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...
pm-business-model
by konglong87Use when: 需要设计商业模式画布、规划盈利模式、制定定价策略、设计收入模型 Do NOT use when: 商业模式已确定且已验证、仅需简单定价调整而非全面设计
pm-portfolio
by konglong87Use when: 管理多个产品线需要组合决策、BCG矩阵分析、产品生命周期评估、资源分配 Do NOT use when: 单一产品无需组合管理、产品线已固定无需调整
super-pm-upgrade
by konglong87Use when: 需要检查super-pm更新、升级到新版本、回退到旧版本 Do NOT use when: 正常使用skill无需版本管理、首次安装而非升级
pm-resource
by konglong87Use when: 需要在多个产品间分配研发资源、进行ROI评估、解决资源冲突、团队规划 Do NOT use when: 资源充足无需分配优先级、单产品无需多项目协调
pm-growth
by konglong87Use when: 需要制定增长策略、优化增长指标、突破增长瓶颈、设计系统化增长方案 Do NOT use when: 产品仍在验证期未上线、仅需单一渠道优化而非系统方案
pm-iteration
by konglong87Use when: 需要规划迭代内容、制定迭代排期、明确迭代优先级、从需求到排期的落地 Do NOT use when: 迭代已由团队自行规划、无需系统化管理
pm-roadmap
by konglong87Use when: 需要规划产品中长期发展、设定里程碑、对齐团队方向、制定季度/年度计划 Do NOT use when: 短期执行无需长期规划、方向已锁定无需路线图
pm-release
by konglong87Use when: 风险管控完成后准备上线发布、需要制定上线检查清单与回滚方案、正式发版流程 Do NOT use when: 上线流程已标准化且由运维自动化执行、仅需简单发布无需检查清单
pm-risk
by konglong87Use when: 技术方案完成后需要风险排查、上线前风险识别评估、项目关键节点风险管控 Do NOT use when: 项目极小风险可控、已存在完整的风险管理机制无需新建
pm-funnel
by konglong87Use when: 需要分析用户转化漏斗、诊断流失原因、生成优化建议、提升用户转化率 Do NOT use when: 用户转化路径已非常清晰、仅需单一指标监控无需全链路分析
pm-demand
by konglong87Use when: 开始新产品规划、需要系统化收集需求、验证产品想法真伪、用户提到"我想做一个产品" Do NOT use when: 需求已明确且已验证、仅需执行而非调研
pm-mvp
by konglong87Use when: 需要确定第一版产品功能范围、已有需求清单需筛选 MVP 功能、需要确定最小可行产品边界 Do NOT use when: 产品已上线需要完整功能集、仅需梳理需求无需裁剪范围
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.