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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camofox-cloaked-browser
by tmchowUse Camofox/Camoufox as an opt-in anti-detection browser server for agent workflows that need cloaked browsing. Covers npm/npx startup, OpenClaw plugin tools, REST API commands, session/tab workflow, environment variables, process-scoped CAMOFOX_URL for Hermes, and hard rules such as always sending userId and re-snapshotting after state-changing actions. Do not use for normal web search, text extraction, curl fetches, or ordinary browser automation.
clawpatch
by tmchowThis skill is specifically for the Clawpatch CLI (openclaw/clawpatch, https://clawpatch.ai) — an npm-installed automated code-review and per-finding fix tool. Use it ONLY when Clawpatch itself is in play: the user says "review with clawpatch", "run clawpatch", "clawpatch fix", "find bugs with clawpatch", "clawpatch report/findings", "clawpatch open-pr", wants to dispatch subagents to fix Clawpatch findings in parallel, or otherwise names Clawpatch or one of its commands. Do NOT use it for generic "review this code", "find bugs", or "code review" requests that don't involve Clawpatch — those belong to a different tool.
chrome-devtools-axi
by tmchowThis skill should be used when the user names chrome-devtools-axi, asks to "execute npx -y chrome-devtools-axi", wants Chrome DevTools Protocol browser automation through the AXI CLI, or needs token-efficient accessibility snapshots, stale-ref-safe clicks/fills, console/network inspection, screenshots, Lighthouse, or performance traces from a Chrome session. Do NOT use for simple web search/static extraction, stealth/anti-detection browsing, or non-Chrome browser targets.
illo
by tmchowCreates original editorial illustrations where a recurring mascot character performs the idea — one caught scene by default, or a hand-built explainer diagram (a flow, fan-out, timeline, loop, or stack) when the structure itself is the point — in one of ten bundled print looks. Triggers only when the skill is directly invoked or "illo" is requested; never on generic illustrate / draw / make-an-image requests.
hzl
by tmchowPersistent task ledger for agent coordination. Plan multi-step work, checkpoint progress across session boundaries, and coordinate across multiple agents with project pool routing.
plan-review
by tmchowReview PRDs, brainstorm docs, tech plans, design docs, specs, or any planning document for issues. This skill should be used whenever the user wants feedback, critique, or a quality check on an existing planning or requirements document — even if they don't use the word "review." Common triggers include: "review the plan", "check the PRD", "critique my tech plan", "what's wrong with this plan", "poke holes in the requirements", "is this plan solid", "take a look at this spec", "give feedback on the brainstorm", or pointing to a doc file and asking if anything is off. Also triggers after writing a PRD or tech plan (to review what was just created), or when invoked by brainstorming or tech-planning skills. Do NOT trigger for code review (PRs, diffs, source files), writing/creating new plans, debugging, or reviewing non-planning documents (READMEs, CLAUDE.md, test coverage).
new-plugin
by tmchowScaffold a new plugin with proper marketplace structure
validate-plugin
by tmchowValidate plugin structure and manifests against marketplace requirements
agent-browser
by tmchowBrowser automation using Vercel's agent-browser CLI. This skill should be used when the user says "browse a website", "fill a form", "take a screenshot", or "test a web page". Uses ref-based element selection.
iterativebrainstorming
by tmchowScope-first brainstorming with intelligent routing — assesses complexity upfront (Quick/Standard/Full), then adapts depth accordingly. Handles simple bug fixes in ~2 exchanges and complex features with full PRD ceremony. Triggers: "brainstorm", "create a PRD", "write requirements", "explore approaches", "think through options", or starting a new feature with unclear direction.
code-review
by tmchowThis skill should be used when the user says "review my code", "check these changes", or wants feedback on code before creating a PR. Also used after completing a task during iterative implementation.
iterativedesign-exploration
by tmchowExplore radically different design approaches for any page, component, or feature. Use when the user wants to compare multiple distinct visual directions side by side — not build one specific thing, but see several fundamentally different takes on the same UI. Signals: "explore designs for", "show me different ways to design", "radically different approaches", "what could this look like", "design exploration", "let's see some options before we commit", "different layout approaches", "different visual identities for the same content", wanting to see multiple families or variations before choosing a direction. Also triggers when the user pastes design exploration feedback (starts with "## Design Exploration Feedback") to iterate on a previous round, or pastes a design direction ("## Design Direction") to finalize. Do NOT trigger for building one specific design, visual refreshes, design critiques, brainstorming without visual output, single widget prototypes, or comparing CSS frameworks.
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.