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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langfuse
by KonghaYaoInteract with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via bunx) and multiple documentation retrieval methods.
teacher
by KonghaYao架构对齐测验——agent 主动从架构维度提问，用户作答，agent 针对偏差给出纠正和解释。 当用户说"考考我架构"、"帮我对齐架构认知"、"测测我对架构的理解"、 "我想确认一下我对这个系统的理解"、"架构对齐"时立即触发。 也适用于："我不确定自己理解对了没"、"帮我检验一下"、"梳理一下架构认知"等场景。 只要用户想验证或校准自己对系统架构的理解，就应使用此 skill。
peri-style
by KonghaYaoPeri 项目的写作风格指南。当用户说"改一下表述"、"这段不像人话"、"太啰嗦了"、 "要有故事感"、"像宣传文案"、"简洁一点"、"不要重复"时触发。 也适用于用户要求润色 README、文档、宣传文案、或对已写文本的风格不满意时。
issue-create
by KonghaYao通过访谈式问答创建结构化的 issue 文档。当用户说"记一个 issue"、"创建一个问题"、 "我遇到一个 bug"、"这里有个问题要记下来"、"帮我记录一下这个问题"、 "有个技术债要记"、"这个需要重构"时立即触发。 也适用于用户描述了一个问题、异常行为、代码坏味道、性能瓶颈、安全风险等场景， 即使没有明确说"创建 issue"，只要用户在描述一个值得记录的技术问题就应触发。 与 fix-issue 不同，本 skill 专注于信息收集和文档化，不执行修复。 与 interview 不同，本 skill 有明确的输出格式（issue 文档）和分类评级逻辑。
llm-log-analyzer
by KonghaYao分析 llm-gateway 代理产生的请求/响应日志。当用户说"分析日志"、"查看 LLM 请求"、"对比 session"、"检查 token 用量"、"日志里有什么"、"帮我看看 data 目录"、"哪个请求失败了"、"找一下 session 的请求"等涉及 LLM 网关日志分析的场景时使用此 skill。即使用户只是笼统地说"看看日志"或"data 里有什么"，也应触发。
issue-archive
by KonghaYao归档已关闭/已修复的 issues，提取经验教训并同步更新 CLAUDE.md 和 spec/global。 当用户说"归档 issue"、"archive issues"、"清理已修复的 issue"、 "归档已关闭的 issue"、"整理一下 issues"、"把修好的 issue 归档了"时触发。 也适用于用户想要清理 spec/issues/ 目录或将已解决问题归档的场景。 如果 spec/issues/ 中有 Fixed/Closed/Done 状态的 issue 积压，应主动建议使用此 skill。
slop-cleaner
by KonghaYao自动化代码卫生扫描——识别大文件、死代码、测试与实现混合、架构坏味道等结构性问题， 产出结构化清扫报告和终端摘要。偏自动化批量扫描，非深度 code review。 当用户说"扫一下 slop"、"清理代码卫生"、"检查代码质量"、 "找找死代码"、"哪些文件太大了"、"测试混在源码里"、"架构有没有问题"、"代码有没有坏味道"、 "slop scan"、"clean up codebase"、"find dead code"、"large files"时立即触发。 也适用于："帮我看看有没有技术债"、"代码库健康度怎么样"、"这个模块该不该拆"、 "有没有未使用的代码"等代码卫生评估场景。 只要用户想快速了解代码库的结构性卫生状况，就应使用此 skill。
issue-verify
by KonghaYaoIssue 状态变更与验证记录。当用户说"验证一下这个 issue"、"issue 验证通过了"、 "这个问题修好了"、"还没修好"、"Reopen 这个 issue"、"部分修复了"、 "关掉这个 issue"、"把 issue 状态改成 xxx"时立即触发。 也适用于 fix-issue 完成后需要记录修复结果、用户回归测试后反馈结果、 或任何需要变更 issue 状态的场景。 即使没有明确说"验证"，只要用户在反馈 issue 的修复效果就应触发。
langgraph-development
by KonghaYaoGuide for building agents with LangChain/LangGraph in TypeScript. Covers createAgent, tools, and agent patterns. Use when creating agents, implementing tools, or setting up agent infrastructure.
hakka
by KonghaYaoRepresent Hakka culture with authentic dialect, mannerisms, and knowledge about history, customs, language, geography, cuisine, architecture, and values. Role-play as Hakka person; answer questions about Hakka culture, figures, migration history; demonstrate Hakka values like diligence, frugality, education, unity.
canton
by KonghaYao用正宗广东话嘅语气、举止同埋知识去代表广府文化，包括历史、风俗、语言、地理、美食、建筑同价值观。扮成一个广东人；回答关于广府文化、人物、移民史嘅问题；展现广东人嘅价值观，好似务实、开放、识饮识食、创业精神同埋享受生活咁。
dongbei
by KonghaYao用地道东北话的语气、举止和知识去代表东北文化，包括历史、风俗、语言、地理、美食和价值观。扮演成一个东北人；回答关于东北文化、人物、闯关东的问题；展现东北人的价值观，比如豪爽、热情、实在、幽默、重感情、能吃苦。
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.