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...
hk-holidays
by samsonllamQuery Hong Kong public holidays and calendar events from the 1823 government hotline. Use when the user asks about public holidays in Hong Kong, when the next holiday is, whether a specific date is a holiday, long weekends, or festival dates. Covers all gazetted public holidays in English and Chinese.
hk-mortgage
by samsonllamQuery Hong Kong residential mortgage statistics from the Hong Kong Monetary Authority (HKMA). Use when the user asks about mortgage rates, property loan data, housing loan statistics, mortgage approval numbers, or the state of the Hong Kong property lending market.
hk-facilities
by samsonllamSearch Hong Kong public sports and leisure facilities from the Leisure and Cultural Services Department (LCSD). Use when the user asks about sports centres, gyms, swimming pools, parks, courts, or any public recreational facility in Hong Kong. Covers 116+ venues across all 18 districts.
hk-finance
by samsonllamQuery Hong Kong financial and monetary data from the Hong Kong Monetary Authority (HKMA). Use when the user asks about HKD exchange rates, HIBOR interest rates, interbank liquidity, aggregate balance, Hong Kong monetary statistics, or financial market data. Provides daily monetary statistics and market data.
hk-geodata
by samsonllamSearch and look up Hong Kong geographic locations, addresses, landmarks, and places using the GeoData API. Use when the user asks about locations in Hong Kong, needs to find an address, wants to know which district a place is in, or needs coordinates for any HK location. Also useful for resolving place names to coordinates for other HK data skills.
hk-hospital
by samsonllamQuery real-time A&E (Accident & Emergency) waiting times at Hong Kong public hospitals from the Hospital Authority. Use when the user asks about emergency room wait times, which hospital has the shortest queue, A&E waiting times, or any question about going to the emergency department in Hong Kong. Covers all 18 public hospitals with A&E services.
hk-news
by samsonllamGet latest Hong Kong news headlines from RTHK (Radio Television Hong Kong). Use when the user asks about Hong Kong news, current events, what's happening in HK, latest headlines, or wants a news briefing. Covers local, international, finance, and sports news in English and Chinese.
hk-open-data
by samsonllamComprehensive Hong Kong open data skill combining weather, transport, parking, and geographic data from HK government APIs. Use when the user asks general questions about Hong Kong that may involve multiple data sources, or when you need to combine weather + transport + parking + location data for a complete answer. Acts as a unified interface to all HK open data.
hk-parking
by samsonllamQuery real-time parking vacancy data for Hong Kong car parks from the Transport Department. Use when the user asks about parking availability, car park vacancies, where to find parking spots, or parking information in Hong Kong. Covers 541+ government and private car parks across HK.
hk-transport
by samsonllamQuery Hong Kong public transport data including KMB bus, Citybus, GMB minibus, NLB (Lantau bus), MTR heavy rail, and Light Rail real-time arrival times and route information. Use when the user asks about bus arrival times, MTR schedules, minibus routes, light rail, route planning, which bus to take, next train time, or any public transport question in Hong Kong. Covers KMB (1600+ routes), Citybus (400+), GMB minibus (569), NLB (64), MTR (all lines), and Light Rail.
hk-weather
by samsonllamQuery real-time Hong Kong weather data from the Hong Kong Observatory (HKO) API. Use when the user asks about Hong Kong weather, temperature, rainfall, humidity, forecasts, typhoon signals, rainstorm warnings, weather warnings, UV index, or any weather-related question specific to Hong Kong. Also triggers for questions about whether to bring an umbrella, what to wear, or outdoor activity planning in HK.
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.