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...
leasing-manager-residential-multifamily
by mariourquiaFunnel operator for a middle-market multifamily property. Owns lead-to-lease conversion, tour quality, new-lease pricing within pricing-overlay bounds, renewal outreach cadence, concession discipline, and site marketing execution. Routes pricing and concession exceptions to the property_manager.
used-car-search
by KnoWhizSearch for used cars from reputable large dealership sources and present a curated catalog. Use when the user asks about buying a used car, finding a car within a budget, searching for vehicles in their area, or any variant like "find me a car under $X", "used cars near me", "looking for a reliable sedan", or "what's available at CarMax". Trigger even when the user doesn't explicitly say "used car search" but is clearly shopping for a vehicle purchase.
client-offer-history
by igptaiReconstructs the complete offer history for a specific client from email — every offer made, at what price, on which property, what the outcome was, and what feedback was received. Use when an agent wants to brief a client on their offer history or prepare strategy for a new offer. Triggers on "client offer history", "what offers have we made", "offer history", "previous offers", "offer track record", "what happened with past offers".
listing-expiry-tracker
by igptaiFinds every active listing agreement referenced in email and surfaces those approaching expiry — including the expiry date, notice requirements, and whether the seller has indicated intent to renew or withdraw. Use when an agent wants to proactively manage listing agreement renewals before they lapse. Triggers on "listing expiry", "listing agreements expiring", "listings coming up for renewal", "which listings are expiring", "listing renewal", "expiring listings".
vendor-coordination-tracker
by igptaiTracks every outstanding vendor coordination item across active transactions — inspectors, appraisers, contractors, title companies, lenders, and attorneys — surfacing what has been scheduled, what is pending, and what is blocking a transaction. Use when an agent wants to ensure all third-party coordination is on track across their deals. Triggers on "vendor coordination", "third party status", "what's pending with vendors", "inspector scheduler", "transaction vendor tracker", "who have I not heard back from".
real-estate-agent
by clawicYour personal real estate agent. Find properties, get alerts on deals, sell or rent your home, and navigate any property decision.
tour-booking
by Demerzels-labSub-agent for outbound listing-office calls to request and confirm property showing slots using a provided call.
action-suggester
by Demerzels-labGenerate non-binding follow-up action suggestions from lead summaries or lead lists.
lead-extractor
by Demerzels-labExtract structured real-estate lead records from parsed message objects.
ontario-professional-real-estate-sales
by gabrielmoreiraA professional real estate assistant named Terra for Ontario that helps users buy and sell homes. It features advanced intent classification logic to determine user goals (listings, renovations, subscriptions, or service links) while maintaining a helpful, professional persona.
ontario-witty-real-estate-agent
by gabrielmoreiraA charismatic real estate assistant for Ontario that autonomously classifies user intent (buyer/seller/investor) and transaction timing. It uses a witty, Ryan Reynolds-esque persona to guide users toward specific sales goals while adhering to strict data consent protocols and tool-based workflows.
new-home-consultant
by HaibarakikuExpert new home sales consultant specializing in new construction, developer representation, and buyer advocacy in new developments
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.