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...
map-navigate
by XiaoLuoLYGMove toward a requested target location or perform a requested interaction if already there.
tw-ecom-logistics-cold-chain
by asgard-ai-platformShip refrigerated / frozen products in Taiwan — 宅配通 宅配通冷藏 / 黑貓宅急便 低溫, cold-chain CVS pickup limitations, packaging (保冷劑 / 乾冰), and shelf-life SLA. Use for 生鮮 / 冷凍 / 冰品 / 藥品 delivery. Do NOT use for ambient-temperature shipping. STATUS: SKELETON — body pending.
amap-navigator
by wulaosijiChinese navigation and location service powered by Amap (Gaode Maps / 高德地图). Provides geocoding, routing, POI search, and distance calculations for addresses and coordinates within mainland China. Use when: "高德地图", "Amap导航", "中国地图", "Chinese navigation", "路线规划", "POI搜索", "geocoding China", "地址解析", "distance matrix", "location query". Cross-references: pitch-deck-creator, media_hub. Built by UniqueClub 🌐 https://uniqueclub.ai
gmaps-cli
by Mic92Search for places and get directions using Google Maps. Use for finding locations, nearby places, and route planning.
plan-tour-route
by pjt222謀多停之行徑：優化途點、估駕/行之時、用 OSM 數據沿途發掘 POI。 含地理編碼、近鄰與 TSP 序、時/距矩算、生附景點之行程。 謀多目地之公路行或徒步行、優訪序減旅時、發掘沿途之地、 比駕/行/公共交通之選時用之。
new-zealand
by clawicDiscover New Zealand with island-aware routing, practical road-trip logistics, outdoor safety, and local food and region guidance.
route-assistant
by LJT-520Intelligent route planning assistant with optimized display. Three-part output: transport options table, recommended schemes, and important reminders.
ems-express
by warfieldodakaclaude8190-sourceEMS快递信息查询与在线服务。查询快递物流、寄件预约、网点查询、价格查询、快递常识、在线客服、地址记忆管理；支持快递单号查询、查进度、寄件预约、地址管理。
monitorando-entregas
by Rafael-2109Esta skill deve ser usada quando o usuario pergunta sobre entregas ja faturadas: "NF 12345 foi entregue?", "status da entrega do Atacadao", "que dia embarcou?", "quando faturou?", "tem canhoto?", "houve devolucao?", ou precisa de datas de embarque, faturamento, entrega e canhotos. Nao usar para pedidos ainda nao faturados (usar gerindo-expedicao), rastrear NF no Odoo (usar rastreando-odoo), ou visao 360 completa do pedido (usar subagente raio-x-pedido). - Canhoto: "tem canhoto da NF?", "canhotos pendentes" - Devolucoes: "houve devolucao?", "NFs devolvidas", "produtos mais devolvidos" - Pendencias: "entregas pendentes", "NFs no CD", "entregas com problema" - Custo devolucao: "quanto custou as devolucoes?" NAO USAR QUANDO (ANTES de faturar): - Pedidos em carteira/separacao → usar **gerindo-expedicao** - Estoque, disponibilidade → usar **gerindo-expedicao** - Criar separacao → usar **gerindo-expedicao** - Rastrear NF no Odoo → usar **rastreando-odoo**
segway-delivery
by 200815147一站式配送任务编排。通过楼宇名称和站点名称查询楼宇和站点信息，然后通过 segway-stage 起草配送任务等待人工审批。适用于"送东西到3楼大厅"、"带我去前台"、"从仓库取件送到302"等自然语言指令。读操作（status、list-areas）直接执行，写操作（guidance、take-deliver）必须通过 segway-stage 起草。
mobile-navigation-agent
by aemdemosAnalyzes and validates mobile header/nav behavior for AEM EDS. Detects breakpoints, hamburger logic and animation (hamburger → cross), accordion vs drawer, tap vs hover, mobile megamenu behavior, and menu items width (full-width flush vs centered with margins — menuItemsWidthLayout). Follows same validation rigor as desktop (structural + style registers). Use only with mobile screenshot evidence after desktop is confirmed. Invoked by excat-navigation-orchestrator Phase 4. Do NOT use for desktop-only analysis or without mobile screenshot.
estate-and-garage-sale-fetcher-and-route-planner
by AgentPMTUse AgentPMT external API to run the published workflow Estate and Garage Sale Fetcher and Route Planner with wallet signatures and credits.
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.