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...
gsap
by codingSamss官方 GSAP 前端动效聚合 skill。用于实现或审查 JavaScript/React/Next/Vue/Svelte 动画、GSAP tweens/timeline、ScrollTrigger 滚动动画、插件、性能优化、reduced motion 与可访问性动效；默认按任务读取 references/ 中的官方 GreenSock GSAP skills。健康/长辈类产品默认使用克制动效。
mx-message-query
by codingSamssQuery Midea MX / 美信 local message cache through the MX local HTTP query service from Codex. Use when the user asks to read MX sessions, search chat history, search messages globally or inside a group/session, list recent messages, or page message history. This is read-only and does not require send authorization. Never fall back to reading SQLite or app cache files directly.
mx-im
by codingSamssSafely search MX users or groups and send Midea MX / 美信 IM messages from Codex. Use when the user asks to notify someone, send a message to a person or group, use a configured group alias, @ users, @ all, or send MX file/image messages. Read lookups need no extra authorization; every live send needs explicit user authorization for that exact target and message.
mx-channel-rules
by codingSamssMX channel output rules. Always active in MX conversations.
ian-xiaohei-illustrations
by codingSamss生成“小黑 + 真实物件 + 物理动作 + 留白叙事”的中文配图。默认输出 16:9 正文配图，用于互联网打工人共鸣图、AI 时代职场焦虑、程序员/产品/创作者处境和正文观点隐喻图；遇到“彩蛋模式 / 长卷故事图 / 超横版 / 个人经历 / 项目复盘 / 产品演化 / 成长路径”时，输出小黑长卷故事图。标准模式默认 #FFFFFF 纯白背景；彩蛋长卷模式使用高级近白背景和一条真实物件人生线。样例是高质量模板母版和出图质量标尺；必须对齐其比例、留白、动作清晰度和叙事关系，但不能复刻其物件组合、空间拓扑、小黑姿态或标签位置。
Read Reddit content via Composio MCP. Actions: search posts, view hot/top/new posts, read post content, read comments. Keywords: reddit, subreddit, post, comment, search reddit, hot posts, top posts.
linuxdo
by codingSamssRead LINUX DO forum content via Discourse JSON API + Chrome Cookie auth. Actions: check login, latest topics, top/trending, full-text search, read topic details, browse categories. Keywords: linuxdo, linux.do, l站, 帖子, 搜索, 最新, 热门, 分类, discourse, forum.
linuxdo
by codingSamssRead LINUX DO forum content via Discourse JSON API + Chrome Cookie auth. Actions: check login, latest topics, top/trending, full-text search, read topic details, browse categories. Keywords: linuxdo, linux.do, l站, 帖子, 搜索, 最新, 热门, 分类, discourse, forum.
screenshot
by codingSamssUse when the user explicitly asks for a desktop or system screenshot (full screen, specific app or window, or a pixel region), or when tool-specific capture capabilities are unavailable and an OS-level capture is needed.
fireworks-tech-graph
by codingSamssUse when the user needs technical/system diagrams or image generation with structured, editable SVG output and optional PNG export: architecture, flowchart, sequence, swimlane, data flow, ER/state-machine, agent/memory, or concept map. Trigger on: "架构图" "流程图" "时序图" "泳道图" "系统图" "数据流图" "sequence diagram" "flowchart" "swimlane" "ER diagram" "state machine" "SVG diagram" "生成图片" "画图" "生图".
fireworks-tech-graph
by codingSamssUse when the user needs technical/system diagrams or image generation with structured, editable SVG output and optional PNG export: architecture, flowchart, sequence, swimlane, data flow, ER/state-machine, agent/memory, or concept map. Trigger on: "架构图" "流程图" "时序图" "泳道图" "系统图" "数据流图" "sequence diagram" "flowchart" "swimlane" "ER diagram" "state machine" "SVG diagram" "生成图片" "画图" "生图".
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.