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...
manuscript-scaffold
by youngeun1209Scaffold a LaTeX manuscript directory for a research project — copy the bundled skeleton (main.tex + sections/* + figures/ + references.bib + .gitignore + README), optionally apply a journal-specific documentclass from templates/journal-registry.json, optionally clone an Overleaf project and cache the Git credential helper (token never persisted to tracked files), commit on the default branch, and ask before pushing. Invoked by /start-research phase 6, but also standalone-callable when adding a manuscript dir to an existing project later.
iterate-revision
by youngeun1209Revise one manuscript section against the reviewer team until DONE, BLOCKED, or HALT. Loops `@paper-writer` ↔ `@reviewer` with a venue-specific reviewer brief, recording every iteration's issues + verdict to `reviews.json` and updating `paper.json.sections[name]` status / iter. The first OMCR engine — a worked example of how to compose `skills/orchestrate/phases/*` primitives into a domain-specific loop. Safe to re-run; safe to resume after BLOCKED or HALT.
outline-expand
by youngeun1209Map-reduce engine — given an outline file and a `paper.json`, draft N sections in parallel by dispatching `@paper-writer` once per section in a single Agent-tool batch, then assemble the prose into each section's `paper.json.sections[name].path`. Passes a shared `nomenclature.md` payload to every dispatch (Phase 2 decision §4) so parallel writers share terminology. After merge, emits a non-blocking `terminology-drift.md` lint artifact listing terms that disagree across sections. First drafts only — does **not** call `/iterate-revision`. Safe to re-run; safe to scope with `--sections`.
respond-reviewer
by youngeun1209Read a reviewer letter, classify each comment by type, dispatch per-comment responses to the right specialist agent (`@paper-writer`, `@analysis-implementer`, `@literature-curator`), and assemble a complete rebuttal letter. Structural comments are surfaced to user attention rather than auto-dispatched. Output defaults to LaTeX; accepts markdown or LaTeX input (auto-detected by extension). Safe to re-run; safe to resume after BLOCKED. The Phase 2 worked example of the classify-and-dispatch orchestration shape.
start-research
by youngeun1209Interview-driven first-research-project initialization — asks about working title, field, hypothesis, target venue, datasets, narrative spine; fills the CLAUDE.md placeholders that /omcr-setup scaffolded; applies an optional domain preset to agent memory (when the existing MEMORY.md is still canonical-template); seeds @reviewer's persistent memory with target-venue aims/scope/editorial-priorities (registry-first, WebFetch fallback); scaffolds the LaTeX manuscript directory via the manuscript-scaffold skill. Requires /omcr-setup to have run first — will offer to run it if not. Safe to re-run; never overwrites filled-in answers, modified MEMORY.md, or existing manuscript content.
verify-citation
by youngeun1209Verify that academic citations exist and match the metadata claimed for them, and optionally write verification results into a project summary table. Looks up DOIs against CrossRef, fetches canonical authors/title/year/journal and the abstract via OpenAlex, and reports any mismatch against a BibTeX entry or a free-form DOI. Use to gate every citation `literature-curator` adds, to audit an existing BibTeX file for fabricated or wrong entries, or to retrieve an abstract so the calling agent can judge whether the paper actually supports a given claim.
todofig
by youngeun1209Compare a captured-figure deck against an outline document and produce a prioritized TODO of gaps (P0/P1/P2). Reads `## Research stack` config from the user's CLAUDE.md (Deck file / Outline file / Figure count / Result pattern / Report language / Report output dir). Accepts an optional figure identifier in `$ARGUMENTS` (e.g. "Fig4") to restrict the analysis to a single figure. Called by the `/todofig` slash command but also standalone-invocable.
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.