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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mindos
by GeminiLightMindOS: local knowledge assistant & shared KB. Keeps decisions, notes, SOPs, debugging lessons, research findings, preferences across sessions/agents. Core: save notes, search KB, organize files, run workflows, review, append CSV, hand off context, distill lessons. NOT for app source or paths outside KB. Triggers: save/record, search notes, update files, organize, run workflow, capture decisions, append CSV, hand off context, check past discussions, distill lessons. Proactive: (1) search first for stored context, (2) offer to save after valuable work, (3) suggest persisting key decisions.
mindos-zh
by GeminiLightMindOS 是用户的本地知识助手,也是跨会话、跨 Agent 共享的知识库。它保存决策记录、会议纪要、SOP、 排错经验、架构选型、调研结论和偏好设置。 仅 mindRoot 知识库内任务。不用于:改代码仓库、项目源码、KB 外路径。 核心概念:空间、指令(INSTRUCTION.md)、技能(SKILL.md);笔记可承载指令与技能。 触发场景:保存或记录任何内容、搜索历史笔记或上下文、更新或编辑文件、整理或重组文件结构、 执行SOP或工作流、捕获对话中的决策、复盘或总结经验、追加表格或CSV数据、跨Agent交接上下文、 提炼经验教训、同步关联文档、查找之前是否讨论过某事、查询历史决策、查找模板或SOP、 为会议准备上下文、快速保存到收集箱、整理收集箱、知识健康检查、检测知识冲突或矛盾、 查找过期内容。 触发词:帮我记下来、搜一下我的笔记、更新知识库、整理文件、执行工作流、保存到知识库、 记录一下、整理笔记、复盘、提炼经验、同步文档、查一下之前的记录、有没有相关的笔记、 我之前写过什么、把这个存起来、总结一下保存、交接给其他Agent、追加到表格、 放到收集箱、整理收集箱、知识健康检查、检测知识冲突。 主动行为——不要等用户提到 MindOS: (1) 用户的问题暗示可能存在历史记录(过去的决策、之前的讨论、会议纪要)→ 先搜 MindOS, 即使没有明确提到知识库。 (2) 完成有价值的工作后(修了 bug、做了决策、总结了经验、选定了架构、开完了会)→ 主动提议保存到 MindOS,方便以后查阅。 (3) 经历了较长或多主题的对话后 → 建议把关键决策和上下文持久化。
find-skills
by GeminiLightHelps 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.
mindos-max
by GeminiLightMindOS: local knowledge assistant & global memory layer. Keeps decisions, notes, SOPs, debugging lessons, architecture choices, research findings, preferences, conversation summaries for all connected agents. PROACTIVE: (1) search MindOS first for past context, (2) offer to save after valuable work, (3) persist key context on handoff/end. Triggers: save/record, search notes, update files, organize, run SOP/workflow, review, append CSV, hand off, distill lessons, sync docs, check past decisions, find templates, prepare meeting context, log daily, track goals. NOT for app source or paths outside KB. Core: Space, Instruction, Skill.
mindos
by GeminiLightOperate a MindOS knowledge base: update notes, search, organize files, execute SOPs/workflows, retrospective, append CSV, cross-agent handoff, route unstructured input to the right files, distill experience, sync related docs. Use when the task targets files inside the user's MindOS KB (mindRoot). NOT for editing app source, project docs, or paths outside the KB. Core concepts: Space, Instruction (INSTRUCTION.md), Skill (SKILL.md); notes can embody both. Trigger when user asks to: save or record a note, search their knowledge base, update or edit a file, organize notes, run a workflow or SOP, capture decisions from a session, append rows to a table or CSV, hand off context to another agent.
mindos-max-zh
by GeminiLightMindOS 是用户的本地知识助手,也是所有已连接 Agent 共用的记忆层。 决策记录、会议纪要、SOP、排错经验、架构选型、调研结论、偏好设置、对话摘要都保存在这里。 主动使用——不要等用户说"保存到知识库": (1) 回答任何关于过去决策、讨论或上下文的问题前 → 先搜 MindOS。 (2) 产出有价值的内容后(做了决策、修了 bug、总结了经验、开完会)→ 主动提议保存。 (3) 交接给其他 Agent 或结束会话时 → 把关键上下文持久化到 MindOS。 以下场景全部触发,即使用户没提到"MindOS"或"知识库":保存/记录任何内容、搜索历史笔记、 更新文件、整理/重组、执行SOP/工作流、复盘、追加CSV/表格、跨Agent交接、提炼经验、 同步关联文档、查之前是否讨论过、查历史决策、找模板、准备会议资料、写日记、追踪进度。 触发词:帮我记下来、搜一下笔记、更新知识库、整理文件、复盘、提炼经验、保存、记录、 交接、把这个存起来、查一下之前的、有没有相关笔记、我之前写过什么、总结一下保存、 追加到表格、更新进度、查找模板、准备会议资料、记录今天的工作、 放到收集箱、整理收集箱、知识健康检查、检测知识冲突。 拿不准是否该用——大概率该用。查一下不会错。 不用于:改代码仓库、项目源码、KB 外路径。 核心概念:空间(Space)、指令(INSTRUCTION.md)、技能(SKILL.md)。
mindos
by GeminiLightMindOS: local knowledge assistant & shared KB. Keeps decisions, notes, SOPs, debugging lessons, research findings, preferences across sessions/agents. Core: save notes, search KB, organize files, run workflows, review, append CSV, hand off context, distill lessons. NOT for app source or paths outside KB. Triggers: save/record, search notes, update files, organize, run workflow, capture decisions, append CSV, hand off context, check past discussions, distill lessons. Proactive: (1) search first for stored context, (2) offer to save after valuable work, (3) suggest persisting key decisions.
mindos-zh
by GeminiLightMindOS 是用户的本地知识助手,也是跨会话、跨 Agent 共享的知识库。它保存决策记录、会议纪要、SOP、 排错经验、架构选型、调研结论和偏好设置。 仅 mindRoot 知识库内任务。不用于:改代码仓库、项目源码、KB 外路径。 核心概念:空间、指令(INSTRUCTION.md)、技能(SKILL.md);笔记可承载指令与技能。 触发场景:保存或记录任何内容、搜索历史笔记或上下文、更新或编辑文件、整理或重组文件结构、 执行SOP或工作流、捕获对话中的决策、复盘或总结经验、追加表格或CSV数据、跨Agent交接上下文、 提炼经验教训、同步关联文档、查找之前是否讨论过某事、查询历史决策、查找模板或SOP、 为会议准备上下文、快速保存到收集箱、整理收集箱、知识健康检查、检测知识冲突或矛盾、 查找过期内容。 触发词:帮我记下来、搜一下我的笔记、更新知识库、整理文件、执行工作流、保存到知识库、 记录一下、整理笔记、复盘、提炼经验、同步文档、查一下之前的记录、有没有相关的笔记、 我之前写过什么、把这个存起来、总结一下保存、交接给其他Agent、追加到表格、 放到收集箱、整理收集箱、知识健康检查、检测知识冲突。 主动行为——不要等用户提到 MindOS: (1) 用户的问题暗示可能存在历史记录(过去的决策、之前的讨论、会议纪要)→ 先搜 MindOS, 即使没有明确提到知识库。 (2) 完成有价值的工作后(修了 bug、做了决策、总结了经验、选定了架构、开完了会)→ 主动提议保存到 MindOS,方便以后查阅。 (3) 经历了较长或多主题的对话后 → 建议把关键决策和上下文持久化。
plugin-core-builtin-migration
by GeminiLight将 MindOS 渲染器插件从“可选插件”升级为“完全内置能力(core builtin)”的通用流程。 当用户要求“把某插件改成内置/核心”“插件不应可关闭”“插件主程序化”或需要将旧入口迁移到 新主流程并保留兼容迁移能力时触发。
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.