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...
agent-wait-monitor-zh
by Hb-zzz-momoAgent 后台监控协议技能：用于长时间任务的后台启动、wait 轮询、进度文件监控、日志监控、卡死判定、自动续跑、失败重试、最终收口和无人值守实验托管。Use when: 后台监控、agent wait、长任务、无人值守、watchdog、自动续跑、卡死恢复、进度轮询、MATLAB长任务、Python长任务、PowerShell后台任务、manifest、checkpoint、结果文件监控。
project-kickoff-delivery-zh
by Hb-zzz-momo项目启动交付技能：用于外包、比赛、课程和工程项目从读题、调研、需求拆解、技术选型、团队分工、接口/数据库设计到前后端/模型服务启动的交付流程。Use when: 项目启动、外包项目、比赛项目、团队分工、技术选型、需求拆解、接口文档、数据库设计、项目计划、启动命令、前后端联调、交付验收。
research-skill-flow-zh
by Hb-zzz-momo科研 Skill 调用流总控技能：用于多痛点、多假设、多创新点的科研任务分阶段路由、候选池生成、Gap Gate、候选 smoke 漏斗、dev/formal 收敛、论文 claim 分级和外部科研 skill 冲突审计。Use when: 科研调用流、科研skill路由、多候选科研流程、多痛点、多假设、多创新点、候选池、Gap Gate、Memory Intake、Candidate Smoke Funnel、论文claim分级、外部科研skill冲突审计。不适用于：直接执行单一实验、单篇论文写作或普通工程开发，路由后应切换到对应阶段主 skill。
ppt-story-design-zh
by Hb-zzz-momoPPT叙事设计技能：用于课程汇报、路演、创新创业、项目展示和技术方案 PPT 的故事线、页面结构、内容定型、痛点表达、证据支撑和视觉风格规划。Use when: 做PPT、PPT大纲、路演PPT、创新创业PPT、项目展示、汇报材料、页面结构、故事线、痛点分析、市场分析、技术创新、专家背书。
skill-router-zh
by Hb-zzz-momo中文 skills 总控路由技能：用于在 C:\Users\zbh\.agents\skills 中按任务领域、阶段、风险和交付物选择最少必要 skill 组合，避免一次性加载全量技能。Use when: 技能路由、选择skill、统筹skills库、该用哪个技能、组合多个skill、开始任务前分流、Codex自如使用skills、优化skills调用。不适用于：直接执行具体专业任务，路由后应切换到对应领域 skill。
translation-zh
by Hb-zzz-momo翻译技能：将英文学术/技术文本翻译为地道中文，确保零英文残留、语义完整、逻辑通顺。Use when: 翻译论文、翻译文档、英译中、翻译技术文章、翻译摘要。不适用于中译英（论文撰写请使用 paper-writing-zh）。
info-evidence-chain-zh
by Hb-zzz-momo信息证据链技能：用于资料收集、分类检索、真实性验证、来源链接整理、证据链表格、调研报告和带来源的结论输出。Use when: 收集信息、查资料、调研、证据链、来源链接、真实性验证、资料分类、文献链接、网站来源、竞品资料、政策资料、行业数据。
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.