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...
weekly-report
by 812lcl按产品项目聚合生成每周工作周报（一级产品项目名 + 直接 bullet，每条复合一个主题），跨 repo 合并同一产品项目的工作。覆盖 skywork/agent 各子项目 git 提交、Obsidian area::work 完成任务、brain 中 quest/memo/notes 进展。Use when the user asks for 周报 / 本周工作总结 / 周五总结 / weekly report / weekly summary, especially around end-of-week or when explicitly invoked.
service-health
by 812lclskywork/agent 项目核心 8 件套（gateway / chat / creation / oh-my-agent / ockernel / channels / user_center / mis）的端到端健康检查。覆盖运行时部署真相（SLS image_name 分布 + ARMS pod image，不以 Jenkins 状态为唯一标准）、SLS ERROR/WARN pattern 分析（抽样自归类，给 top 3-5 模式 + 占比 + 样例）、部署前后对比（pattern 新增 / CPU 内存差异 / QPS 差异）、ARMS pod CPU/内存（按 ReplicaSet 分组）、RDS/Redis 实例 CPU/QPS/连接数（不查数据）。多服务并行用子代理。当用户说 "健康检查 / 查看服务状态 / check 服务 / service health / 看一下 X 服务是否正常 / X 服务怎么样 / 巡检 / 体检" 等场景时使用，无论是否带具体服务名。
brain
by 812lclPersonal knowledge base CLI — your long-term memory across sessions. Use when the user asks about 记录, 知识库, memo, note, dsat, insight, knowledge, preference, todo, checkpoint, or any persistent capture/recall task. Also load at the start of any non-trivial task and run brain brief to absorb durable preferences and recent context in one shot.
lark-slides
by 812lcl飞书幻灯片：创建和编辑幻灯片，接口通过 XML 协议通信。创建演示文稿、读取幻灯片内容、管理幻灯片页面（创建、删除、读取、局部替换）。当用户需要创建或编辑幻灯片、读取或修改单个页面时使用。当用户给出 doubao.com 的 /slides/ URL/token 时，也应直接使用本 skill，不要因为域名不是飞书而回退到 WebFetch；路由依据是 URL 路径模式和 token，而不是域名。
obsidian-review
by 812lclObsidian vault 中 Daily/Weekly/Monthly/Quarterly/Yearly review 与 plan 的总入口。Review 段先聚合上一周期数据（含 vault 文件 + 完成 tasks + 跨 repo git 提交 + brain memo）→ 苏格拉底追问 → 写回 review 字段；Plan 段从任务池（四象限 + backlink 两视图）筛 → 用户挑序号 → 自动 ⏳ schedule + 写入 plan 字段。Use when the user invokes `/obsidian-review`, `/obsidian-review daily|weekly|monthly|quarterly|yearly [review|plan]`, or asks for 日回顾 / 周回顾 / 月回顾 / 季回顾 / 年回顾 / 日计划 / 周计划 / 月计划 / 季计划 / 年计划 / daily review / weekly review 等场景。
knowledge-clip
by 812lclClip external content (podcasts, articles, videos, tweets, PDFs, local files) into the user's Obsidian vault as structured Markdown notes following the Knowledge.md template. Triggers when the user pastes a URL or asks to clip / save / archive / 剪藏 / 收藏 / 存到知识库 / 加到 Obsidian / 收进知识库 with a URL or file path. Auto-detects source type (小宇宙, YouTube, B站, X, 微信公众号, 少数派, PDF, local), fetches content (defuddle for web, yt-dlp for video, autocli for X and B站), generates AI summary plus key points plus category plus tags, then writes to the vault path 4-knowledge_hub/Clippings/ with file name "[type] title.md". After clipping, if the vault has a 5-wiki/ directory, asks the user once whether to also run /wiki-ingest on the clip to feed it into the LLM Wiki. Supports running across projects and other agents via OBSIDIAN_VAULT_PATH env var.
lark-drive
by 812lcl飞书云空间（云盘/云存储）：管理云空间（云盘/云存储）中的文件和文件夹。上传和下载文件、创建文件夹、复制/移动/删除文件、查看文件元数据、管理文档评论、管理文档权限、订阅用户评论变更事件、修改文件标题（docx、sheet、bitable、file、folder、wiki）；也负责把本地 Word/Markdown/Excel/CSV/PPTX 以及 Base 快照（.base）导入为飞书在线云文档（docx、sheet、bitable、slides）。当用户需要上传或下载文件、整理云空间（云盘/云存储）目录、查看文件详情、管理评论、管理文档权限、修改文件标题、订阅用户评论变更事件，或要把本地文件导入成新版文档、电子表格、多维表格/Base/幻灯片 时使用。"云空间"、"云盘"和"云存储"是同一概念，用户说"云盘"、"云存储"、"网盘"、"我的空间"时均路由到本 skill。当用户给出 doubao.com 的云空间资源 URL/token，或明确提到豆包里的 file/folder/docx/sheet/bitable/wiki 资源时，也应直接使用本 skill，不要因为域名不是飞书而回退到 WebFetch；路由依据是资源类型、URL 路径模式和 token，而不是域名。
brain
by 812lclPersonal knowledge base CLI — your long-term memory across sessions. Use when the user asks about 记录, 知识库, memo, note, dsat, insight, knowledge, preference, todo, checkpoint, or any persistent capture/recall task. Also load at the start of any non-trivial task and run brain brief to absorb durable preferences and recent context in one shot.
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.