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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meta-theory
by KimYx0207Meta_Kim executable governance dispatcher. It classifies the run, loads only needed references, preserves foundational capabilities and runtime-native abilities, routes owner + weapon + dependency + runtime + OS + verification, and closes only with evidence, intent acceptance, and writeback decision.
same-set-reusable-flow-for-project-file-inventor
by KimYx0207Reusable Meta_Kim file inventory classification flow. It helps separate durable sources, generated evidence, runtime mirrors, temporary state, and risky unknowns before cleanup or commit.
kim-orchestrator
by KimYx0207多AI协作编排技能 - 自动协调Claude、Codex、Gemini完成需求分析→代码生成→代码审查工作流。 【核心触发场景】 - 开发任务、写代码、实现功能、编程任务 - 新功能开发、功能实现、代码实现 - 重构、优化代码、代码改进 - Bug修复、问题修复、错误处理 - 系统设计、架构设计、模块设计 【显式触发词】 - "AI编排"、"ai编排"、"多AI协作"、"三引擎"、"三AI" - "codex"、"gemini"、"kim team"、"kim-team" - "协作开发"、"编排流程" 【任务类型触发】 - 实现XXX功能、开发XXX模块、写一个XXX - 创建XXX、构建XXX、搭建XXX - 添加XXX功能、新增XXX特性 - 修改XXX、更新XXX、升级XXX - 修复XXX、解决XXX问题 - 重构XXX、优化XXX性能 - 设计XXX架构、规划XXX系统 【技术任务触发】 - API开发、接口实现、后端开发、前端开发 - 数据库设计、表结构、CRUD操作 - 用户认证、登录注册、权限控制 - 文件处理、数据导入导出 - 第三方集成、SDK接入
agent-teams-playbook
by KimYx0207Cross-runtime Agent Teams orchestration playbook for Claude Code, Codex, OpenClaw, and Cursor. This skill should be used when the user asks to "create agent teams", "use agent swarm", "setup multi-agent collaboration", "orchestrate agents", "coordinate parallel agents", "organize team collaboration", "build agent teams", "implement swarm orchestration", "setup multi-agent system", "coordinate agent collaboration", or needs guidance on adaptive team formation, quality gates, skill discovery, task distribution, team coordination strategies, or Agent Teams best practices. 或者当用户说"多agent"、"agent协作"、"agent编排"、"并行agent"、"分工协作"、"拉团队"、"拉个团队"、"多代理协作"、"swarm编排"、"agent团队"时也应使用此技能。Note: "swarm/蜂群" is a generic industry term; Claude Code's official concept is "Agent Teams"; Codex, OpenClaw, and Cursor should map this playbook to their native or host-provided agent capabilities without deleting any workflow stage.
find-skills
by KimYx0207Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
code-security
by KimYx0207Runs Semgrep security scans on the current project to detect vulnerabilities, secrets leakage, and OWASP Top 10 issues. Use when the user asks for security scanning, vulnerability detection, code auditing, secrets checking, or says things like 安全扫描, 代码扫描, 扫漏洞, 安全检查, 漏洞检测, 扫一下安全.
laojin
by KimYx0207Use when the user asks for KIM, Kim, laojin, 老金, 问问老金, 老金怎么看, asks for decision analysis, structured reasoning, product/business/content review, PRD, MVP, user path, growth, monetization, pricing, client delivery, revenue, cost, scope, strategy, retrospectives, or a concrete plan with pass conditions. Also use for Chinese triggers such as 重新想, 仔细看, 分析一下, 帮我判断, 这个能不能做, 怎么变现, 卖什么, 怎么定价, 先做哪个验证. Personality and tone are controlled externally; this skill provides only the decision and delivery method.
laojin
by KimYx0207Use when the user asks for KIM, Kim, laojin, 老金, 问问老金, 老金怎么看, asks for decision analysis, structured reasoning, product/business/content review, PRD, MVP, user path, growth, monetization, pricing, client delivery, revenue, cost, scope, strategy, retrospectives, or a concrete plan with pass conditions. Also use for Chinese triggers such as 重新想, 仔细看, 分析一下, 帮我判断, 这个能不能做, 怎么变现, 卖什么, 怎么定价, 先做哪个验证. Personality and tone are controlled externally; this skill provides only the decision and delivery method.
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.