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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standard-replenish
by kweaver-aiDefault procurement order via ERP. Use when no substitute is available and supplier cannot expedite.
supplier-expedite
by kweaver-aiSend expedite request to supplier portal. Use only when the material's supplier has capability=expedite.
exp-session-echo
by kweaver-aiEcho back the agent input's session_id verbatim. Designed specifically for verifying session_id propagation through agent chains. Invoke this skill whenever a session_id is present in the agent input.
substitute-swap
by kweaver-aiSwap to substitute material via MES. Picks the best candidate using a multi-criteria scorer (Python).
archive-protocol
by kweaver-ai全局归档协议。只要任务需要写入任何文件（含 PLAN.md、报告、JSON 等归档物），必须按本技能执行 Session→ARCHIVE_ID、TIMESTAMP、双轨路径（根段须为 archives/）、回读校验与状态回执；WebUI 的 archive_grid 必须用 Markdown 中语言标识为 json 的围栏代码块输出。
schedule-plan
by kweaver-ai定时计划协议。仅当用户请求创建定时计划、定时任务、提醒、自动化安排、周期或延迟任务时生效；包含 ORA 拆解、PLAN.md 持久化、用户书面确认 PLAN 后再创建定时任务、已落地计划的修改须同步更新 PLAN.md、任务消息首要指令与 PLAN 模板。须与 archive-protocol 技能一并遵守。
data-quality
by kweaver-ai基于 Data View 和 Task Center API 的数据质量管理。管理质量规则、查询逻辑视图、创建检测工单。当用户需要数据质量相关操作时使用。
bkn-rules
by kweaver-ai从 PRD/对话/建模中提取业务规则，生成可复用的业务规则 Skill。必执行。
bkn-test
by kweaver-ai生成测试集与验证用例。三种模式：schema_review / rules_verification / qa_verify。
bkn-creator
by kweaver-aiBKN 全生命周期编排入口。自包含 KWeaver CLI 操作层（内化 bkn-kweaver）。 凡涉及创建 BKN、从 PRD/文档提取对象关系、 生成或更新 `.bkn`、做数据视图绑定、环境检查、测试集生成、校验与推送时， 优先使用 bkn-creator 进行流程路由、阶段门禁、子 skill 编排与结果回执。 适用于 BKN 的 create/read/update/delete/extract/copy/validate 场景， 技能包能力补齐/skill 草案生成场景， 以及使用反馈巡检与改进场景（定时任务触发、Agent 对话质量异常、 feedback_brief 传入、知识网络持续优化）。 不应用于纯数据语义查询，该场景应交由 data-semantic 处理。
bkn-modeling-advisor
by kweaver-ai指导业务知识网络（BKN）建模，输出符合 BKN 2.0.0 的对象类型、关系类型、操作类型、风险类型与概念分组定义。适用于用户提出本体设计、知识网络建模、实体关系梳理、Action 设计、Schema 评审、从文档提取初稿或扩展现有 BKN 的场景。
data-semantic
by kweaver-ai数据语义服务 API - 提供表单视图的语义理解功能。 用于: (1) 查询字段语义和业务对象识别结果 (2) 触发/批量理解表单视图 (3) 批量业务对象匹配
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.