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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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whoami
by sorawit-wA short, conversational interview that builds a portable profile of the user — who they are and how they want to be collaborated with — so the agent can tailor its responses. Triggers on `/whoami`, "get to know me", "set up my profile", "personalize how you work with me", "calibrate how you respond", the user mentioning they are new to AI or switching agent vendors, or a request for their collaboration "character sheet" or player profile. Produces six collaboration dials, an RPG-style class + subclass with a character portrait, a memory profile, and a self-contained HTML character sheet. When a profile exists, bare `/whoami` reviews it and offers to correct or re-run; `/whoami rerun` restarts the interview. Does NOT trigger for project- or task-specific work calibration (use `handshake`), routine memory edits or CRUD (use memory-management), or brand-voice work. `whoami` is person-level and broad; `handshake` is project-level and feeds from it.
pixel-art
by sorawit-wGenerate hi-fidelity pixel art images — scenes, characters, buildings, nature, and title cards — from a short brief, with a built-in design system (palette tokens, density specs, composition rules, font catalog, anti-pattern checklist) so the user does not have to re-specify the style every time. Two style modes — `hi-fi` (default, painterly high-density pixel art like medieval harbor scenes and tavern interiors) and `lo-fi` (scanlined warm-paper banner aesthetic, matching the agent-skills repo banners). Generation is **capability-gated**: when a connected image generator MCP is available (Z-image Turbo, Imagen / Nano Banana, OpenAI Image, etc.), the skill generates inline; otherwise it emits a copy-pasteable, model-agnostic prompt brief the user runs in their preferred tool (Midjourney, DALL-E, SDXL, Imagen, any other). Title cards additionally have a code-based SVG path using VT323 (default) plus a curated pixel-font catalog. Triggers on phrases like "pixel art", "pixel-art [scene/character/building/title c
define
by sorawit-wContextual definition and translation of a single word or phrase, resolved from the sentence or paragraph around it. Use when the user wants to understand what a specific word or phrase means *as used here* — the true in-context sense, not the dictionary default. Like the "Sider" select-and-define experience, minus the selection: the user names a target word/phrase and gives surrounding text; this skill disambiguates the sense and explains it for a language learner (contextual meaning, why this sense, register, etymology, related senses, difficulty), and can translate it contextually into another language. Triggers on phrases like "what does X mean here", "what does this word/phrase mean in this sentence", "define X in context", "what's the meaning of X as used here", "true meaning of X in this paragraph", "translate just this word/phrase in context", "explain this word given the surrounding text", or any word-sense / vocabulary / reading-comprehension request that supplies a target plus its context. Does NOT
gamification-fit
by sorawit-wAnalyze a product, idea, or resource set (source code, docs, URLs, analytics exports) against a stated goal and produce a restraint-first report on where gamification genuinely fits — and, more prominently, where it deliberately does NOT. The superpower is discrimination, not idea volume: most features should not be gamified, and a tool that gamifies everything is harmful (dark patterns, noise). Anchored on Self-Determination Theory; steers away from default Points/Badges/Leaderboards; refuses manipulation outright via a non-droppable ethics veto. Produces an editable Markdown artifact plus a self-contained HTML report (default paths `docs/gamification-fit/gamification-fit.md` and `docs/gamification-fit/gamification-fit.html`). The report is a forward-looking, hand-off-ready brief a developer or agent builds from. Use whenever the user asks to "gamify my product", "where should I add gamification", "should I gamify X", "suggest game mechanics for", "make this more engaging with game mechanics", "gamification
riskiest-assumption-test
by sorawit-wWalks the founder through assumption dump, risk × impact ranking, falsifiable hypothesis rewriting, and test-method selection — produces a 1-page Assumption Test Plan with the top 3 ranked hypotheses, success/kill criteria, and an interactive HTML risk × impact matrix. Use after `validation-canvas` and BEFORE `pitch-deck` — pitching on untested assumptions is sales theater. Triggers on phrases like "test my assumptions", "riskiest assumption", "RAT", "what should I validate first", "assumption mapping", "experiment design", "how do I de-risk this", "Wizard of Oz test", "fake door test", "concierge MVP", "smoke test", "pre-sale validation", "5-interview rule", or when the user has `validation-canvas.md` with populated Stress Tests and asks what to do next. Job — "what have we proven?" (experimental). This is the upstream half of validation closure; the downstream half is updating the canvas based on results (loop-back). NOT a discussion of testing in general (use `team-composer`); NOT a generic experiment plat
i18n
by sorawit-wA comprehensive skill for handling internationalization (i18n) translation files and producing culturally authentic translations. Use when: (1) Editing, creating, or modifying translation files (JSON, YAML, TypeScript, JS) — especially large ones that risk exceeding token limits, (2) Translating UI strings into any supported language/dialect, (3) Reviewing translations for cultural accuracy and naturalness, (4) Syncing translation keys across multiple locale files, (5) Adding new languages or regional variants to an existing i18n setup. Triggers on phrases like "translate", "i18n", "localization", "translation file", "locale", "add language", "translation keys", or any mention of specific locale codes (e.g., th, ja, ko, zh-CN, de, fr, es, it, zh-TW, zh-HK, th-bupphe). Does NOT trigger on defining or glossing a single word/phrase inside a sentence ("what does X mean here", "define X in context") — that is the `define` skill. This skill is for i18n files and shipping localized strings, not inline word-meaning l
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.