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...
yard-management
by kishorkukrejaWhen the user wants to optimize yard operations, manage trailer parking, or improve dock door utilization. Also use when the user mentions "yard management," "trailer tracking," "yard jockey," "drop trailer program," "trailer pool," "dock scheduling," or "gate management." For cross-dock operations, see cross-docking. For warehouse design, see warehouse-design.
aerospace-supply-chain
by kishorkukrejaWhen the user wants to optimize aerospace and defense supply chains, manage long lead-time components, handle MRO operations, or ensure AS9100 compliance. Also use when the user mentions "aerospace supply chain," "aviation manufacturing," "MRO supply chain," "AS9100," "aircraft parts," "defense procurement," "AOG support," "airworthiness," "ITAR compliance," "aerospace certification," or "aviation aftermarket." For general manufacturing, see production-scheduling. For quality, see quality-management.
3d-bin-packing
by kishorkukrejaWhen the user wants to pack 3D boxes into containers, optimize 3D space utilization, or solve container loading problems. Also use when the user mentions "3D packing," "container packing," "box packing," "3D bin packing problem," "cube packing," "three-dimensional packing," "cargo loading," or "space optimization." For pallet-specific loading, see pallet-loading. For container logistics, see container-loading-optimization.
2d-bin-packing
by kishorkukrejaWhen the user wants to pack 2D rectangular items into bins, optimize 2D cutting patterns, or minimize material waste in 2D packing. Also use when the user mentions "2D packing," "rectangular packing," "sheet packing," "2D bin packing problem," "guillotine cutting," "two-dimensional packing," or "rectangle packing optimization." For 1D problems, see 1d-cutting-stock. For 3D problems, see 3d-bin-packing.
food-beverage-supply-chain
by kishorkukrejaWhen the user wants to optimize food and beverage supply chains, manage perishability, ensure food safety, or handle retail distribution. Also use when the user mentions "food supply chain," "beverage distribution," "HACCP," "food safety," "perishable logistics," "shelf life management," "FEFO," "farm to fork," "CPG distribution," "grocery supply chain," or "fresh produce logistics." For retail allocation, see retail-allocation. For promotional planning, see promotional-planning.
nesting-optimization
by kishorkukrejaWhen the user wants to nest irregular shapes on sheets, pack non-rectangular parts optimally, or solve nesting problems for manufacturing. Also use when the user mentions "nesting," "irregular shape packing," "polygon nesting," "shape nesting," "marker making," "leather nesting," "sheet metal nesting," "garment cutting optimization," or "CNC nesting." For rectangular items, see 2d-cutting-stock. For 1D problems, see 1d-cutting-stock.
process-optimization
by kishorkukrejaWhen the user wants to optimize manufacturing processes, improve throughput, reduce cycle times, or simulate process performance. Also use when the user mentions "process improvement," "bottleneck analysis," "simulation," "discrete-event simulation," "throughput optimization," "cycle time reduction," "process efficiency," "queuing theory," "process mapping," or "capacity analysis." For lean methods, see lean-manufacturing. For scheduling, see production-scheduling.
automotive-supply-chain
by kishorkukrejaWhen the user wants to optimize automotive manufacturing supply chains, manage tier suppliers, implement JIT production, or handle automotive-specific logistics. Also use when the user mentions "automotive manufacturing," "OEM supply chain," "tier 1/2/3 suppliers," "sequenced parts delivery," "just-in-time automotive," "vehicle assembly," or "automotive aftermarket." For general manufacturing, see production-scheduling. For lean principles, see lean-manufacturing.
carbon-footprint-tracking
by kishorkukrejaWhen the user wants to measure, track, or reduce carbon emissions in the supply chain. Also use when the user mentions "carbon accounting," "GHG emissions," "Scope 1/2/3 emissions," "carbon footprint calculation," "emissions reporting," "carbon reduction," "climate impact," "decarbonization," or "emissions baseline." For circular economy approaches, see circular-economy. For sustainable sourcing, see sustainable-sourcing.
vrp-backhauls
by kishorkukrejaWhen the user wants to solve VRP with Backhauls (VRPB), optimize routes with both deliveries and pickups, or handle reverse logistics. Also use when the user mentions "VRPB," "backhaul optimization," "linehaul and backhaul," "delivery and pickup routes," "reverse logistics," or "return pickups." Backhauls are pickups that occur AFTER all deliveries on a route. For paired pickup-delivery, see pickup-delivery-problem.
vrp-time-windows
by kishorkukrejaWhen the user wants to solve VRP with time windows (VRPTW), optimize routes with delivery time constraints, or handle appointment scheduling. Also use when the user mentions "VRPTW," "time window routing," "scheduled deliveries," "appointment routing," "delivery windows," "earliest/latest delivery," or "hard/soft time windows." For basic VRP, see vehicle-routing-problem.
2d-cutting-stock
by kishorkukrejaWhen the user wants to cut 2D sheets optimally, minimize waste in rectangular sheet cutting, or solve two-dimensional cutting stock problems. Also use when the user mentions "2D cutting," "sheet cutting optimization," "panel cutting," "glass cutting," "steel plate cutting," "guillotine cutting patterns," "two-stage cutting," or "2D trim loss." For 1D problems, see 1d-cutting-stock. For bin packing, see 2d-bin-packing. For irregular shapes, see nesting-optimization.
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.