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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node-plan
by whichguyDual-perspective Node.js/TypeScript plan review (TypeScript/API + Node runtime) with iterative convergence loop. 36 Node/TS-specific questions (N20/N21 naming+docs deferred to L1's Q-G6/Q-G7 which cover these universally). **AUTOMATICALLY INVOKE** when: - Any plan exists for Node.js or TypeScript changes - Plan references package.json, tsconfig.json, .ts files, npm/yarn/pnpm/bun - Plan targets Express, Fastify, NestJS, Next.js, or similar Node frameworks - Plan modifies async code, environment variables, or Node process lifecycle - User says "review plan", "check plan", "node-plan" **NOT for:** GAS plans (use /gas-plan), code review (use /review), non-Node plans
proactive-research
by whichguyHook-driven proactive research pipeline. Runs automatically via UserPromptSubmit — there is no slash command to trigger it. This page documents how it works, how to disable it, and the env-var knobs.
improve-prompt
by whichguyResearch-backed prompt improvement workflow. Analyzes with Q1-Q13 structural diagnostics, researches domain + prompt engineering best practices, generates fixed+dynamic evaluation questions, validates improvement plan (quality gate), runs E parallel experiment variants, scope-preservation gate (12-question check against baseline for unintended regression), evaluates via questions-based judge (not holistic), reconciles all learnings into a single ideas file, and commits only if improved. Three loop modes: default (stall detection stops after max_stalls consecutive failures), fixed (explicit --iterations N always completes all N), and duration (--duration 2h or "until 5pm" loops until time expires). Strategy escalation guides bolder changes after stalls. Position bias mitigated via randomized judge ordering. Supports --iterations N, --experiments N, --max-stalls N, and --duration. AUTOMATICALLY INVOKE when user mentions: - "improve this prompt", "make this prompt better", "optimize this prompt" - "prompt impro
form990
by whichguyForm 990 Skill — Orchestrator for end-to-end IRS Form 990 preparation. Guides a nonprofit Executive Director through all 10 phases (P0–P9): intake, source discovery, chart-of-accounts mapping, financial statements, Part IV checklist, core parts, schedule generation, Part I rollup, CPA quality review (30 gates, 3 tiers), and reference PDF fill + e-file handoff packet. Stateful plan-file journal enables cold-resume across sessions. AUTOMATICALLY INVOKE when: - User says "prepare form 990", "990 filing", "nonprofit tax return" - User provides a Google Sheets budget or financial data for a nonprofit - Resuming a prior 990 session ("continue", "pick up where we left off") - User asks about Part IV, Schedule A/B/G/O in a 990 filing context NOT for: General tax questions, for-profit returns, 990-N or 990-PF variants
concurrent-evolution
by whichguyEvolve agentic logic through high-concurrency (8x) stress testing using isolated sub-agents. Ensures 100% cognitive statelessness and strict structural scoring.
c-thru-config
by whichguyUnified c-thru configuration: diagnose the active setup, resolve what a capability alias maps to, switch connectivity modes, remap per-capability models, validate the config, or reload the running proxy. Subcommands: diag [--verbose] | resolve <cap> | mode [<mode>] [--reload] | remap <cap> <model> [--tier <tier>] [--reload] | set-cloud-best-model <cap> <model> [--tier <tier>] [--reload] | set-local-best-model <cap> <model> [--tier <tier>] [--reload] | route <model> <backend> [--reload] | backend <name> <url> [--kind <kind>] [--auth-env <VAR>] [--reload] | agent list | agent set <agent> <cap> [--reload] | agent pin <agent> <model> [--reload] | agent reset <agent> [--reload] | alias list | alias set <pattern> <cap> [--reload] | alias remove <pattern> [--reload] | override list | override set <from> <to> [--reload] | override remove <from> [--reload] | validate | reload | restart [--force]
c-thru-config
by whichguyUnified c-thru configuration: diagnose the active setup, resolve what a capability alias maps to, switch connectivity modes, remap per-capability models, validate the config, or reload the running proxy. Subcommands: diag [--verbose] | resolve <cap> | mode [<mode>] [--reload] | remap <cap> <model> [--tier <tier>] [--reload] | set-cloud-best-model <cap> <model> [--tier <tier>] [--reload] | set-local-best-model <cap> <model> [--tier <tier>] [--reload] | route <model> <backend> [--reload] | backend <name> <url> [--kind <kind>] [--auth-env <VAR>] [--reload] | agent list | agent set <agent> <cap> [--reload] | agent pin <agent> <model> [--reload] | agent reset <agent> [--reload] | validate | reload | restart [--force]
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.