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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author-skill
by n1kbenAuthor, review, or improve a skill. Trigger whenever a skill itself is the subject — any request to create, add, write, edit, update, review, critique, improve, fix, or tweak a skill, however loosely phrased.
brainstorm
by n1kbenA generative thinking partner that brings its own ideas to provoke yours, opening a problem up instead of narrowing to an answer. Trigger when the user wants to open up an area or grow a half-formed idea rather than reach a decision — or says brainstorm.
data-modelling
by n1kbenDesigns and critiques how information is represented — reducing concepts to their irreducible facts, separating fact from derived, catching models tangled with flags and statuses, and translating the result into a database schema, type system, application state, API payload, event log, form, or spreadsheet. Trigger whenever someone designs, reviews, or refactors a data model, schema, type, table, or state shape; asks what fields, columns, tables, or enums a thing needs; weighs whether something is one entity or two, a flag or its own thing, stored or computed; or senses a representation is tangled, duplicated, or hard to change — however loosely phrased.
grill-me
by n1kbenInterview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
handoff
by n1kbenCompact the current conversation into a handoff document for another agent to pick up.
improve
by n1kbenTurn a vague request to improve something into concrete, grounded changes through short rounds. Trigger when the user wants to improve, polish, tighten, refine, or clean up an existing artifact without first saying exactly what's wrong.
librarian
by n1kbenCache a remote git repository as a reusable local checkout so repeated reads reuse it instead of re-cloning. Trigger whenever a remote repo comes up as reference — the user pastes a GitHub/GitLab/Bitbucket URL, a `git@…` address, or `owner/repo` shorthand, or asks you to "look at", "check out", "clone", or "use as reference" a repo you don't have locally.
review
by n1kbenSurface the real flaws in whatever's in front of you. Use whenever the user wants something reviewed, critiqued, audited, torn apart, or checked for problems — code, prose, a plan, a design — however loosely phrased.
notate
by n1kbenLay a thing out as an indented monospace text structure — a tree, signature, schema, or flow — rather than prose. Trigger when the user wants something shown as structure rather than paragraphs — notate, outline, sketch, map it out, show the shape of it — but not when they want a rendered or graphical diagram, which is a drawing tool's job.
sync
by n1kbenCommit, rebase onto latest master, and force-push the current branch. Trigger when the user wants to "sync", "sync my branch", "rebase and push", or bring their branch up to date with master.
plannotator-review
by n1kbenOpen Plannotator's browser-based code review UI for the current worktree or a pull request URL, then act on the feedback that comes back.
librarian
by n1kbenCache a remote git repository as a reusable local checkout so repeated reads reuse it instead of re-cloning. Trigger whenever a remote repo comes up as reference — the user pastes a GitHub/GitLab/Bitbucket URL, a `git@…` address, or `owner/repo` shorthand, or asks you to "look at", "check out", "clone", or "use as reference" a repo you don't have locally.
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.