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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home-assistant
by HouseGarofaloUltimate Home Assistant skill - complete administration, wireless protocols (Zigbee/ZHA/Z2M, Z-Wave JS, Thread, Matter), ESPHome device building, advanced troubleshooting, performance optimization, security hardening, custom integration development, and professional dashboard design. Covers configuration, REST API, automation debugging, database optimization, SSL/TLS, Jinja2 templating, and HACS custom cards. Use for any HA task.
streamlit-dashboards
by HouseGarofaloBuild Python-native dashboards with Streamlit. Covers layouts, components, session state, caching, charts, custom components, and deployment. Use for data science dashboards, ML demos, internal tools, and rapid prototyping with Python. Triggers on streamlit, python dashboard, data visualization, ML demo, interactive dashboard, data app.
mlflow
by HouseGarofaloML lifecycle management with MLflow. Track experiments, package models, manage registries, and deploy models. Use for ML operations, experiment tracking, and model deployment.
esphome-devices
by HouseGarofaloCreate and configure ESPHome devices for DIY smart home sensors and actuators. Write YAML configurations for ESP8266/ESP32 boards, sensors, displays, and automations. Use when building custom IoT devices, flashing ESPHome firmware, or integrating with Home Assistant. (project)
mqtt-iot
by HouseGarofaloConfigure MQTT brokers (Mosquitto, EMQX) for IoT messaging, device communication, and smart home integration. Manage topics, QoS levels, authentication, and bridging. Use when setting up IoT messaging, smart home communication, or device-to-cloud connectivity. (project)
matter-thread
by HouseGarofaloMatter and Thread protocol management for smart home interoperability. Configure Matter controllers, Thread border routers, device commissioning, and multi-admin fabric setup. Use when working with Matter, Thread, smart home protocols, device pairing, border routers, or cross-platform compatibility.
archon-workflow
by HouseGarofaloTask management with Archon MCP server. Manage projects, tasks, documents, and RAG knowledge base. Use for tracking work, organizing projects, searching documentation, and maintaining persistent context across sessions.
onboarding-mode
by HouseGarofaloActivate new developer helper mode. Patient mentor for explaining codebases, answering questions, and guiding learning. Use when onboarding new team members, explaining project structure, or answering beginner questions.
pydantic-ai
by HouseGarofaloBuild AI agents with PydanticAI. Type-safe agent framework with structured outputs, tools, and dependency injection. Use for production AI agents, type-safe LLM applications, and Python AI development.
data-visualization
by HouseGarofaloCreate effective data visualizations with React charting libraries. Covers chart selection, Recharts, Chart.js, D3.js basics, real-time data, accessible charts, and color palettes. Use for charts, graphs, dashboards, and data-driven displays.
instructor
by HouseGarofaloStructured outputs with Instructor. Extract typed data from LLMs using Pydantic models and validation. Use for data extraction, structured generation, and type-safe LLM responses.
octoprint
by HouseGarofaloComplete OctoPrint management skill for Raspberry Pi with Creality Ender 3D printers. Control prints, monitor temperatures, manage files, configure timelapses (Octolapse), view bed leveling mesh, troubleshoot issues, and maintain OctoPi. Supports REST API and SSH access. Use when managing 3D prints, checking printer status, uploading G-code, creating timelapses, or troubleshooting print issues.
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.