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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wrds
by edwinhuUse when "query WRDS", "pull SEC filings", "access Compustat/CRSP/ExecuComp/Capital IQ", "Form 4 insider data", "ISS governance/compensation", "TAQ intraday/NBBO", "SDC M&A or new issues", "FISD bonds", "Form D/ADV", "FJC court data", or any WRDS PostgreSQL query or SAS ETL on the WRDS grid (qsub/qsas/SGE).
consensus
by edwinhuThis skill should be used when the user asks to "search Consensus", "consensus search", "find RCT papers", "find clinical papers", "search medical literature via consensus", "find papers on consensus.app", or needs to search Consensus.app for academic/medical literature via the consensus CLI tool.
hpc
by edwinhuUse when submitting jobs to UVA HPC (Rivanna/Afton), writing Slurm scripts (sbatch/srun/squeue), converting SGE to Slurm, running compute on any Slurm-managed cluster, or building WRDS data pipelines with polars on HPC. Triggers: 'submit to HPC', 'sbatch', 'squeue', 'slurm job', 'run on Rivanna', 'run on Afton', 'HPC array job', 'convert SGE to Slurm', 'polars on HPC', 'WRDS from HPC'.
dev-test
by edwinhuThis skill should be used when the user needs to 'debug web applications', 'test UI interactions', 'capture screenshots or network requests', 'test desktop automation', or needs to select between testing tools. Routes to platform-specific E2E testing skills: Chrome MCP for debugging, Playwright for CI/CD, Hammerspoon for macOS, Linux for X11/Wayland.
dev-test-hammerspoon
by edwinhuThis skill should be used when the user asks to "debug macOS app", "test native app", "automate macOS workflow", "test native macOS application", or needs desktop automation for testing macOS applications with Hammerspoon. Use for application launch/control, window management, keyboard/mouse simulation, and visual verification.
headline-card
by edwinhuUse this skill when the user asks to add news headline cards, 'Last Week Tonight'-style cards, headline slides, news quote slides, or media quote cards to a Typst presentation. Also use when the user wants to modify existing headline cards (add/remove cards, change quotes, swap logos, fix layout issues). Trigger on: 'add a headline', 'news card', 'LWT card', 'headline slide', 'quote card', 'media quote', 'add a quote from [publication]'.
marimo-serve
by edwinhuServe every marimo notebook in a project directory under one ASGI host with auto-discovery. Use when the user wants to browse multiple marimo apps at once (localhost, LAN, or tailnet) instead of running `marimo run` per file. Handles expose-on-tailnet via `tailscale serve`.
marimo
by edwinhuUse when working with marimo notebooks — creating, editing, debugging, converting from Jupyter, or pairing with a running marimo server.
readwise-docs
by edwinhuManage Readwise Reader documents. Use when the user wants to list, save, update, or delete documents in their Reader library. Triggers on "add to reader", "save article", "list my documents", "readwise list", "reader documents".
readwise-search
by edwinhuSearch Readwise highlights. Use when the user wants to find highlights, quotes, notes, or annotations from their reading library. Triggers on "search highlights", "find in readwise", "what did I highlight about", "my notes on".
readwise
by edwinhuThis skill should be used when the user asks to "search Readwise", "find highlights", "get quotes from my reading", "add highlights to notebook", "search my annotations", "get full document text", "fetch article content", "add tagged documents to notebook", or needs to query their Readwise library.
readwise-prune
by edwinhuClean up stale Readwise Reader documents. Use when the user wants to declutter their reading library, remove old unread articles, or manage Reader inbox. Triggers on "clean up readwise", "prune reader", "delete old articles", "declutter reading list".
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.