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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bkn-modeling-advisor
by UnicomAI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。
bkn-creator
by UnicomAI【首选技能】凡涉及知识网络的任何操作，优先使用 bkn-creator，不要直接触发 create-bkn。 bkn-creator 是 BKN 全生命周期管理器，负责流程路由识别、阶段门禁、子流程编排与结果回执。 覆盖新增、查找、更新、删除（CRUD）并采用渐进式执行。 触发词：创建知识网络、新建BKN、BKN建模、本体建模、对象类、关系类、动作类、 概念组、BKN文件、BKN push、BKN pull、对象类绑定、关系类映射、 对象类提取、关系类提取、实体关系抽取、 知识网络查询、知识网络更新、知识网络删除。 不应触发：仅查询平台功能、Agent 对话、Vega/Catalog 操作、 健康巡检、数据源连接器等非 BKN 生命周期操作时，应由 ontology-core 等其他技能处理。
find-skills
by UnicomAISearch and discover OpenClaw skills from various sources. Use when: user wants to find available skills, search for specific functionality, or discover new skills to install.
fullstack-dev
by UnicomAIFull-stack backend architecture and frontend-backend integration guide. TRIGGER when: building a full-stack app, creating REST API with frontend, scaffolding backend service, building todo app, building CRUD app, building real-time app, building chat app, Express + React, Next.js API, Node.js backend, Python backend, Go backend, designing service layers, implementing error handling, managing config/auth, setting up API clients, implementing auth flows, handling file uploads, adding real-time features (SSE/WebSocket), hardening for production. DO NOT TRIGGER when: pure frontend UI work, pure CSS/styling, database schema only.
pptx-generator
by UnicomAIGenerate, edit, and read PowerPoint presentations. Create from scratch with PptxGenJS (cover, TOC, content, section divider, summary slides), edit existing PPTX via XML workflows, or extract text with markitdown. Triggers: PPT, PPTX, PowerPoint, presentation, slide, deck, slides.
ontology-core
by UnicomAI操作 知识网络（BKN）— 构建知识网络、查询 Schema/实例、语义搜索、执行 Action。 操作数据源与数据视图 — 数据源连接与查询、原子/自定义视图浏览与 SQL 查询。 操作 Vega 可观测平台 — 查询 Catalog/资源/连接器类型、健康巡检。 当用户提到"知识网络"、"知识图谱"、"查询对象类"、"执行 Action"、 "数据源"、"数据视图"、"原子视图"、"Catalog"、"Vega"、 "健康检查"、"巡检"等意图时自动使用。
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
smart-data-analysis
by UnicomAI数据分析员工（Data Analyst Agent）的唯一总入口：凡与数据资产、取数、指标、表/视图、 治理职责、知识网络、统计或分析相关的问题，必须先经本 skill 做编排与路由，再进入找表或问数等子流程。 负责 kn 分域、上下文注入（accountId / date）、多候选 KN 时的 LLM 决策、 问数分支的 SQL 生成；与 smart-search-tables / smart-ask-data / ontology-core 的交接。 当用户提出任何数据类自然语言任务、或需在多条业务 KN 间切换时使用； 所有 ontology CLI 执行均委托 ontology-core 完成，本 skill 不直接执行 CLI。
smart-search-tables
by UnicomAI找表/找数端到端编排：在元数据型知识网络下用 ontology bkn object-type query 检索表/视图实例， 再在职责型知识网络下检索相关部门职责与治理边界，最后汇总为中文结论 （候选表 + 职责要点 + 下一步）。当用户问「表在哪、哪个视图、数据资产归属、谁负责这类数据」时使用。 所有 ontology CLI 执行均委托 ontology-core 完成；本 skill 不直接执行 CLI。
mcp2skill
by UnicomAI将 MCP Server 的工具列表转换为 Skill 格式的结构化 Markdown 文档
polymarket
by UnicomAIQuery Polymarket prediction markets. Check odds, find trending markets, search events, track price movements.
smart-ask-data
by UnicomAI问数端到端编排（native CLI 版）：从候选 KN 选定知识网络，用 bkn object-type 发现对象类与字段， 由编排层 LLM 生成 SQL，再由 ontology dataview query 执行取数； 最后输出中文结论与口径说明。 当用户需要指标、统计、趋势、SQL 取数或数据查询时使用。
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.