBIN のハーネス run で Generator として実装し、checkpoint と検証証跡を generator.json に記録するときに使います。
harness-optimizer
by haruto-yamada1TABBIN の Evaluator 指摘や governance event から learning.json を作り、必要な follow-up issue または .apm/instructions 追記候補を整理するときに使います。
harness-planner
by haruto-yamada1TABBIN のハーネス run で要件、制約、検証方針を Planner として分解し、planner.json と orchestrator.json の plan を更新するときに使います。
migrate-to-skills
by haruto-yamada1Cursor rule（.cursor/rules/*.mdc）と slash command（.cursor/commands/*.md）を Agent Skills 形式（.cursor/skills/）へ変換します。rule や command を skill へ migrate、.mdc rule を SKILL.md 形式へ変換、command を skills directory に統合したいときに使います。
react-doctor
by haruto-yamada1React の変更後に問題を早期検出するために実行します。React project の code review、feature 完了、bug fix 時に使います。
remotion-best-practices
by haruto-yamada1Remotion（React による動画作成）のベストプラクティス
requesting-code-review
by haruto-yamada1タスク完了時、大きな機能実装後、merge 前に要件を満たしているか検証するときに使います。
security-review
by haruto-yamada1TABBIN のブラウザ拡張 security、permission、storage handling、user-provided content、dependency change、release-sensitive code をレビューするときに使います。
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.