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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mintlify-mdx
by Kevin-Liu-01Mastering Mintlify's MDX pipeline — undocumented constraints, snippet sandbox rules, CSS override patterns, and npm bundling via yoink. Use when writing or debugging Mintlify snippets, customizing docs.json/styles.css, bundling npm packages into snippets, hitting cryptic MDX compile errors, or when the user says 'mintlify', 'snippet won't compile', 'MDX error', 'yoink', 'lint-snippets', 'docs.json', 'Mintlify CSS', 'function-body mode', 'baseUrl error', or 'findExport'. Reverse-engineered from Mintlify internals at Dedalus Labs.
dedalus-machines
by Kevin-Liu-01How Agent Machines work -- providers (Dedalus, E2B, Sprites), agent runtimes (Hermes, OpenClaw, Claude Code, Codex), MCP catalog, bootstrap, and closed-loop verification. Use when asked about the runtime, scaling, sleep/wake, destroy, or inspecting this environment.
doctor-enforcement
by Kevin-Liu-01Auto-run the relevant codebase doctor(s) after large edit sessions or before committing. Detects which subsystems were touched and runs the matching doctor. Use when the agent has completed a significant edit (5+ files), before claiming work is done, or when the user says "run doctors", "health check", "doctor pass", or "check everything."
pixijs-2d
by Kevin-Liu-01Fast, lightweight 2D rendering engine for creating interactive graphics, particle effects, and canvas-based applications using WebGL/WebGPU. Use this skill when building 2D games, particle systems, interactive canvases, sprite animations, or UI overlays on 3D scenes. Triggers on tasks involving PixiJS, 2D rendering, sprite sheets, particle effects, filters, or high-performance canvas graphics. Alternative to Canvas2D with WebGL acceleration for rendering thousands of sprites at 60 FPS.
og-metadata-audit
by Kevin-Liu-01Audit and fix OpenGraph and Twitter card metadata in Next.js App Router projects. Cross-reference metadata exports across pages, identify missing fields, DRY violations, broken middleware, and incorrect canonicals. Use when Twitter cards aren't rendering, OG images are broken, metadata is inconsistent across pages, or when the user says "audit OG", "fix opengraph", "twitter cards broken", "metadata audit", "OG not working", or "social preview broken".
substance-3d-texturing
by Kevin-Liu-01Comprehensive skill for Adobe Substance 3D Painter texturing and material creation workflow. Use this skill when creating PBR materials, exporting textures for web/game engines, optimizing 3D assets for real-time rendering, or automating texture workflows. Triggers on tasks involving Substance 3D Painter, PBR texturing, material creation, texture export for Three.js, Babylon.js, Unity, Unreal, glTF optimization, or Python API automation. Creates optimized textures for threejs-webgl, react-three-fiber, and babylonjs-engine materials.
babylonjs-engine
by Kevin-Liu-01Comprehensive skill for Babylon.js 3D web rendering engine. Use this skill when building real-time 3D experiences, browser-based games, interactive visualizations, or immersive web applications. Triggers on tasks involving Babylon.js, 3D scenes, WebGL/WebGPU rendering, entity-component systems, physics simulations, PBR materials, shadow mapping, or 3D model loading. Alternative to Three.js with built-in editor integration and game engine features.
blender-web-pipeline
by Kevin-Liu-01Blender to web export workflows for 3D models and animations. Use this skill when exporting Blender models to glTF for web, optimizing 3D assets for Three.js or Babylon.js, batch processing models with Python scripts, automating Blender workflows, or creating web-ready 3D pipelines. Triggers on tasks involving Blender glTF export, bpy scripting, 3D asset optimization, model compression, texture baking, or Blender automation. Exports models for threejs-webgl, react-three-fiber, and babylonjs-engine skills.
humanizer
by Kevin-Liu-01Remove signs of AI-generated writing from text, calibrated to Kevin's voice. Based on blader/humanizer (Wikipedia's "Signs of AI writing" guide) with a permanent voice profile extracted from Kevin's actual writing samples. Use when editing or reviewing any text Kevin will publish: social posts, Reddit posts, README copy, application essays, bios, wiki prose.
dcs-test
by Kevin-Liu-01Run DCS test profiles against controlplane (Go), host-agent (Rust), storage-daemon, or live clusters. Use when testing DCS changes locally or triggering CI test suites.
rive-interactive
by Kevin-Liu-01State machine-based vector animation with runtime interactivity and web integration. Use this skill when creating interactive animations, state-driven UI, animated components with logic, or designer-created animations with runtime control. Triggers on tasks involving Rive, state machines, interactive vector animations, animation with input handling, ViewModel data binding, or React Rive integration. Alternative to Lottie for animations requiring state machines and two-way interactivity.
skill-auditor
by Kevin-Liu-01Security auditor for agent skills. Run before installing any new skill from skills.sh, GitHub, or any source. 6-step vetting protocol covering typosquatting, permissions, dependencies, prompt injection, network exfiltration, and content red flags. Use when about to install a skill, reviewing a SKILL.md, auditing existing skills, or when a skill update changes permissions. Triggers on "install skill", "add skill", "npx skills add", "vet this skill", "audit skill", or "is this skill safe." Based on useai-pro/openclaw-skills-security. MUST be invoked before any skill installation — this is part of the quality gate.
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.