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...
seoul-density
by NomaDamas서울 주요 121개 핫스팟 장소의 실시간 혼잡도와 인구 현황을 조회한다. 지금 강남역이 얼마나 붐비는지, 홍대 인파가 얼마나 되는지 물어볼 때 사용한다.
weather-guide
by siddsacharGuidance for using weather tools effectively.
skills-menu
by AlpacaLabsLLCShow all available skills, agents, and how to use them — organized by task.
unified-notifications-ops
by JamkrisOperate notifications as one ECC-native workflow across GitHub, Linear, desktop alerts, hooks, and connected communication surfaces. Use when the real problem is alert routing, deduplication, escalation, or inbox collapse.
report-motor-fault
by Kitjesen关节电机故障：记录故障、通知处理并归档
schedule-ops-skill
by OpenLoaf当用户要把任务交给后台异步执行、按 cron / 周期性重复、委派给项目 Agent 长时间跑，或查看/审批/取消看板任务时触发。典型说法"每天 9 点帮我 X"、"让 coder agent 跑这个"、"有哪些待审批"。**不用于**：一次性即时答复（→直接用工具）、真实日历会议 / 约会（→calendar-ops-skill）、一次性计划审批（→`SubmitPlan`）。
run2-scheduling-pro
by cxcscmuAdvanced scheduling with priority-based sorting (EDF) and flexible slot overwriting.
plow-tracker
by diegosouzapwTrack Pittsburgh snow plows in real-time. Check plow locations, see which streets have been plowed, and monitor snow response activity. Uses live data from the City of Pittsburgh's Snow Response Dashboard.
mess
by diegosouzapwRequest physical-world tasks from human executors. Use for observations, checks, actions, photos, and purchases that require physical presence.
iaa-task-integration
by XcantloadXAdd or update an Ichika Auto Assistant task by wiring everything around the task implementation itself. Use when Codex needs to create a new task placeholder, register a task, add scheduler enable switches, extend config models and default JSON files, expose task settings in the desktop GUI, or verify that a new task is reachable from CLI and GUI without implementing the business logic yet.
fsl-scheduling-policies
by PranavNagrechaUse this skill to create, configure, or tune Field Service Lightning scheduling policies — including work rules (pass/fail filters) and service objectives (weighted ranking criteria). Covers the four default policies, custom policy design, work rule type selection, and objective weighting strategy. NOT for configuring service territories, resource availability calendars, or the Salesforce Scheduler (Appointment Scheduling) product.
fsm-sidebar-summarization
by Happy-Technologies-LLCGenerate sidebar summaries for field technicians with key case context, asset history, customer details, and actionable next steps for mobile field service views
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.