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...
architecture-studio
by DreamLab-AIAEC (Architecture, Engineering, Construction) studio with 36 skills and 7 specialist agents. Single entry point via /studio [task]. Covers site planning, NYC zoning/due diligence, workplace programming, sustainability (EPD/GWP), materials research, FF&E schedules, specifications (CSI), and presentations. From AlpacaLabsLLC/skills-for-architects. Use when designing buildings, site planning, zoning analysis, or sustainability assessments (AEC domain).
csv-to-sif
by DreamLab-AIConvert a CSV or Excel FF&E product list to SIF (Standard Interchange Format) for dealer and procurement systems.
clipcannon
by DreamLab-AIAI-powered video understanding, editing, voice synthesis, and real-time voice agent via 51 MCP tools. 22-stage analysis pipeline with 5 embedding spaces (SigLIP, Nomic, Wav2Vec2, WavLM, ECAPA-TDNN). Declarative EDL editing with adaptive captions, face-tracking crop, split-screen, PIP, canvas compositing, motion effects. Voice cloning (Qwen3-TTS 1.7B), lip-sync avatars (LatentSync 1.6), AI music (ACE-Step), text-to-video generation. Voice Agent ("Jarvis") with wake-word ASR + local LLM. 7 platform profiles (TikTok, Reels, Shorts, YouTube, YouTube 4K, Facebook, LinkedIn). Tamper-evident SHA-256 provenance chain. 100% local GPU. Use when the user says "edit this video", "find the best moments", "create a highlight reel", "add captions", "clone voice", "lip sync", "render for TikTok", "talk to Jarvis".
history
by DreamLab-AINeighborhood context and history — adjacent uses, architectural character, landmarks, commercial activity, and planned development from an address.
zoning-analysis-nyc
by DreamLab-AIAnalyze zoning envelope rules for lots in New York City using PLUTO data and the NYC Zoning Resolution
environmental-analysis
by DreamLab-AIClimate and environmental site analysis — temperature, precipitation, wind, sun angles, flood zones, seismic risk, soil, and topography from an address.
flow-nexus-swarm
by DreamLab-AICloud-based AI swarm deployment on Flow Nexus infrastructure. Event-driven workflows, message queue processing, intelligent agent coordination at scale. Requires Flow Nexus account. NOT CURRENTLY INSTALLED in this environment. Use when deploying cloud-based AI swarms on Flow Nexus infrastructure.
open-montage
by DreamLab-AIAgentic video production system. Describe a video idea in natural language; the agent orchestrates research, scripting, asset generation, editing, and rendering across 11 pipelines and 49 tools. Supports zero-key mode (Piper TTS + Pexels stock + Remotion + FFmpeg) and premium APIs (ElevenLabs, Runway, Kling, Veo 3, Suno). Use when the user says "make a video", "create an explainer", "produce a trailer", "video production", "animate", or "podcast to video". From calesthio/OpenMontage.
sif-to-csv
by DreamLab-AIConvert a SIF (Standard Interchange Format) file to a clean, readable CSV or Google Sheet.
expel-lesson-extractor
by DreamLab-AIPost-task experiential learning. After each completed task with an observable terminal outcome (success or explicit failure), distil 0-N generalisable lessons from the trajectory + ExecutionTraces and store them in RuVector namespace `code-harness-lessons` as ex:DistilledLesson records (memory_type=semantic, durable). Lessons are surfaced at task start by the skill-router for similar scopes. Confidence decremented on contradiction (LLM-judge sampled 1/10 retrievals, floor 0.3, archive below). Privacy filter applied to all trace evidence before write (per ADR-008 + ADR-019).
meta-xr-sdk
by DreamLab-AIDeep integration with Meta's VR/AR developer ecosystem for Quest 2/3/3S/Pro. Covers WebXR (IWER, RATK, @react-three/xr), Meta Spatial SDK (Android), Horizon Platform SDK, hzdb CLI (40+ MCP tools), Unity MCP Extensions, and agentic VR development skills. Use when building immersive WebXR experiences, Quest-native spatial apps, mixed reality with passthrough, hand tracking, spatial anchors, plane/mesh detection, or porting 2D apps to Quest. Complements wasm-js (WASM compute), game-dev (full studio), and unreal-engine (UE5 automation).
latex-book
by DreamLab-AIConvert markdown/HTML manuscripts to arXiv-compliant LaTeX with memoir class, biblatex citations, professional typesetting, and parallel agent swarm conversion. Use when converting a multi-chapter markdown book to LaTeX for academic publication, arXiv submission, or print-ready PDF.
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.