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...
quantized-llama2-7b-mlc
by Seeed-ProjectsDeploy quantized Llama2-7B with MLC LLM on Jetson Orin NX for fast edge inference. Uses jetson-containers Docker workflow with 4-bit quantization (q4f16_ft). Requires Jetson Orin with ≥16GB RAM, JetPack 5.x, and HuggingFace access token.
deploy-deepseek-mlc
by Seeed-ProjectsDeploy DeepSeek on Jetson Orin using MLC (Machine Learning Compilation) for optimized edge inference. Uses Docker/jetson-containers. Requires Jetson with >8GB RAM and JetPack 5.1.1+.
local-rag-llamaindex
by Seeed-ProjectsDeploy a local RAG chatbot on Jetson using LlamaIndex + ChromaDB + quantized Llama2-7b (MLC). Uses jetson-containers Docker environment. Requires Jetson with ≥16GB RAM and JetPack 5.1+.
voice-llm-reachy-mini-physical
by Seeed-ProjectsDeploy a fully local voice-interactive robotic assistant on reComputer Mini J501 with Reachy Mini for physical AI applications. Integrates Ollama LLM, FunASR speech recognition, and Coqui TTS for embodied conversational interaction. Requires JP6.2 and Reachy Mini Lite.
l4t-differences
by Seeed-ProjectsReference guide for differences between Seeed and NVIDIA L4T Board Support Packages across versions 35.3.1, 35.5, 36.3, 36.4, and 36.4.3. Covers added drivers for CAN bus, Wi-Fi, Ethernet, GMSL, TPM, audio codecs, and USB on Seeed Jetson devices.
recomputer-veye-compat-fix
by Seeed-ProjectsFix VEYE camera not detected on reComputer Jetson. Upgrades the USB hub chip (VL822) firmware to resolve i2c detection failure.
ethercat-setup
by Seeed-ProjectsEstablish EtherCAT communication between Jetson and EtherCAT slave devices using the EtherLab EtherCAT Master driver. Covers driver installation, configuration, slave scanning, and motor control example (MyActuator X4). Requires JetPack 6.2 (L4T 36.4.3).
security-scan
by Seeed-ProjectsDeploy a knife detection model on Triton Inference Server using reComputer J1010 for X-ray security scanning, with Raspberry Pi clients sending images for inference and displaying detection results.
llama-cpp-rpc-distributed
by Seeed-ProjectsDistribute LLM inference across multiple Jetson devices using llama.cpp RPC backend with CUDA. Build from source with RPC+CUDA, convert models to GGUF, and run multi-node inference for horizontal scaling. Requires two Jetson devices with JetPack 6.x+.
whisper-realtime-stt
by Seeed-ProjectsDeploy OpenAI Whisper on NVIDIA Jetson Orin for real-time speech-to-text. Clones the deployment repo, installs dependencies including ffmpeg, tests the environment, and runs real-time STT from a USB microphone (e.g. reSpeaker). Includes Riva vs Whisper comparison context.
bsp-source-build
by Seeed-ProjectsBuild Seeed's custom BSP source code for Jetson devices — obtain NVIDIA L4T, overlay Seeed patches, compile kernel, and flash. Requires Ubuntu 20.04/22.04 host PC.
usb-wifi-88x2bu-setup
by Seeed-ProjectsInstall and enable Realtek RTL88x2bu USB Wi-Fi adapters (for example 0bda:b812) on Ubuntu/Jetson. Clones a maintained driver repo, builds against the running kernel, installs the module, loads it, and verifies the new Wi-Fi interface.
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.