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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vcad
by ectoCreate 3D CAD models using vcad MCP tools. Use when the user asks to create 3D parts, mechanical components, enclosures, brackets, gears, or any parametric geometry. Supports primitives, sketch-based operations (extrude, revolve, sweep, loft), booleans, patterns, fillets, shell, assemblies, and export to STL/GLB.
vcad-step-import
by ectoImport and work with STEP files using vcad MCP tools. Use when the user mentions STEP files, .step, .stp, importing CAD from other software (Fusion 360, SolidWorks, Onshape), or converting between CAD formats.
vcad
by ectoCreate 3D CAD models using vcad MCP tools. Use when the user asks to create 3D parts, mechanical components, plates with holes, brackets, or any parametric geometry. Supports primitives (cube, cylinder, sphere, cone), boolean operations, transforms, patterns, and export to STL/GLB.
vcad-assembly
by ectoBuild multi-part assemblies with joints and run physics simulations. Use when the user asks about robot arms, mechanisms, hinges, joints, physics simulation, reinforcement learning environments, or assembly of multiple parts.
firmware-review
by ectoReviews Rust firmware code for the BVR (Base Vectoring Rover) with focus on safety-critical systems, CAN bus protocol compliance, motor control logic, state machine correctness, and embedded testing patterns. Use when reviewing BVR firmware changes, debugging actuator control, testing motor communication, validating safety mechanisms, checking async patterns, or evaluating control system modifications. Covers watchdog implementation, e-stop handling, rate limiting, VESC motor controller integration, and Tokio async runtime patterns.
depot-services-review
by ectoReviews Rust microservices in the depot/ directory with focus on Axum web framework, SQLx database patterns, Docker deployment, and WebSocket communication. Use when reviewing depot service changes (discovery, dispatch, map-api, gps-status, mapper), adding new endpoints, modifying database schemas, implementing WebSocket protocols, or debugging service integration issues. Covers Tokio async patterns, REST/WebSocket API design, PostgreSQL migrations, error handling, health checks, and Docker multi-stage builds.
documentation-automation
by ectoAutomatically maintains project documentation including CHANGELOG.md, README files, inline code documentation, and cross-references. Use proactively after implementing features, fixing bugs, making API changes, or completing significant work. Updates CHANGELOG.md with conventional commit format, adds README sections for new features, generates inline documentation for new functions/structs/components, and ensures documentation cross-references are up-to-date. Covers Rust doc comments, TypeScript JSDoc, conventional commits (feat/fix/docs/refactor/test/chore), and multi-level README organization.
console-frontend-review
by ectoReviews React/TypeScript code for the depot console web application with focus on real-time rover teleoperation, state management, WebSocket communication, and 3D visualization. Use when reviewing console frontend changes, debugging teleop UI issues, optimizing rendering performance, validating WebSocket protocols, checking React Three Fiber implementations, or evaluating state management patterns. Covers Zustand store architecture, binary protocol encoding, input handling, page visibility safety, memory management, and 360-degree video streaming.
deployment-automation
by ectoGuides deployment of Muni software to rovers (aarch64 cross-compilation, systemd) and depot (Docker Compose). Use when deploying firmware updates, installing services, configuring environments, troubleshooting deployment failures, or setting up new rovers/depot instances. Covers cross-compilation with `cross` tool, deploy.sh script usage, systemd service management, Docker Compose profiles, environment variables, and rollback procedures.
integration-testing
by ectoGuides end-to-end testing, mocking, and simulation for the Muni codebase. Use when writing integration tests, setting up test environments, creating mock CAN bus, testing WebSocket protocols, validating database fixtures, or debugging test failures. Covers Rust test patterns (tokio::test, integration tests), TypeScript testing (Vitest), mock infrastructure (Docker Compose for tests), CAN bus simulation, WebSocket test clients, database seeding, and Rerun recording validation. Essential for ensuring components work together correctly.
mcu-embedded-review
by ectoReviews embedded Rust firmware for RP2350 (Raspberry Pi Pico 2 W) and ESP32-S3 (Heltec) microcontrollers with focus on Embassy async runtime, memory constraints, LED control, CAN attachment protocols, and SLCAN bridging. Use when reviewing MCU firmware changes, debugging LED controller issues, evaluating Embassy async patterns, checking static memory allocation, validating SLCAN implementations, or assessing hardware-specific code for WS2812 LEDs, CAN peripherals, and tool attachments. Covers no_std environments, PIO state machines, RMT peripherals, and USB CDC serial communication.
arxiv
by ectoFetch and summarize arXiv papers. Search by topic, read specific papers by ID or URL, and get plain-language summaries. Use when the user mentions arXiv, asks about research papers, wants to find recent academic work on a topic, or is discussing algorithmic or architectural choices that could benefit from literature review.
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.