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.
Querying local SQLite index...
threejs-builder
by chongdashuCreates simple Three.js web apps with scene setup, lighting, geometries, materials, animations, and responsive rendering. Use for: "Create a threejs scene/app/showcase" or when user wants 3D web content. Supports ES modules, modern Three.js r150+ APIs.
nano-banana-builder
by chongdashuBuild full-stack web applications powered by Google Gemini's Nano Banana & Nano Banana Pro image generation APIs. Use when creating Next.js image generators, editors, galleries, or any web app that integrates gemini-2.5-flash-image or gemini-3-pro-image-preview models. Covers React components, server actions, API routes, storage, rate limiting, and production deployment patterns.
phaser4-gamedev
by chongdashuBuild 2D browser games with Phaser 4: WebGL-first rendering, scenes, filters, lighting, shaders, DynamicTexture and RenderTexture, tilemaps, SpriteGPULayer, TilemapGPULayer, and Phaser 3 to 4 migration work. Trigger: phaser 4, phaser v4, migrate phaser 3 to 4, phaser webgl renderer, phaser filters, phaser render nodes, phaser SpriteGPULayer, phaser TilemapGPULayer.
retro-diffusion
by chongdashuUse Retro Diffusion for pixel-art image generation, img2img edits, spritesheets, and animation experiments such as platformer walk cycles, turnarounds, and action sheets from reference images.
phaser4-gamedev
by chongdashuBuild 2D browser games with Phaser 4: WebGL-first rendering, scenes, filters, lighting, shaders, DynamicTexture and RenderTexture, tilemaps, SpriteGPULayer, TilemapGPULayer, and Phaser 3 to 4 migration work.
threejs-builder
by chongdashuDesign and implement lightweight Three.js (r150+) ES-module scenes—hero sections, interactive product viewers, particle backdrops, GLTF showcases, or quick prototypes—whenever prompts mention 'three.js/threejs scene', '3D web background', 'orbit controls', or 'WebGL demo'.
playwright-testing
by chongdashuPlan, implement, and debug frontend tests: unit/integration/E2E/visual/a11y. Use for Playwright MCP browser automation, Vitest/Jest/RTL, flaky test triage, CI stabilization, and canvas/WebGL games (Phaser) needing deterministic input plus screenshot/state assertions. Trigger: "test", "E2E", "flaky", "visual regression", "Playwright", "game testing".
phaser-gamedev
by chongdashuBuild 2D games with Phaser 3 framework. Covers scene lifecycle, sprites, physics (Arcade/Matter), tilemaps, animations, input handling, and game architecture. Trigger: "create phaser game", "add phaser scene", "phaser sprite", "phaser physics", "game development with phaser".
playwright-testing
by chongdashuPlan, implement, and debug frontend tests: unit/integration/E2E/visual/a11y. Use for Playwright/Cypress/Vitest/Jest/RTL, flaky test triage, CI stabilization, and canvas/WebGL games (Phaser) needing deterministic input + screenshot/state assertions.
phaser-gamedev
by chongdashuBuild 2D browser games with Phaser 3 (JS/TS): scenes, sprites, physics (Arcade/Matter), tilemaps (Tiled), animations, input. Trigger: 'Phaser scene', 'Arcade physics', 'tilemap', 'Phaser 3 game'.
threejs-capacitor-ios
by chongdashuBuild and ship Three.js apps on Capacitor iOS with Vite and Swift Package Manager: GLTF loading, assets_index animation UI, OrbitControls mouse/touch mappings, and iOS sync/run troubleshooting.
threejs-builder
by chongdashuCreate simple, performant Three.js web apps/scenes (JS/TS, ES modules): scene/camera/renderer setup, lighting, geometries, materials, animation loop, resizing, OrbitControls, GLTF/GLB loading, and practical performance guardrails.
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.