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...
yume-release-readiness
by yume-infraReview create-yume release readiness by comparing the current dev branch against main, checking implementation, tests, generated smoke, docs/spec/user knowledge, dependency state, and release-note risk, then producing a blocker-first release checklist with verification evidence.
ym-aiming
by yume-infra当用户已经给出初始 aim，但后续对话、临时想法或新请求可能偏离最初目标时使用；提醒 aim drift 并让用户选择继续、切换或暂存。不用于开启专注执行循环、替用户设 goal、或阻止用户主动改目标。
ym-handoff
by yume-infra当需要在 compact 前创建或更新 handoff 文档、用户显式要求交接/暂停/transfer 时使用；compact 后恢复已有 handoff 时不要触发，直接读 active handoff 文档。不用于普通总结、review artifact、promote 或 pick。
ym-harness-smoke
by yume-infra创建或修改 AI harness skill、contexta pack、repo-local Codex skill、plugin export 或 workflow helper script 后使用；跑最小真实验证并报告可用命令和粗糙点。不替代大范围代码变更后的 full CI。
ym-harness-wire
by yume-infra当需要把 agent behavior、workflow shortcut 或粗糙 prompt pattern 接成 repo-local Codex skill 或 contexta 分发的 AI harness 工具时使用；优先走现有 contexta export 和 skill validation。不用于普通 prose prompt 或 unsupported runtime surface。
ym-loop-automate
by yume-infra当手工 repo workflow、harness validation step 或重复 agent action 需要变成 command、script、package script、hook 或 exported skill 时使用；只有循环重复或易错时才自动化。不自动化一次性 theory work。
ym-probe-loop
by yume-infra当用户的需求、目标或设计方向还没想清楚，需要 agent 探索上下文、拆 decision tree、给 recommended answer 并协同收敛决策时使用；方向明确后移交 push-loop。不用于用户已有明确方向只需同步口径或直接实现的情况。
ym-push-loop
by yume-infra当用户已有明确决策方向，但复杂理论、task 工作面或多轮纠偏需要持续同步口径到完全一致时使用；用 relentless loop 组织 draft、review、实现和验证。需求方向未定时先用 probe-loop。
ym-requirement-cut
by yume-infra当需求、计划、workflow 或工具想法过宽、过理论、流程过重时使用；先质疑、删除、简化，再压成最小可执行 slice。不用于 slice 已清楚后的普通 code review 或验证。
ym-ship-slice
by yume-infra当用户要把零散 theory、想法或产品方向立刻转成粗糙实现时使用；做最小可执行 repo change，只记录必要工作材料，并用最快真实命令验证。不用于长期 architecture planning。
ym-skill-improver
by yume-infra当用户点名 skill-improver 并给出一个 target skill 或指出现有 skill 的 trigger、读取策略、boundary、workflow 偏差时使用；把目标 skill 改成最小可验证 patch。不用于从零创建新 skill 或普通 code review。
aiming
by yume-infra当用户已经给出初始 aim，但后续对话、临时想法或新请求可能偏离最初目标时使用；提醒 aim drift 并让用户选择继续、切换或暂存。不用于开启专注执行循环、替用户设 goal、或阻止用户主动改目标。
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.