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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alfworld-object-storer
by taomiaoThis skill places an object into a selected storage receptacle after confirming its suitability. It should be triggered when the agent has identified an appropriate storage location and is ready to complete the storage task. The skill takes the object and target receptacle as inputs and results in the object being stored.
time-and-motion-in-the-home
by TibsfoxApplying motion study and time analysis to household work. Based directly on Lillian Gilbreth's *The Home-Maker and Her Job* and *Cheaper by the Dozen*, this skill covers the therblig-motion catalog, task decomposition, batch processing, parallelism, routine chart design, and the ergonomics of work surfaces. Use when diagnosing why a household is running out of time, designing a weekly routine, teaching task sharing to children, or reducing the friction of any repeated household task.
adhd-task-management
by diegosouzapwADHD-optimized task tracking and intervention system. Use when tracking tasks, detecting context switches, providing accountability interventions, or managing ADHD-specific productivity patterns for Ariel Shapira.
adhd-task-management-skill
by diegosouzapwADHD-optimized task tracking with abandonment detection, intervention strategies, and completion accountability
habit
by shikidmsh-rgb习惯打卡 — 需要长期坚持并追踪'做了没有'的事（如运动、喝水、学习）。
todo
by shikidmsh-rgb一次性待办 — 需要被追踪催促直到完成，但做完就结束不会再来的事（如买猫粮、约牙医、查资料）。用户回报完成某事且不属于活跃习惯时也走这里。
good-morning
by debs-obrienA skill that responds to good morning with a cheerful greeting
eldercare-companion
by nclamvnBạn đồng hành AI cho bà nội. Gồm 4 chức năng: A) Phát nhạc xưa (bolero, cải lương) qua loa phòng bà B) Đọc truyện bằng TTS tiếng Việt, nhớ vị trí đọc dở C) Nhắc sinh hoạt mỗi 2 giờ (uống nước, đổi tư thế, ăn nhẹ) D) Nhận voice command đơn giản từ bà (giọng yếu, fuzzy match) Mọi audio output đều VOLUME CAO (bà nặng tai). TTS tốc độ chậm hơn bình thường (rate 0.8).
adhd-copilot
by dsx0511ADHD友好型交互助手，同时在科研工作中作为思维监测层运行。触发场景包括： (1) 日常ADHD场景：需要开始做某件事但迟迟无法行动、沉迷当前活动难以停止、 对即将到来的安排感到焦虑或反复想、需要制定计划或日程、感到拖延或内耗、 需要把大任务拆解、想要时间管理建议。关键词：ADHD/注意力/专注/分心/拖延/执行力/ "不想动""停不下来""一直在想""做不了""内耗""焦虑某个安排"。 (2) 科研ADHD监测场景（与research-copilot联动）：当用户在科研讨论中频繁跳转话题、 在一个方向未收敛时就打开新方向、积累了过多未闭合的探索线程、或明显从深度思考 滑入浅层浏览模式时，此skill应同时触发以提供思维梳理。注意：在科研场景中用英文交互。 (3) 自适应学习由配套的adhd-profile-tracker skill处理——它会持续观察用户的ADHD 行为模式，维护用户画像文件，并据此调整介入策略和建议方式。 核心原则：永远不说教，永远提供具体可执行的微小行动，永远以温暖和理解的态度回应。 在科研场景中，尊重创造性发散，只在发散失控时介入。
proactive
by kid0317主动唤醒 SOP。由定时任务触发，判断是否向用户发送主动关心消息。 包含发送条件检查、消息类型选择、角色声音生成、飞书发送。
healthcheck
by bighardpersonTrack water and sleep with JSON file storage.
wilma-triage
by dvcrnDaily triage of Wilma school notifications for Finnish parents. Fetches exams, messages, news, schedules, and homework — filters for actionable items, syncs exams to Google Calendar, and reports via chat. Requires the `wilma` skill and `gog` CLI (or `gog` skill from ClawHub) for calendar access.
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.