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...
ue5-world-interaction
by UnrealXuUE5.6/UE5.7 world interaction systems for pickups, spawners, overlap/trace checks, and visual feedback. Use when requests involve interactive world actors, spawn logic, pickup behavior, interaction radius checks, success/failure feedback, and actor lifecycle control.
ue5-debug-validation
by UnrealXuUE5.6/UE5.7 debugging and validation workflow for logs, asset checks, and regression triage. Use when requests involve troubleshooting why gameplay does not work, validating expected output, narrowing minimal repro, and producing concrete fix steps.
ue5-module-router
by UnrealXuRoute UE5.6/UE5.7 questions to the most precise skill using module names, aliases, intent keywords, and layer context. Works for explicit module prompts (RenderCore, AIModule, AssetRegistry) and natural language requests.
ue5-pcg-building
by UnrealXuUE5.6/UE5.7 PCG building generation workflow for modular buildings, blockouts, facade rules, and runtime generation. Use when requests involve Procedural Content Generation (PCG), Shape Grammar, lot-based building spawn, deterministic random seeds, density/filter pipelines, or converting designer constraints into reusable PCG graphs.
ue5-performance-packaging
by UnrealXuUE5.6/UE5.7 performance and packaging readiness workflow. Use when requests involve PIE performance checks, runtime stat review, pre-package validation, build configuration sanity, and release readiness checklists.
ue5-save-load-replication
by UnrealXuUE5.6/UE5.7 save/load and multiplayer replication workflow for gameplay systems. Use when requests involve SaveGame schema design, serialization, restore pipelines, RepNotify handling, RPC entry points, and server-authoritative validation.
ue5-ui-umg-slate
by UnrealXuUE5.6/UE5.7 UI development workflow using UMG and Slate integration. Use when requests involve Widget Blueprint setup, Slate host widgets, lifecycle binding, input and focus handling, tooltip behavior, or viewport clamping logic.
ue5-architecture
by UnrealXuUE5.6/UE5.7 architecture planning and module boundary design for Unreal projects. Use when requests involve module layout, Build.cs dependencies, reflection exposure strategy, Public/Private API boundaries, naming conventions, and preventing circular dependencies.
ue5-auto-assistant
by UnrealXuUE5.6/UE5.7 automatic assistant entry for beginners. Use when users ask Unreal questions without naming a specific skill. Auto-route to the most precise UE5 skill and recommend dedicated MCP tools.
ue5-blueprint-workflow
by UnrealXuUE5.6/UE5.7 Blueprint graph workflow for feature implementation, input events, node wiring, and graph validation. Use when requests involve adding Blueprint logic, keyboard input behavior, function chains, event graph edits, or pin-level connection guidance.
ue5-cpp-gameplay
by UnrealXuUE5.6/UE5.7 gameplay C++ implementation for Actors, Components, DataAssets, and gameplay logic. Use when requests ask to write .h/.cpp pairs, expose UPROPERTY/UFUNCTION to Blueprint, use GameplayTags, or build reusable component-based systems.
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.