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...
audit-engine
by Kunshao1117[Audit] Health audit semantic reasoning engine — AI-driven analysis for /08_audit_index §2 (S1–S5, API, test coverage, architecture). Use when: 執行 /08_audit_index 的語義推理審查（安全架構 S1–S5 / 前後端串接比對 / 測試覆蓋缺口 / 架構分析）。 DO NOT use when: 執行 ESLint/npm audit 等工具掃描（用 code-audit）、非 /08_audit_index 工作流、修復或重構場景。
map
by Kunshao1117框架導航索引卡。記錄所有 Layer 1 父記憶卡的模組名稱與範圍摘要， 供對話啟動時的 D7 Push 機制快速載入，讓 AI 在不深讀各模組卡的情況下知道「哪裡有什麼」。 Use when: D7 三路徑探測的第一條路徑（_map 在清單中）。
11-handoff
by Kunshao1117Use when: 交接、handoff、彙整目前對話成果、掃描記憶卡並產出下一個 AI 可接手的提示詞。DO NOT use when: 仍在實作或需要提交。
11-handoff
by Kunshao1117Use when: 交接、handoff、彙整目前對話成果、掃描記憶卡並產出下一個 AI 可接手的提示詞。DO NOT use when: 仍在實作或需要提交。
06-test
by Kunshao1117Use when: 執行 E2E、視覺測試、介面適配證據、瀏覽器功能測試、桌面 GUI 驗證、終端輸出驗證、回歸驗證或測試委派。DO NOT use when: 只需要單元測試設計或純程式碼審查。
09-commit
by Kunshao1117Use when: 提交、commit、push、版本紀錄、CHANGELOG、plugin/extension/插件/延伸模組、VSIX、Release/發布、version/版本、tag、update reminder/更新提醒 前置掃描與受治理備份。DO NOT use when: 尚未完成實作或只想查看 git 狀態。
09-commit
by Kunshao1117Use when: 提交、commit、push、版本紀錄、CHANGELOG、plugin/extension/插件/延伸模組、VSIX、Release/發布、version/版本、tag、update reminder/更新提醒 前置掃描與受治理備份。DO NOT use when: 尚未完成實作或只想查看 git 狀態。
08-1-infra
by Kunshao1117Use when: 健檢第一階段、專案型態偵測、平台能力快照、基礎盤點、相容性、依賴掃描、治理拓樸、技能覆蓋率與目錄衛生檢查。DO NOT use when: 要完整健檢入口，改用 08-audit。
08-audit
by Kunshao1117Use when: 全光譜專案健檢、audit、證據式健檢、專案型態偵測、相容性檢查、治理巡檢、基礎盤點、深度邏輯審查、真實驗證、plugin、VSIX、Release、version、tag、update reminder 與健康報告。DO NOT use when: 只要單一測試或單一 bug 修復。
08-3-report
by Kunshao1117Use when: 健檢第三階段、彙整證據式健康報告、紅黃綠燈號、未驗證/阻塞判定、優先修復清單、位置索引與行動建議。DO NOT use when: 尚未完成前兩階段健檢。
08-2-logic
by Kunshao1117Use when: 健檢第二階段、深度邏輯審查、安全架構、API/資料流串接比對、狀態不變量、測試覆蓋缺口、真實證據缺口、效能可靠性、plugin、VSIX、Release、version、tag、update reminder 與死碼偵測。DO NOT use when: 要完整健檢入口，改用 08-audit。
06-test
by Kunshao1117Use when: 執行 E2E、視覺測試、介面適配證據、瀏覽器功能測試、桌面 GUI 驗證、終端輸出驗證、回歸驗證或測試委派。DO NOT use when: 只需要單元測試設計或純程式碼審查。
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.