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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worry-list-manager
by egbertie担忧清单管理器 - 从被动响应到主动预警（5标准版本）： S1: 输入担忧来源/风险信号/监控范围 S2: 担忧管理（收集→评估→分级→预警→行动） S3: 输出担忧报告+应对建议+状态更新 S4: cron每日09:07自动执行并推送 S5: 担忧评估准确性验证（误报/漏报检查） S6: 局限标注（无法预测黑天鹅事件） S7: 对抗测试（模拟已知风险测试发现能力）
archive-handler
by egbertie安全通用的压缩文件处理工具。支持 ZIP/RAR/7z/TAR/TAR.GZ 格式的解压、内容预览和文件提取，无网络依赖，本地安全处理。
archive-handler
by egbertie安全通用的压缩文件处理工具。支持 ZIP/RAR/7z/TAR/TAR.GZ 格式的解压、内容预览和文件提取，无网络依赖，本地安全处理。
todo-tracker
by egbertie待办事项跟踪器 - 会议行动项的集中管理和跟踪： 1. 全局考虑：覆盖收集、分类、提醒、完成、归档全生命周期 2. 系统考虑：输入→分类→跟踪→提醒→完成→归档闭环 3. 迭代机制：根据完成率优化提醒策略 4. Skill化：标准接口，可对接会议提取器 5. 流程自动化：自动分类、提醒、逾期检测
satisficing-partner-decision
by egbertie满意解研究所核心技能 - 合伙人匹配决策支持体系。 基于五路图腾方法论，提供从初筛到深度评估的全流程决策支持。 强调：AI辅助决策，人类最终决策。安全第一，成本可控。
expert-profile-manager
by egbertie专家数字替身档案管理系统 - 满意解研究所专家网络管理工具。管理黎红雷、罗汉、谢宝剑等教授专家的档案、联系记录、咨询历史。
satisficing-partner-decision
by egbertie满意解研究所核心技能 - 合伙人匹配决策支持体系。 基于五路图腾方法论，提供从初筛到深度评估的全流程决策支持。 强调：AI辅助决策，人类最终决策。安全第一，成本可控。
unified-meeting-suite
by egbertieUnified meeting management and productivity suite. Replaces effective-meeting, weekly-meeting, meeting-to-action, ai-meeting-notes with single integrated interface. Use for: meeting scheduling, agenda management, note-taking, action items tracking, meeting analytics.
unified-governance-suite
by egbertieUnified governance and oversight suite. Replaces decision-governance, workspace-integrity-guardian, continuous-improvement-engine with single integrated interface. Use for: governance oversight, workspace integrity, continuous improvement, compliance monitoring.
iterative-research-optimizer
by egbertie迭代研究优化器 - 自动执行研究内容的三轮自我修正： 1. 全局考虑：覆盖信息饱和、逻辑强化、表达精炼三个维度 2. 系统考虑：输入→修正→验证→输出完整闭环 3. 迭代机制：三轮递进式优化，每轮有明确检查点 4. Skill化：标准接口，可对接任何研究输出 5. 流程自动化：全自动三轮迭代，无需人工介入
academic-deep-research
by egbertie学术级深度研究标准Skill - 满足5个标准： 1. 全局考虑：六层模型全覆盖（范式→情报→证据→多维→逻辑→迭代） 2. 系统考虑：研究设计→执行→验证→报告完整闭环 3. 迭代机制：三轮自我修正循环，持续优化研究质量 4. Skill化：标准SKILL.md格式，可安装可调用 5. 流程自动化：六层检查自动执行，质量门控自动验证
autonomous-execution-system
by egbertie7×24小时自主推进体系 - 让项目在无人值守时也能持续推进 核心机制：每日晨报、小时协调、安全检查、周复盘、空闲学习 确保项目不停滞、时间全利用、持续迭代进化
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.