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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pre-release
by ZhuoZhuoCrayonAutomates release preparation for throttled-py. Triggers when user message contains: - "release vX.Y.Z" with a GitHub release draft URL - "release vX.Y.Z" followed by changelog content Tasks: update version numbers, sync CHANGELOG_EN.rst and CHANGELOG.rst, run dependency sync.
code-style
by ZhuoZhuoCrayon统一通用代码风格与实现约束：按目标项目规范（AGENTS.md / CONTRIBUTING.md）与语言参考约束命名、格式、复杂度与错误处理。 编写、修改、评审任意语言源码或迁移代码规则时使用，交付代码前据此确保改动符合项目与工作区风格。
dev-env
by ZhuoZhuoCrayon准备和修复 Go、Node.js、Python 本地开发环境。 用于版本切换、工具链缺失、依赖环境异常、虚拟环境问题，以及编译、测试、检查命令中的环境类失败。
doc-style
by ZhuoZhuoCrayon结构化编写、重构、润色和验收 Markdown / MDC 文档。 只要用户要创建、编辑、润色、改写、评审或整理任何 `.md` / `.mdc` 文件， 或需要整理规则文档、普通说明文档、PR review 评论、GitHub 评论、零散草稿，就应使用这个 skill。
git-workflow
by ZhuoZhuoCrayonGit 提交规范。 用户要求提交、commit、拆分提交、起草 commit message、整理待提交范围或处理暂存区时必须使用。 用于区分工作区知识库提交与非工作区项目提交：默认按工作区规范，用户明确要求提交其他项目时遵循项目自身规范。
code-review
by ZhuoZhuoCrayon执行代码审查、PR review、复查，或在处理未解决 review threads、发 review 评论、request changes、approve 前必须使用。 审查范围覆盖 GitHub PR diff、本地变更、相关源码、测试覆盖、项目规范与既有评论。 当用户以方案文档（PLAN.md）配合 PR 发起 review 时，额外核对实现与方案一致性，并在 review 阶段回写方案。 回写方案包括同步版本锚点，以及在实现更优或方案过期时主动询问是否更新方案。 默认只输出对话草稿，仅在明确授权后发布评论或回写方案。
knowledge-mgr
by ZhuoZhuoCrayon管理工作区知识对象的检索与全生命周期操作，路径涉及 `knowledge/`，知识对象包括 issue（需求）、plan（方案 / 计划）、snippet（代码片段）、article（文章）和 troubleshooting（排障经验）。 当用户询问事项进展、最近做了什么、周报 / 日报 / 总结素材，或者问题依赖历史结论、过往决策时，应优先使用本 skill。 当用户要求将结论沉淀到知识对象体系，或者要求检索、创建、修改、归档、迁移知识对象时，应使用本 skill 执行相关操作。 外部资料调研、纯代码实现 & 调试 & 测试等无需落地知识对象的行为不属于本 skill 场景。
project-mgr
by ZhuoZhuoCrayon通过 repos.json 注册和管理工作区中的项目。 当用户想要接入项目、移除项目、询问项目设置规范，或消息中提到已注册项目名并需要定位 local_path 时使用。
work-schedule
by ZhuoZhuoCrayon需求排期录入工具——将工作事项同步到企业微信智能表格。 当用户提到"加入排期"、"录入排期"、"更新排期表"、"需求排期"、"把 xxx 加到排期"等意图时触发。 也适用于用户提供新周期 schema/key 要求配置新文档的场景。
iwiki-sync
by ZhuoZhuoCrayon同步 AI 工作区文档到 iWiki、从 iWiki 回写本地、做日常增量对齐时使用。 只要用户提到“同步到 iWiki”“从 iWiki 拉取”“双向更新”“补齐映射”“重传文档”“个人空间目录对齐”，都应立即使用本 Skill。
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.