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...
brand-colors
by breverdbidderUse when generating UI components, checking color compliance, or designing frontend elements for BidDeed.AI or ZoneWise. Triggers on: brand colors, Navy #1E3A5F, Orange #F59E0B, Inter font, dark background #020617, UI component, Tailwind, frontend design, brand compliance.
brevard-site-development
by breverdbidderBrevard County site plan development expertise. Invoke when analyzing parcels, checking zoning, or navigating the permit process in Brevard County, Florida.
construction-takeoff
by breverdbidderAutomated construction quantity surveying from PDF plans using AI. Processes construction drawings, extracts quantities/materials, analyzes competitor solutions via Firecrawl, calculates costs, and generates Excel-based takeoff reports for stakeholder collaboration.
deal-intel
by breverdbidderPoint at any folder of foreclosure documents (PDFs, DOCX, court filings) and get structured analysis. Extracts judgment amounts, property addresses, plaintiff names, lien positions, and generates Supabase-ready JSON. Adapted from Mark Kashef's /file-intel pattern.
zonewise-district-lookup
by breverdbidderLooks up zoning district information for Florida properties across 17 jurisdictions and 273 Municode-verified districts. Use when determining permitted uses, setbacks, density limits, height restrictions, or zoning compliance. Returns regulatory requirements from verified municipal codes. Triggers on: zoning lookup, permitted uses, setback requirements, land use, district regulations, development feasibility, zoning code, what can I build.
zonewise-github-deployment
by breverdbidderGitHub deployment operations for ZoneWise.AI projects. Includes automated git operations, GitHub Actions workflow triggers, Cloudflare Pages deployment, and post-deploy verification. Repos include brevard-bidder-scraper, zonewise, life-os, spd-site-plan-dev. Uses REST API (not git clone). Triggers on: deploy, GitHub push, commit, workflow, CI/CD, Cloudflare, GitHub Actions.
auction-pipeline
by breverdbidderBidDeed.AI auction intelligence pipeline operations. Use when triggering scrapers, running analysis, generating reports, debugging pipeline stages, or checking county auction data. Covers the 12-stage Everest Ascent pipeline: Discovery → Scraping → Title → Lien Priority → Tax Certs → Demographics → ML Score → Max Bid → Decision Log → Report → Disposition → Archive.
lien-discovery-skill
by breverdbidderDiscover and analyze lien positions from AcclaimWeb and RealTDM to detect HOA foreclosures and senior mortgage survival
max-bid-calculator-skill
by breverdbidderCalculate maximum bid using BidDeed formula and bid/judgment ratios to determine BID/REVIEW/SKIP decisions
zonewise-site-analysis
by breverdbidderPerforms comprehensive site analysis for development feasibility in Florida. Calculates buildable area considering setbacks, FAR, lot coverage, and height limits. Identifies constraints including flood zones, wetlands, and easements. Determines approval pathway (by-right vs variance). Generates professional site analysis reports. Triggers on: site analysis, buildable area, FAR calculation, development feasibility, setback analysis, lot coverage, site constraints.
d1-pathway-reports
by breverdbidderGenerate professional D1 swimming recruitment pathway reports for Michael Shapira with XGBoost analysis, roster progressions, competition calendar, and training recommendations
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.