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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deep-discuss
by zhu1090093659结构化深度讨论 Skill，用于与用户进行多轮问题分析和方案设计。当用户描述一个问题现象、故障表现、 技术困惑、方案选择困难，或明确说"讨论一下"、"帮我分析"、"我遇到一个问题"、"你觉得怎么样"、 "帮我想想"、"我在纠结"时，必须使用本 skill。当用户提供了一段描述（可能附带截图）并期望深入分析 而非直接给答案时，也应触发本 skill。即使用户只是抛出一个现象描述没有明确提问，也要使用本 skill 来引导结构化思考。不要在简单的事实查询（"X是什么"）或明确的执行指令（"帮我写个脚本"）上触发。
spec-driven-develop
by zhu1090093659Automates pre-development workflow for large-scale complex tasks. Use when the user mentions "rewrite", "migrate", "overhaul", "refactor entire project", "transform", "rebuild in [language]", "spec-driven", or describes any large-scale project transformation that requires planning before coding. Also triggers on Chinese keywords: "改造", "重写", "迁移", "重构", "大规模", "规范驱动". Performs full project analysis, task decomposition, documentation generation, project-level instruction and native memory surface resolution, progress tracking setup, and then executes the plan within the same session.
morph-ppt
by zhu1090093659Use this skill when the user wants a .pptx with smooth cross-slide animation — PowerPoint Morph transitions, Keynote-style continuous motion, shapes that grow / move / rotate as the slide advances. Trigger on: 'morph', 'morph transition', 'smooth transition', 'continuous animation across slides', 'Keynote-style transition', 'animated slide sequence', 'shape continuity across slides'. Output is a single .pptx. This skill is a scene layer on top of officecli-pptx — inherits every pptx v2 rule (visual floor, grid, palettes, connector canon, Delivery Gate 1–5a). DO NOT invoke for a generic deck, pitch deck, or board review without cross-slide motion — route those to officecli-pptx base or officecli-pitch-deck.
officecli
by zhu1090093659Create, analyze, proofread, and modify Office documents (.docx, .xlsx, .pptx) using the officecli CLI tool. Use when the user wants to create, inspect, check formatting, find issues, add charts, or modify Office documents.
deepseek-automation
by zhu1090093659Use when implementing, resuming, reviewing, or verifying the DeepSeek++ Codex-style automation feature in this repository. Covers reading docs/progress/MASTER.md, following GitHub Issues
agent-browser
by zhu1090093659Browser automation via the agent-browser CLI for navigation, interaction, snapshots, extraction, screenshots, network controls, storage, tabs, frames, and debugging. Use when tasks require automated web UI actions, form filling, scraping, state capture, or repeatable browser workflows.
create-assistant
by zhu1090093659Interactive assistant creator for CodeConductor. Use when users want to create a new custom assistant, design an AI persona, or configure a specialized helper. Triggers on: "create an assistant", "make a new helper", "design an AI that...", "I need an assistant for...", "帮我创建一个助手", "新建助手".
judgment-redteam
by zhu1090093659判断红队 skill。当用户做出某个决策、倾向某个方案、要下判断、说"我决定做 X"、"我觉得应该 Y"、"我想选 Z"、"我的判断是……"、"我要 xxx 了"时，必须使用本 skill。本 skill 不帮用户做判断，而是扮演最锋利的反对者，系统性攻击用户的决策，逼用户看清自己的盲点、前提漏洞、失败场景。适用于技术选型、架构决策、职业选择、投资决定、产品方向等一切需要下注的场景。不要在用户只是在征求信息（"X 是什么"）或明确请求 Claude 给建议的场景下触发。
taste-audit
by zhu1090093659品味审查 skill。当用户展示一段代码、一个设计、一个架构方案、一个 API，或者一段文字内容（文档、文案、邮件），并想判断"这东西好不好"、"是不是还能更优雅"、"有没有哪里丑"时，必须使用本 skill。当用户说"你帮我看看这个"、"这样写对吗"、"有没有更好的写法"、"这个设计怎么样"时，也应触发。本 skill 不给评价，不给答案——它只问问题，逼用户自己看出美丑。不要在用户明确要求"直接给建议"或"帮我写"的场景下触发。
intent-refine
by zhu1090093659意图精炼 skill。当用户描述一个想做的事但表述模糊、想定义一个需求/spec、在纠结某个目标怎么表达、说"我想做个 XX"、"帮我定义一下 XX"、"我要实现的是 XX"、"我的目标是 XX"、"这个需求应该怎么写"时，必须使用本 skill。本 skill 不帮用户写需求，而是通过逼问把用户模糊的意图炼成精确、可验证、无歧义的一句话 spec。适用于做产品需求、定义 skill、写 API 合约、设计实验、制定个人目标等场景。不要在用户已经提供了清晰 spec 并要求执行的场景下触发。
abstraction-uplift
by zhu1090093659抽象升级 skill。当用户的代码/设计已经 work 了，但感觉结构不够好、想问"还有没有更好的抽象"、"要不要重构"、"这里是不是该提取一个 xxx"、"这段结构有点怪"、"好像重复了"、"这个设计是不是还能更简洁"时，必须使用本 skill。本 skill 不直接给重构方案，而是逼用户自己看清当前抽象的问题、识别更深层的结构模式、判断升级方向是否值得。注意：抽象升级有时意味着**增加**一层，有时意味着**删掉**一层——本 skill 对两个方向同等开放。不要在用户明确要求"帮我重构"或"帮我写出来"的场景下触发。
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.