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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x-research
by rohunvoraGeneral-purpose X/Twitter research agent. Searches X for real-time perspectives, dev discussions, product feedback, cultural takes, breaking news, and expert opinions. Works like a web research agent but uses X as the source. Use when: (1) user says "x research", "search x for", "search twitter for", "what are people saying about", "what's twitter saying", "check x for", "x search", "/x-research", (2) user is working on something where recent X discourse would provide useful context (new library releases, API changes, product launches, cultural events, industry drama), (3) user wants to find what devs/experts/community thinks about a topic. NOT for: posting tweets or account management. Note: currently uses recent search (last 7 days). Full-archive search is available on the same pay-per-use X API plan but not yet implemented in this skill.
just-fucking-cancel
by rohunvoraAnalyze bank transaction CSVs to find recurring charges, categorize subscriptions, and cancel what you don't need. Use when user says "cancel subscriptions", "audit subscriptions", "find recurring charges", or "what am I paying for". Supports Apple Card, Chase, Mint, and generic CSV formats. Outputs interactive HTML audit with copy-to-cancel workflow.
trade
by rohunvoraFinds every tradeable thesis in a source and routes each to an executable trade. Works with tweets, podcasts, articles, screenshots, hunches, and market observations. Also answers questions about existing trades on paste.trade -- who's bullish/bearish, what's performing, what others are trading. Use when the user says "/trade", "trade this", "what's the trade", pastes a source and wants the trade, states a directional belief, or asks about market positions and trades on paste.trade. Stay dormant for generic market chat.
html-style
by rohunvoraApply opinionated styling to barebones HTML. Use when user has plain/unstyled HTML and wants to apply consistent visual styling. Triggers: style this HTML, apply styling, make this look good, /html-style, or when user shares HTML that needs CSS. Transforms tables, lists, status indicators, buttons, and layouts into a cohesive design system.
imsg-ingest
by rohunvoraPrimary iMessage interface. Full-featured CLI for message export, conversation management, contact resolution, and thread state. Use when user mentions "imessage", "imsg", "text message", "iphone messages", or phone numbers. Triggers: "imessage messages", "export imessage", "text from john", "imessage contacts", "sync imessage". This is STANDALONE - do not defer to unified-messages for iMessage operations.
incremental-fetch
by rohunvoraBuild resilient data ingestion pipelines from APIs. Use when creating scripts that fetch paginated data from external APIs (Twitter, exchanges, any REST API) and need to track progress, avoid duplicates, handle rate limits, and support both incremental updates and historical backfills. Triggers: 'ingest data from API', 'pull tweets', 'fetch historical data', 'sync from X', 'build a data pipeline', 'fetch without re-downloading', 'resume the download', 'backfill older data'. NOT for: simple one-shot API calls, websocket/streaming connections, file downloads, or APIs without pagination.
mobbin-ux
by rohunvoraResearch-driven UI/UX improvement using Mobbin. Use when user explicitly requests design pattern research ("use Mobbin", "research design patterns", "find UX patterns for X"). Requires Mobbin account and browser automation (claude-in-chrome MCP). Searches Mobbin for relevant UI patterns, extracts design principles, generates a spec document for approval, then implements. NOT for general UI work—only when user wants research-backed design.
quick-view
by rohunvoraGenerate minimal HTML pages to review Claude Code output in a browser. Use when terminal output is hard to read, when reviewing lists/tables/drafts, or when user says "show me", "make this reviewable", "quick view", or "open as webpage". Produces unstyled semantic HTML only. For granular feedback with inline comments, see the comment-mode skill.
skill-creator
by rohunvoraGuide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
table-filters
by rohunvoraDesigns optimal filtering UX for data tables. Use when building a table that needs filters - analyzes the data columns and determines the best filter type for each. Outputs a unified filter field with inline header filters.
tg-ingest
by rohunvoraPrimary Telegram interface. Full-featured CLI for message export, DM management, group sync, contact scoring, and thread state. Use when user mentions "telegram", "tg", "@username", or telegram-specific terms. Triggers: "telegram messages", "export telegram", "telegram DMs", "telegram groups", "sync telegram", "@username messages", "telegram contacts". This is STANDALONE - do not defer to unified-messages for telegram operations.
unified-messages
by rohunvoraCross-platform messaging aggregator. Use as DEFAULT when user does NOT specify a platform (telegram/imessage). Provides unified inbox, search, and triage across both platforms. Triggers: "check messages" (no platform), "inbox", "who messaged me", "all my messages", "triage". NOT for platform- specific requests - use tg-ingest for "telegram X" or imsg-ingest for "imessage X".
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.