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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forgecad-lld
by KoStardWrite a Low-Level Design (LLD) for a CAD model — exact dimensions, constraints, parameters, and verification criteria. Use after a High-Level Design (HLD) exists and decisions are locked, or for simple parts that don't need an HLD. The detailed design document that code implements.
onshape
by ReshefElishaProtocols for driving Onshape CAD via the onshape-mcp plugin. Render-first and entity-first workflows, unit + coordinate conventions, iteration discipline, when to reach for FeatureScript, and the gotchas (REMOVE-on-face auto-flip, Variable Studios as separate elements, deterministic ID remapping). Load before building anything in Onshape — the plugin's MCP tool surface makes more sense with this doc in context.
vision-decompose
by ReshefElishaLook at an engineering reference image (drawing, iso render, photo) and produce a structured feature decomposition BEFORE building anything in Onshape. Output is a feature tree the user can review and the build phase can execute against. Use this whenever the user gives you a reference image and asks you to model it. Skip if the user has already described the part in plain text.
fusion360-expert
by theneoaiExpert Autodesk Fusion 360 user for integrated CAD/CAM/CAE. Use when designing mechanical parts, creating 3D prints, or preparing CNC manufacturing
solidworks-expert
by theneoaiSolidWorks机械设计：零件、装配体、工程图。Use when creating mechanical designs. Triggers: 'SolidWorks', 'CAD', '机械设计'. Works with: Claude Code, Codex, OpenCode, Cursor, Cline, OpenClaw, Kimi.
prototyping-fabrication
by TibsfoxCAD fundamentals, 3D printing (FDM/SLA/SLS), CNC machining, workshop skills, rapid prototyping methodology, and testing of physical prototypes. Covers fidelity levels, material selection for prototypes, dimensional tolerancing, assembly planning, and the iterate-test loop. Use when building prototypes, selecting fabrication methods, planning physical tests, or choosing between prototyping technologies.
gridfinity-baseplate-planner
by dgalarzaUse this skill when planning and designing gridfinity baseplates for 3D printing. This includes calculating optimal grid sizes from given measurements, determining how to slice large grids into printable chunks based on printer bed dimensions, and calculating padding requirements for non-exact fits. The skill handles both metric and imperial measurements and provides guidance for using gridfinity.perplexinglabs.com to generate the actual STL files.
technical-exploded-view
by cxcscmuGuidelines for creating technical exploded-view diagrams. Use this skill whenever drawing hardware, internal components, or technical exploded views.
stl-cube-scaffold
by diegosouzapwAnalyze an STL mesh (AABB + approximate OBB) and generate a single connected STL that includes an L×L×L cube scaffold (default 100mm) plus connectors, so the final solid's OBB is locked to the target cube size for packing/measurement checks.
3d-cad-skill
by aresbitCreate and iterate parametric 3D CAD models for Claude using an inspectable feedback loop. Use when the task involves OpenSCAD, build123d, STL/STEP/3MF output, fixture/enclosure/adapter design, or debugging shape accuracy from renders or screenshots.
openscad
by archibateCreate and render OpenSCAD 3D models. Generate preview images from multiple angles, extract customizable parameters, validate syntax, and export STL files for 3D printing.
freecad
by znlgisFreeCAD 是开源参数化 3D CAD 建模软件，基于 OCCT 几何内核与 Coin3D 显示引擎，提供 Sketcher、Part、PartDesign、Assembly、Draft、Arch（BIM）、CAM、FEM 等专业模块，支持 Python 脚本与插件扩展，适合机械设计、建筑 BIM、产品原型与教学。
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.