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...
ev-transition-monitor
by MarketcheckHubEV investment thesis and equity research intelligence. Triggers: "EV market update", "EV adoption rate", "EV vs ICE pricing", "Tesla market position", "EV investment signal", "electric vehicle trends", "EV depreciation", "EV price parity", "hybrid adoption", "electrification progress", "EV days supply", "which OEMs are winning EV", "EV penetration by state", "EV investment thesis", "EV stock signal", "electrification transition risk", tracking electric vehicle market dynamics for investment thesis development and equity research.
ev-lending-risk-monitor
by MarketcheckHubEV intelligence for lender sales reps. Triggers: "EV lending risk", "EV depreciation for lending", "should I lend on EVs", "EV market update for sales", "which EVs hold value", "EV vs ICE for lending", "EV lending programs", "EV residual value", "battery risk", understanding EV market dynamics for advising dealers on EV lending programs and managing EV lending risk.
ev-transition-monitor
by MarketcheckHubEV lending risk and portfolio exposure analysis. Triggers: "EV market update", "EV adoption rate", "EV vs ICE pricing", "Tesla market position", "EV lending risk", "electric vehicle trends", "EV depreciation", "EV price parity", "hybrid adoption", "electrification progress", "EV days supply", "which OEMs are winning EV", "EV penetration by state", "EV residual risk", "EV portfolio exposure", "EV collateral risk", tracking electric vehicle market dynamics for lending risk assessment and portfolio management.
ev-transition-monitor
by MarketcheckHubEV market dynamics for OEM electrification strategy. Triggers: "EV market update", "EV adoption rate", "EV vs ICE pricing", "electrification progress", "EV penetration by state", "EV price parity", "hybrid adoption", "EV launch planning", "regional EV heatmap", "EV competitive position", "which OEMs are winning EV", "EV days supply", "electric vehicle trends", "EV strategy", "electrification strategy", tracking electric vehicle market dynamics for OEM strategy, regional launch planning, or competitive EV positioning.
deal-finder
by MarketcheckHubBest-deal sourcing and negotiation leverage. Triggers: "find me the best deal", "cheapest option near me", "best price on a", "deal finder", "is this a good price", "should I buy now or wait", "compare deals", "negotiate this price", "find a car for my customer", sourcing best-priced vehicles, validating deal fairness, building negotiation leverage with market data.
depreciation-tracker
by MarketcheckHubValue erosion intelligence for auction timing. Triggers: "depreciation rate", "value retention", "which cars hold value", "which cars are losing value fastest", "depreciation curve for [model]", "residual trends", "fast depreciators", "consignment urgency by depreciation", understanding how quickly vehicles are losing value, which affects consignment timing and expected hammer prices.
run-list-analyzer
by MarketcheckHubEvaluate consigned VINs before sale day. Triggers: "evaluate run list", "check these consigned VINs", "predict which will sell", "sale day prep", "analyze my run list", "price the auction list", "how will these VINs do at auction", "expected hammer prices", "sell-through prediction", evaluating a batch of VINs already consigned for an upcoming auction event.
depreciation-tracker
by MarketcheckHubVehicle depreciation and value retention analysis. Triggers: "depreciation rate", "value retention", "residual value", "how fast is it losing value", "which cars hold value", "EV depreciation", "price trend over time", "brand value ranking", "depreciation curve", "residual forecast", "MSRP parity", "price over sticker", "incentive effectiveness", "geographic value variance", "which states have higher prices", residual value forecasting, segment value comparisons, brand retention rankings, MSRP-to-transaction price tracking for multi-location dealer groups.
vehicle-appraiser
by MarketcheckHubComparable-backed vehicle valuation. Triggers: "appraise this vehicle", "what's it worth", "trade-in value", "comparable analysis", "fair market value", "wholesale vs retail", "appraisal report", "how much should I offer", "vehicle valuation", defensible valuations for trade-ins, acquisitions, or retail pricing decisions.
vehicle-appraiser
by MarketcheckHubInsurance valuation with comparable evidence. Triggers: "appraise this vehicle", "what's it worth", "insurance valuation", "comparable analysis", "fair market value", "pre-loss value", "appraisal report", "settlement valuation", "vehicle valuation", "claims appraisal", building a defensible, comparable-backed vehicle valuation for insurance claims, total-loss determinations, or settlement pricing decisions.
vehicle-appraiser
by MarketcheckHubCollateral valuation with transaction evidence. Triggers: "appraise this vehicle", "what's it worth", "collateral value", "comparable analysis", "fair market value", "wholesale vs retail", "collateral valuation report", "LTV calculation", "vehicle valuation", "loan collateral check", building a defensible, comparable-backed vehicle valuation for collateral assessment, portfolio revaluation, or loan origination decisions.
claims-valuation
by MarketcheckHubTotal-loss determination and settlement pricing. Triggers: "total loss valuation", "claims value", "settlement offer", "salvage estimate", "insurance claim pricing", "total loss threshold", "what's the claim worth", "settlement range", "pre-loss value", "diminished value", "total loss determination", insurance claim vehicle valuation, total-loss determination, settlement pricing, or salvage value estimation.
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.