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...
analyze-spending
by caseygAnalyze finances using LunchMoney API. Use when the user asks about spending patterns, budget analysis, subscription audit, recurring expenses, or financial overview for periods like "this month", "last month", "this year", or custom date ranges.
book-fitness
by caseygBook fitness classes at Chelsea Piers via REST API with JWT caching. Use when the user asks to book a class, check yoga schedule, sign up for fitness, or book a class exactly 24 hours before it starts.
coop-shift
by caseygFind and book Park Slope Food Coop shifts using browser automation. Use when the user asks to book a coop shift, find available shifts, check coop schedule, or manage PSFC work assignments.
not-ai
by caseygRewrite AI-sounding text into clear, natural plain language. Use when the user asks to make text sound less like AI, rewrite in plain language, de-AI text, or make writing more natural.
notion-obsidian-sync
by caseygSync Notion workspace to Obsidian vault using notoma. Use when the user asks to sync notion, import notion pages, export notion to obsidian, or back up notion to markdown.
orbital
by caseygBack up Orbital.nyc reflections (weekly logs, monthly logs, quarterly log reflections, and quarterly community updates with wins/lessons/intentions/talk) as Markdown to Obsidian. Use when the user asks to sync, back up, export, or archive Orbital reflections or logs.
remarkable
by caseygManage reMarkable tablet documents - upload PDFs/EPUBs, download with annotations, backup notebooks, and list/search documents. Use when the user mentions reMarkable, wants to send files to their tablet, download annotated PDFs, or backup their notebooks.
reorder-basics
by caseygReorder items from Amazon Buy Again page using browser automation and 1Password integration. Use when the user asks to reorder, buy again, or repurchase an item from Amazon.
things-to-todoist
by caseygMigrate tasks from Things 3 to Todoist with duplicate detection and merge support. Use when the user asks to migrate, export, sync, or move tasks from Things to Todoist.
timing-analysis
by caseygAnalyze time tracking data from Timing App. Use when the user asks about how they spent their time, wants a time report, or mentions Timing app data for periods like "today", "yesterday", "this week", "last week", "this month", or custom date ranges.
todoist-triage
by caseygTriage and clean up Todoist with duplicate detection, stale task cleanup, and ADHD-friendly coaching. Use when the user asks to triage, organize, or clean up their Todoist inbox or tasks.
tripit-export
by caseygExport TripIt travel data (trips, flights, hotels) to JSON using browser automation. Use when the user asks to export, download, or backup their TripIt data.
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.