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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oq-manage
by Kawato-MilesOQ（Open Question）管理 skill。正本：Vault `memory/Sens_wiki/wiki/erp/08-open-questions/`（平層＝未結案佇列；`_archives/<YYYY>/`＝已結案封存）。 觸發時機： 1. 任何討論／撰寫中識別到設計不確定項（MUST 立即開檔，禁 inline 標注） 2. Miles 說「新增 OQ」「這個要記下來」「有個問題要確認」（mode B） 3. 討論開始前查特定模組未解 OQ（mode A） 4. OQ 已拍板（mode C 解答與封存） 5. Miles 說「遷出 [檔案] 的 OQ」或掃描發現 inline OQ 措辭（mode D） 6. Miles 說「整理 OQ」「掃 OQ」「OQ 健康」（mode E 批次整理） 不適用：已確認的決策記錄（直接寫正本卡）、術語定義更新、一般討論備忘、未消化的觀察素材（走 vault-ingest）。
vault-ingest
by Kawato-MilesERP_Vault raw 素材承接與精練 skill（素材進入知識庫的唯一閘門）。 觸發時機： 1. Miles 說「存進 raw」「我要記」「先收集」「這份檔案存 raw」（mode A） 2. Claude 完成被指派的研究任務（WebFetch / WebSearch）後（mode A，自主） 3. Claude 在對話中識別「值得記」的素材（mode A，MUST 先問 Miles） 4. Miles 說「精練 [檔名]」「ingest 這張」「拆解 raw」（mode B） 5. Miles 說「看 raw」「掃 raw」「raw 待處理」；或累積 ≥ 10 張 status=raw 時建議（mode C） 範圍：寫入限 `memory/Sens_wiki/raw/`（mode A）與 Miles 確認後的 wiki 卡（mode B）；mode C 純報告不動檔。 不適用：已精練的知識更新（直接改既有 vault 卡）、明確未解問題（走 oq-manage）、UI 規範（留 DESIGN.md）。
erp-planning-pre-check
by Kawato-MilesERP 規劃前 know-how 稽核 skill。Claude 規劃 ERP 功能前 MUST 跑稽核識別缺漏 / 錯誤，修補既有卡（不新建抽象卡），避免重複問已答過的真實狀況、設計跑偏。 正本框架：`.claude/skills/erp-planning-pre-check/references/audit-framework.md` 領域分類：`memory/Sens_wiki/wiki/erp/00-meta/business-domain-taxonomy.md`（6 領域 + 跨領域共用層） 觸發時機： 1. Miles 說「規劃 X 功能」「設計 Y」「修改 Z 邏輯」「我要規劃 ...」「我想做 ...」 2. CLAUDE.md 路由偵測到「款項 / 發票 / 收款 / 對帳 / OA / 退款 / 補收」等觸發詞 3. 規劃中 Claude 遇到「不知道」MUST 立即再跑（不能繞過去自編） 4. OpenSpec change 工作流（`/opsx:propose` / `/opsx:new`）背景對齊階段 範圍：**只處理 ERP_Vault know-how 稽核**。 輸出：對話報告（量化矩陣）+ 追加 wiki/log.md 一筆（動作=健檢、標籤=pre-check，只記摘要）+ 修補既有卡 + 缺漏項標 OQ。 不適用：純查詢術語 / 狀態機（不涉及規劃）、Vault 整體健康稽核（用 vault-audit）。 **執行者與稽核者分離**（受 YouTube /goal 影片啟發）：稽核 sub-agent 跑稽核，主對話 agent 跑修補；MUST NOT 同 agent 自審。
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.