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.
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crossbeam-ops
by mikeOnBreezeOperations manual for the CrossBeam ADU Permit Assistant. Teaches AI agents how to operate the deployed system — trigger flows, check status, read results, navigate the UI, and query the database.
adu-corrections-complete
by mikeOnBreezeGenerates the final response package for ADU permit corrections — the second half of the corrections pipeline. This skill should be used after adu-corrections-flow has produced its analysis files and the contractor has answered questions. It reads the research artifacts (categorized corrections, state law findings, city research, sheet observations) plus contractor answers, and produces four deliverables — a response letter to the building department, a professional scope of work, a corrections status report, and per-sheet annotations. Triggers when a session directory contains corrections analysis files and a contractor_answers.json has been provided. This skill runs as a cold start — it has no conversation history from the analysis phase and relies entirely on the file artifacts.
adu-targeted-page-viewer
by mikeOnBreezeExtracts construction plan PDFs into page PNGs, reads the sheet index to build a sheet-to-page manifest, and enables targeted viewing of specific sheets. This skill should be used when a corrections letter references specific plan sheets (e.g., "Sheet A3", "Detail 2/S3.1") and those sheets need to be located and analyzed within the PDF binder. Much faster than full plan extraction — builds the sheet manifest in under 2 minutes, then individual sheet lookups are instant. Triggers when a plan PDF needs to be navigated by sheet reference, or when the corrections interpreter needs to look at specific pages.
cc-guide
by mikeOnBreezeClaude Code documentation expert. This skill should be used when the user asks questions about Claude Code features, settings, hooks, skills, MCP servers, keyboard shortcuts, IDE integrations, Agent SDK, Claude API, or Anthropic SDK. Invoke with /cc-guide followed by a question. Examples: "/cc-guide how do hooks work", "/cc-guide what keyboard shortcuts are available", "/cc-guide how do I set up agent teams".
fal-ai
by mikeOnBreezeThis skill enables AI video generation from images AND text-to-speech voiceover generation using Fal.ai's API. Use this skill when the user asks to (1) generate videos from images (image-to-video), or (2) generate voiceovers/narration from text (text-to-speech via ElevenLabs). Works seamlessly with the nano-banana skill for image-to-video workflows. IMPORTANT: Check references/ for latest models and pricing - AI models change frequently.
product-demo-video
by mikeOnBreezeGenerate 2-minute product demo videos for technical projects (GitHub repos, APIs, technical products). Runs parallel build options with structured critic review. Use when demoing code, explaining architecture, or creating explainer videos.
shadcn
by mikeOnBreezeExpert guide for ShadCN UI component library with Next.js. Use when working with ShadCN/UI projects including (1) Initial setup and configuration, (2) Adding and customizing components, (3) Theming and styling patterns, (4) Avoiding common mistakes like hardcoding styles instead of using variants/design tokens. Critical for maintaining consistent, reusable component patterns instead of one-off hardcoded implementations.
adu-city-research
by mikeOnBreezeResearches city-level ADU regulations, municipal codes, and standard details for any California city. This skill supports three research modes — Discovery (WebSearch to find key URLs), Targeted Extraction (WebFetch to pull content from discovered URLs), and Browser Fallback (Chrome MCP for cities with difficult websites). When used standalone, run all three modes sequentially. When invoked by an orchestrator (e.g., adu-corrections-flow), run in the specified mode only. Triggers on city-specific ADU questions, corrections letter items referencing municipal code, or when the California ADU state-level skill indicates a question requires local jurisdiction rules.
adu-corrections-flow
by mikeOnBreezeAnalyzes ADU permit corrections letters — the first half of the corrections pipeline. Reads the corrections letter, builds a sheet manifest from the plan binder, researches state and city codes, views referenced plan sheets, categorizes each correction item, and generates informed contractor questions. This skill should be used when a contractor receives a city corrections letter for an ADU permit. It coordinates three sub-skills (california-adu for state law, adu-city-research for city rules, adu-targeted-page-viewer for plan sheet navigation) to produce research artifacts and a UI-ready questions JSON. Does NOT generate the final response package — that is handled by adu-corrections-complete after the contractor answers questions. Triggers when a corrections letter PDF/PNG is provided along with the plan binder PDF.
adu-corrections-pdf
by mikeOnBreezeFormats a draft corrections letter (markdown) into a professional PDF. Single-purpose formatting sub-agent — no research. Receives markdown from the research agent, generates a styled PDF using the document-skills/pdf primitive, and returns a screenshot for QA. If the main agent finds issues in the screenshot, it will re-invoke this skill with fix instructions.
adu-pdf-extraction
by mikeOnBreezeThis skill extracts construction PDF plan binders into agent-consumable formats. It should be used when a contractor or homeowner provides a PDF binder of construction plans (site plans, floor plans, structural drawings, Title 24 reports) that needs to be parsed for permit review, corrections response, or plan check analysis. Produces three outputs: page PNGs for vision analysis, structured markdown per page via vision extraction, and a JSON manifest for routing.
adu-plan-review
by mikeOnBreezeCity-side ADU plan review — the flip side of adu-corrections-flow. Takes a plan binder PDF + city name, reviews each sheet against code-grounded checklists, checks state and city compliance, and generates a draft corrections letter with confidence flags and reviewer blanks. Coordinates three sub-skills (california-adu for state law, adu-city-research OR a dedicated city skill for city rules, adu-targeted-page-viewer for plan extraction). Triggers when a city plan checker uploads a plan binder for AI-assisted review.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
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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.