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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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ooda-loop
by jackjin1997Use when decisions must happen under time pressure or in adversarial/changing conditions — production incidents (P0, on-call, live debugging, rollback choices), competitive moves, real-time negotiations, anything where the situation reacts to your action. Triggers on Chinese phrases 事故 / P0 / 故障 / 排障 / oncall / 救火 / 谈判 / 实时 / 紧急 / 来不及, and English signals "incident", "outage", "live", "real-time", "the other side just". Especially when information is arriving while you must respond, when 5-minute decision cycles beat 1-hour ones, or when you catch yourself wanting to "analyze more" while the situation degrades. Do NOT use for slow life decisions (use wrap), for problems where information is already complete (use eisenhower-matrix or weighted decision matrix), or for Cynefin Clear-domain SOPs.
inversion
by jackjin1997Use when user pursues an abstract goal where the path to success is unclear but the path to failure is concrete — '想做出好产品' / '想长寿' / '想升 staff' / '战略方向' / '怎么变得更好'. Triggers Munger's "invert, always invert" mental model: ask 'how do I GUARANTEE failure?' then avoid those. Also use when user is stuck brainstorming positive actions ('should I do X or Y'), facing analysis paralysis on abstract criteria, or when industry advice gives contradictory positive recommendations. Especially powerful for investment decisions, hiring criteria, product PRDs, strategic direction. Do NOT use for concrete quantifiable goals (use OKR), low-cost trial decisions (just try), or in teams already in self-blame mode (will be heard as criticism).
langgraph-fundamentals
by jackjin1997INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph creation, node functions, edges, state schemas with reducers (Annotated), and the Command API.
langgraph-persistence-memory
by jackjin1997INVOKE THIS SKILL when your LangGraph needs to remember state across calls, use memory, or persist conversations. Covers checkpointers (MemorySaver, Postgres), thread_id configuration, and Store for long-term memory.
langgraph-execution-control
by jackjin1997INVOKE THIS SKILL for LangGraph workflows, parallel execution, interrupts, or streaming. Covers Send API for fan-out, interrupt() for human-in-the-loop, Command for resuming, and stream modes (values/updates/messages).
bezos-frameworks
by jackjin1997Two complementary Bezos heuristics in one skill. (A) Two-Way Door — classify decisions by reversibility; reversible = decide fast with 70% info, irreversible = decide slow with 90% info. (B) Regret Minimization — for life-defining choices, project to age 80 and pick what minimizes regret. Use Two-Way Door triggers on Chinese 决策瘫痪 / 反复纠结小事 / 开了三次会还没定 / 大事小事一样慢; Regret Min triggers on 离职 / 创业 / 移民 / 结婚 / 生育 / 转行 / 重大决定 / 这辈子. Especially when team applies identical heavy process to all decisions (need Two-Way), or when user faces once-in-a-decade pivot where rational analysis ties (need Regret Min). Do NOT misclassify One-Way as Two-Way (most expensive mistake), use Regret Min for daily decisions (age-80 view on "what to eat" is meaningless), or run Regret Min in heated emotion (cool 24-48h first to avoid romanticizing risk).
cynefin
by jackjin1997Use BEFORE selecting any other decision framework — Cynefin (kuh-NEV-in) classifies WHICH framework fits the current situation across 5 domains (Clear / Complicated / Complex / Chaotic / Confused). Triggers when user is about to apply a method and the fit feels off, asks meta-questions like "should we follow SOP or explore?", or when same approach that worked last time seems wrong now. Also use after failure when "the method was right but the situation didn't match", or when team argues over deterministic-plan-vs-experimentation. Especially valuable at the start of major decisions to avoid using the wrong hammer for the nail. Do NOT use for trivial classified decisions (don't run Cynefin for "what to eat for lunch"), true Chaotic situations needing immediate stabilizing action (act first, classify later), or teams unfamiliar with the model (use simpler known/unknown binary).
douban
by jackjin1997豆瓣电影、图书查询与个人书单/影单管理。当用户提到豆瓣、想看/在看/看过、书单、影单、电影评分、图书评分、Top250、豆列时使用。
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.