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...
add-key-concepts-section
by quarcs-labInserts a pedagogical "Key Concepts" glossary section into a metricsAI chapter notebook (.qmd), between Chapter Overview and Setup. Each entry has a bold term, a 1–2 sentence definition, and two side-by-side collapsible Quarto callouts — a dataset-grounded *Example* and a vivid *Analogy* — so readers can skim definitions and expand only the panels they need. The new section is ADDITIVE: it complements (does not replace) the chapter's existing inline `> **Key Concept N.M**` blockquotes by picking 5–8 concepts disjoint from them, sized to chapter complexity, and tied to the chapter's `**What you'll learn:**` bullets. Refuses to run if the section already exists. Fully autonomous one-shot — drafts content, inserts, syncs all formats (.ipynb / .md / .html), verifies the HTML render, and reports. Does not commit. Goal is to foster learning through dataset-grounded examples and vivid analogies. Invoke via /add-key-concepts-section chNN.
create-chapter-code-summary
by quarcs-labCreates a self-contained Python code summary ("cheat sheet") for a metricsAI chapter's Key Takeaways section. Reads the chapter .qmd and its web app to identify key libraries, functions, datasets, and concepts, then generates a single markdown code block that reproduces the chapter's core workflow. The code block is pedagogical (commented), self-contained (runs in an empty Colab notebook), and aligned with the web app's key concepts. Invoke via /create-chapter-code-summary chNN.
chapter-standard
by quarcs-labVerifies and standardizes metricsAI chapter notebooks against CH01-04 template. Checks structure (45-75 cells, 70-80% markdown), front matter (visual summary, Learning Objectives, overview, setup), content (section numbering, 7-11 Key Concepts), back matter (Key Takeaways, Practice Exercises). Generates compliance report with CRITICAL/MINOR/SUGGESTIONS. Use when reviewing chapters, before PDF generation, or ensuring consistency. Supports --apply for automated fixes.
compile-book
by quarcs-labCompiles all chapter PDFs into a single metricsAI book PDF. Detects modified chapters, regenerates their PDFs, then merges all chapters with cover page, copyright page, Brief Contents, Detailed Contents, Key Concepts TOC, clickable TOC hyperlinks, page numbers, and section-level PDF bookmarks. Use after editing any chapter notebook.
bibtex-check
by quarcs-labThis skill should be used when the user asks to "check references", "check bibliography", "check bibtex", "audit references", "update references", "fix bibtex", "check citations", or "check bib". Also use when the user mentions missing volume, pages, or DOI in bibliography entries. Audits references.bib for completeness and currency, checking only entries cited in the manuscript.
lit-review
by quarcs-labConducts a structured literature review with search strategy, synthesis, and gap identification. Use when surveying a research area.
literature-note
by quarcs-labCreates a structured annotation note in references/ with sections for research question, data, findings, and relevance. Use when documenting a paper.
research-ideation
by quarcs-labGenerates research questions using four frameworks (Puzzle, Policy, Data, Extension). Use when brainstorming new directions.
prepare-region-submission
by quarcs-labUse this skill when the user asks to "prepare a REGION submission", "freeze a blind copy for the editor", "create a submission bundle", "send the paper to REGION", or "make a submission folder for peer review". Creates a self-contained, reviewer-blind submission folder at legacy/submission-YYYYMMDD/ containing the REGION PDF, Word version, single-file HTML, a fully standalone LaTeX tree, a blind manifest README, and a non-blind cover letter addressed to the editor. Verifies that the standalone LaTeX tree compiles end-to-end and that no author-identifying strings leak into reviewer-facing files before reporting success.
log-progress
by quarcs-labThis skill should be used when the user asks to "log progress", "save progress", "write a log", "session summary", "end session", or "wrap up". Also use when the user says goodbye or indicates they are finishing work. Creates a timestamped progress log entry in the ./log/ directory to preserve context across sessions.
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.