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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cartographer
by kingbootoshiMaps and documents codebases of any size by orchestrating parallel subagents. Creates docs/CODEBASE_MAP.md with architecture, file purposes, dependencies, and navigation guides. Updates CLAUDE.md with a summary. Use when user says "map this codebase", "cartographer", "/cartographer", "create codebase map", "document the architecture", "understand this codebase", or when onboarding to a new project. Automatically detects if map exists and updates only changed sections.
nano-banana
by kingbootoshiGenerates AI images using the nano-banana CLI (Gemini 3.1 Flash default, Pro available). Handles multi-resolution (512-4K), aspect ratios, reference images for style transfer, green screen workflow for transparent assets, cost tracking, and exact dimension control. Use when asked to "generate an image", "create a sprite", "make an asset", "generate artwork", or any image generation task for UI mockups, game assets, videos, or marketing materials.
codex-orchestrator
by kingbootoshiDEFAULT PIPELINE for all tasks requiring execution. You (Claude) are the strategic orchestrator. Codex agents are your implementation army - hyper-focused coding specialists. Trigger on ANY task involving code, file modifications, codebase research, multi-step work, or implementation. This is NOT optional - Codex agents are the default for all execution work. Only skip if the user explicitly asks you to do something yourself.
directional-prompting
by kingbootoshiWrite prompts, system instructions, agent directives, slash commands, and skill descriptions using two stacked layers — outcome-first (define the destination, success criteria, stopping condition) plus directional language (every sentence names the path with positive verbs). Triggers when writing or reviewing any prompt, system message, AGENTS.md, CLAUDE.md, skill description, agent instruction, tool description, slash command body, eval rubric, or anywhere an LLM reads instructions. Use when the user says "write a prompt", "improve this prompt", "audit this system prompt", "outcome-first", "success criteria", "directional", "make this prompt positive", or when authoring any new skill, agent, or directive.
tla-precheck
by kingbootoshiDesign and verify state machines using the TLA PreCheck TypeScript DSL. Use when building billing flows, subscription lifecycles, agent orchestration, queue processing, deployment pipelines, or any critical state machine where a bug means corrupted data, stuck users, or silent failures. Triggers on .machine.ts files, state machine design tasks, or when formal verification of state transitions is needed.
intent-contract
by kingbootoshiTurns a brief, discussion, or PRD into a Locked Intent Boundary - a small human-signed artifact that fixes intent before planning, so an instruction-following agent cannot silently redefine the goal or grade itself against its own rewrite. ONE mechanism for every task - destination deltas, invariants, non-substitutions, authorized change rights, proofs, stop/relock - with rigor scaled to blast radius, not a destructive/additive split. Use when turning research + discussion into a master PRD, before phasing/Linear issues, or for any build/remove/replace/migrate/refactor task. Triggers - "intent contract", "/intent-contract", "intent boundary", "lock the boundary", "write the master PRD", "before we plan this", "is this remove or replace". Derives the phased plan and runs a change-surface audit where every diff op (add/modify/delete/rename) must map to a signed authorization.
rgr
by kingbootoshiUse RGR for strict Red-Green-Refactor work in Claude Code or Codex when a code change should prove the test failed before implementation, protect that Red test from silent edits, prove Green with the same command, and finish with replay verification.
claude-code-orchestrator
by kingbootoshiUse when Codex should spawn, monitor, steer, or review Claude Code agents through the local claude-agent tmux CLI/service, especially for frontend workbench variants and Opus design passes.
opus-agent-orchestrator
by kingbootoshiRuns Opus/Claude Code agents through the local claude-agent tmux CLI/service for codebase exploration, frontend/workbench variants, implementation support, and second-model review. Trigger on /opus-agent-orchestrator, opus-agent, claude-agent, use opus agents, spawn opus, Claude Code agents, frontend variants, workbench variants, explore with Opus. Pairs with codex-orchestrator: Codex remains commander; Opus agents are specialist scouts/builders.
claude-code-orchestrator
by kingbootoshiSpawn and manage sibling Claude Code agents through claude-agent for parallel frontend, workbench, implementation, and review tasks.
diff-review
by kingbootoshiHand a finished session's git diff to Saint's phone for a full-screen, Tinder-style swipe review, then act on his verdict. This is the mobile diff reviewer — it supersedes the old HTML-page diff-review. Fire after you finish an implementation pass in a repo, or when Saint says "diff review", "$diff-review", "review changes", "review the work", "review this on my phone", "mobile review", "send it to review", "let me approve the diff", or "swipe review". Surfaces a URL + scannable QR, blocks until Saint approves or flags each file, then reworks only the flagged files using his per-file note verbatim and leaves approved files untouched.
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.