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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noah-zender-it
by zvadaadamRank mental models from Noah Zender's curated 468-item idea library by leverage for your current situation, with one concrete way to apply each. Use when you're at a decision point — naming, positioning, writing, picking a project, scoping work — and you want frameworks that actually fit your case, not generic mental-models advice. Default source library: noahzender.com/ideas.
code-review
by zvadaadamMulti-lens code review — spawns parallel sub-agents to independently review branch changes for bugs, security, and design, then validates and reports only high-signal findings. Use when you want a thorough review before merging or when self-reviewing your own changes.
brand-name-explore
by zvadaadamMulti-persona naming exploration with consensus. Spawns parallel sub-agents — each embodying a different naming philosophy (David Placek's Lexicon methodology, the Poet, the Linguist, the Culture Hacker, the Futurist) — to explore divergent naming directions for a product or company, then synthesizes into a ranked shortlist. Based on David Placek's naming framework (Lexicon Branding — Swiffer, BlackBerry, Impossible, Sonos, Pentium). Use when naming a product, company, feature, or brand and you want breadth, surprise, and strategic advantage before committing.
interview-me
by zvadaadamInterview the user about a plan, design, or idea until reaching shared understanding. Walks down every branch of the decision tree, resolving dependencies one by one. Maintains a durable transcript on disk so the interview survives context loss. Use when you want to stress-test a plan, think through a design, or need the agent to gather all the context it needs before building.
ai-journa
by zvadaadamPROACTIVE SKILL - Observe and document how Adam works with AI. This skill MUST be triggered automatically at the START of every conversation and periodically during long sessions. It runs silently in the background using a sub-agent. Use this skill PROACTIVELY whenever Adam is: coding, debugging, building, implementing, refactoring, planning, reviewing, deploying, testing, designing, configuring, researching, fixing bugs, adding features, writing scripts, setting up infrastructure, asking questions, brainstorming, or doing ANY software engineering task. Triggers on: code, debug, build, implement, refactor, plan, review, deploy, test, design, configure, research, fix, feature, script, infrastructure, question, brainstorm, create, update, delete, modify, change, add, remove, install, setup, migrate, optimize, improve, analyze, explore, search, find, write, edit, commit, push, pull, merge, branch, release, CI, CD, API, database, server, client, frontend, backend, fullstack, react, next, node, python, rust, go, d
tour
by zvadaadamTake a guided tour of a codebase subsystem — explore the code in parallel, then produce a self-contained HTML tour document that teaches the architecture so the reader doesn't need to read the code themselves. Optionally critiques the architecture for issues.
devs-roundtable
by zvadaadam5 legendary engineers debate your problem in parallel using the most powerful AI model available. Spawns sub-agents as John Carmack, Rich Hickey, Sandi Metz, Linus Torvalds, and Kent Beck — each explores independently, then synthesizes into a ranked consensus. Use when facing architectural decisions, non-obvious implementation choices, or when you want to stress-test an approach from multiple angles before committing.
skill-feedback
by zvadaadamSubmit concise feedback about AZ skills directly to PostHog. Use when a skill was useful, confusing, broken, missing context, or worth improving.
plan-for-goal
by zvadaadamTurn whatever's in the current conversation into one prompt to hand to the agent's `/goal` orchestration loop (Claude Code, Codex, or any harness with the same shape). The prompt is re-injected on every turn of the loop, so it must hold direction, taste, and a self-verification path across many iterations. Use when you're ready to hand a piece of work to `/goal`.
plan-for-mega-goal
by zvadaadamTurn a multi-objective piece of work into a roadmap on disk plus a small pointer prompt for the agent's `/goal` orchestration loop. Each sub-goal in the roadmap is shaped like a single `plan-for-goal` output; the roadmap holds the multi-goal scaffolding that wouldn't fit in `/goal`'s 4000-character objective limit. Use when the conversation has 3+ distinct objectives that share a destination.
greenlight-pr
by zvadaadamGreenlight a PR — fix CI failures, triage review comments, iterate until green.
repo-history-book
by zvadaadamBuild a durable, evidence-backed "book" of how an engineering project evolved by mining git history, PRs, releases, and docs. Use when the user wants repository archaeology, commit-by-commit walkthroughs, engineering learning extraction, pivot analysis, or a readable chronology of what the team changed, learned, reverted, and refined over time.
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.