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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leiloeiro-mercado
by sickn33Analise de mercado imobiliario para leiloes. Liquidez, desagio tipico, ROI, estrategias de saida (flip/reforma/renda), Selic 2025 e benchmark CDI/FII.
cap-rate-calculator
by revfactoryReal estate yield calculator. Reference formulas and models used by the profitability-analyst agent for quantitative investment return analysis. Use for requests involving 'Cap Rate', 'yield analysis', 'DCF', or 'cash flow analysis'. Tax advisory and loan underwriting are out of scope.
real-estate-analyst
by revfactoryReal estate investment analysis pipeline. An agent team collaborates to produce market research, location analysis, profitability analysis, risk assessment, and investment reports. Use this skill for requests such as 'analyze this real estate', 'apartment investment analysis', 'studio apartment yield', 'real estate market research', 'location analysis', 'real estate investment report', 'buy vs lease', 'reconstruction investment analysis', 'commercial property yield analysis', and other general real estate investment analysis tasks. Actual purchase contracts, brokerage services, interior design, and property management are outside the scope of this skill.
real-estate-analyst
by revfactory부동산 투자 분석 파이프라인. 시장조사, 입지분석, 수익성, 리스크 평가, 투자 보고서까지 에이전트 팀이 협업 생성한다. '부동산 분석해줘', '아파트 투자 분석', '오피스텔 수익률', '부동산 시장 조사', '입지 분석', '부동산 투자 보고서', '매매 vs 전세', '재건축 투자 분석', '상가 수익률 분석' 등 부동산 투자 분석 전반에 이 스킬을 사용한다. 실제 매매 계약, 중개 서비스, 인테리어 설계, 임대 관리는 이 스킬의 범위가 아니다.
realestate-compare
by zubair-trabzadaSide-by-Side Property Comparison — takes two addresses and compares across price, specs, rental income, neighborhood, and investment potential with a winner per category and overall recommendation
realestate-commercial
by zubair-trabzadaCommercial Property Analysis — NOI, cap rate, expense ratio, tenant mix, vacancy, debt coverage, replacement cost, and lease analysis with Commercial Score (0-100)
realestate-screen
by zubair-trabzadaProperty Screener — searches for properties matching investment criteria with pre-built screens for Cash Flow, Appreciation, BRRRR, First-Time Buyer, and Short-Term Rental strategies plus custom criteria support
realestate-quick
by zubair-trabzada60-Second Property Snapshot — quick assessment without subagents for fast property evaluation with signal, key factors, and CTA for full analysis
realestate-neighborhood
by zubair-trabzadaNeighborhood Analysis — schools, crime, walkability, demographics, amenities, growth trajectory, and natural disaster risk with Neighborhood Score (0-100)
realestate-market
by zubair-trabzadaLocal Market Analysis — median prices, inventory, days on market, price trends, rental conditions, economic drivers, and market classification with Market Score (0-100)
full-property-analysis-orchestrator
by zubair-trabzadaLaunches 5 parallel AI agents to produce a comprehensive property analysis with composite Property Score (0-100), investment grade, and actionable recommendations
comparable-sales-analysis
by zubair-trabzadaFinds and analyzes 5-10 comparable recent sales to estimate fair market value, calculate price adjustments, and score the property's value proposition
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.