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...
p6
by mosonlab流水线 P6：从正文切片里只挑五类硬伤（人设矛盾/逻辑断裂/设定冲突/时间线错乱/爽点铺了没爆），不改稿、对文笔完全免疫、宁漏报不凑数；必须开独立于写正文的全新会话跑。吃设定卡＋事件摘要＋正文切片，吐硬伤清单。何时用：作者说 审稿、查硬伤、挑逻辑漏洞、P6，或每写满 10 章质检时。
p2
by mosonlab流水线 P2：在作者选定的脑洞下挖市场钩子与事件毛坯，按 a→c→b 顺序产出书名卖点 12 组（P2-a）、简介 3 版（P2-c）、18 个事件毛坯（P2-b）供作者挑约 70% 改写；含可选素材对齐机（P2-d）。吃套路 JSON 题材调性＋作者一句话脑洞，吐 00_脑洞.md 底稿。何时用：作者说 起书名、写简介、挖事件、脑洞供料、事件毛坯、P2，或换壳主轴拍板后要定书名简介事件时。
p5
by mosonlab流水线 P5：把本章细纲一镜到底演成净 2100–2300 字正文，解析并删净括号控场指令，全程守调性卡＋文风卡。吃六槽（调性卡＋文风卡＋角色卡＋事件摘要＋本章细纲＋上章结尾），吐 正文/第NNN章.md（落盘后跑 check.ts 核净字数）。何时用：作者说 写正文、出稿、写第 N 章、接着写下一章、P5，或细纲锁定要出正文时。
p8
by mosonlab流水线 P8：滚动维护记忆层活文档——P8-a 事件摘要（每章）/ P8-b 设定卡重出（每 10 章）/ P8-c 数值台账＋新角色回流（每章顺手）/ P8-d 卷末收口快照（每卷一次）。吃本章正文＋旧活文档，回写 02_人物圣经／03_设定卡／04_事件摘要／05_数值台账／收口快照。何时用：作者说 更新摘要、更新设定卡、记台账、收口快照、P8，或每章正文落盘后、每 10 章、每卷末。
open-novel-fanqie
by mosonlab番茄男频开新书·全流程总向导。带作者从「选定对标书」一路走到「逐章出正文」，把 P1–P6 + P8 十个步骤串起来，每个要作者拍板脑洞/走向的节点都停下来问、绝不替定。两阶段：①开书上游（拆书→换壳→脑洞→书名简介事件→蓝图→圣经设定卡→章纲→细纲→锁定）②逐章循环（正文→审稿→活文档）。何时用：作者说 开新书、拆书仿写、从对标书走大纲细纲、带我走开书上游、逐章写正文、接着写下一章。
p3
by mosonlab流水线 P3：先锁调性卡＋文风卡两张随身卡，再扩出分层大纲 ABCD（世界观金手指／势力反派梯队／前 30 章逐章剧情点／人物感情线）；含 P3C 续卷（挖本卷事件＋扩散下一卷）。吃 P1 套路 JSON＋作者选定的脑洞/卖点/事件，吐 01_蓝图.md。何时用：作者说 做蓝图、出大纲、锁设定、写续卷/第二卷、P3、P3C，或 P2 挑完事件要进大纲时。
p3b
by mosonlab流水线 P3b：从蓝图大纲 A/B/D 整理两份每章喂正文的活文档——人物圣经（主角卡 ≤300 字＋核心配角 ≤5 张各 ≤150 字）和设定卡（5 块 ≤500 字）。吃 01_蓝图.md，吐 02_人物圣经.md＋03_设定卡.md。何时用：作者说 人物圣经、设定卡、立角色卡、整理活文档、P3b，或蓝图完成要进章纲前。
p4b
by mosonlab流水线 P4b：把章纲展开成 P5 能直接照着演的场景脚本（细纲）——分场分拍、要害台词原话、憋放焊进动作；铁律 3 关键步，细纲信息密度决定正文质量上限。吃本章章纲＋设定卡＋调性卡＋上章尾，吐 细纲/第NNN章.md。何时用：作者说 出细纲、展开章纲、场景脚本、P4b，或章纲过关要进正文前。
p4a
by mosonlab流水线 P4a：把大纲 C 第 N 章那行拆成 3–5 条核心事件序列（每条 ≤20 字），标爽点位置、结尾钩子方向、章节名；只列发生了什么，不展开怎么发生。吃大纲 C 本章行＋上章结尾 200 字，吐 章纲/第NNN章.md。何时用：作者说 出章纲、拆章、列本章核心事件、章纲拆解、P4a，或细纲前要先定本章骨架时。
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.