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...
haze-blur
by Kototoro-appExpert guidance on Haze blur effect configuration, styling, materials, progressive blur, masking, and input scale. Use when configuring blur radius, tint/color effects, noise, HazeMaterials/CupertinoMaterials/FluentMaterials, gradient blurs, or blur masking.
haze-custom-effects
by Kototoro-appExpert guidance on building custom VisualEffect implementations for Haze. Use when creating custom shader effects, non-blur visual effects, or extending Haze with new effect types.
haze-faq
by Kototoro-appFrequently asked questions about Haze. Use when asked about Haze vs Modifier.blur, cross-platform blur consistency, or which Android versions Haze supports.
haze-migration
by Kototoro-appGuide for migrating from Haze v1.x to v2.0. Use when upgrading Haze dependencies, fixing compilation errors after a version bump, or converting v1 API calls to v2 syntax.
haze-patterns
by Kototoro-appExpert guidance on common Haze usage patterns and recipes. Use when implementing Scaffold blur, sticky headers, overlapping effects, dialog blur, or deep hierarchy state sharing with Haze.
haze-performance
by Kototoro-appExpert guidance on Haze performance optimization, platform-specific backends (Android API levels, Skiko/Skia), benchmarks, and known limitations. Use when diagnosing blur performance issues, choosing platform strategies, or benchmarking Haze in production.
haze
by Kototoro-appExpert guidance on the Haze library for Compose Multiplatform visual effects (blur, glassmorphism). Use when asked about background blur, glass effects, haze setup, or any Haze-related question. This is the routing skill — load specific sub-skills for detailed guidance.
kototoro-browse-source-scroll
by Kototoro-appDiagnose and fix Kototoro Browse/Explore page content source scrolling jank when many sources exist. Use for ExploreHostScreen, ExploreViewModel, source quick access, LazyColumn source rows, source grouping, browse recommendation loading, and tab switching performance.
kototoro-details-return-stability
by Kototoro-appDiagnose and fix Kototoro Android issues where returning from reader or video player to details causes flicker, cover reload, panorama reload, progress reset, status bar jump, Compose subtree disposal, or Activity recreation. Use for DetailsScreen, VideoPlayerActivity, MainActivity, system bars/insets, shared transition, Coil cache key, and reader page identity regressions.
kototoro-main-loading-stability
by Kototoro-appDiagnose and fix Kototoro main screen loading flicker, blank flashes, chrome jumps, transient empty states, and route switching flashes. Use for KototoroApp, AppNavGraph, FavoritesHostScreen, FavouritesContainerViewModel, DiscoverViewModel, collectAsStateWithLifecycle initial values, top/bottom bar offsets, and main route transitions.
android-emulator-skill
by Kototoro-appProduction-ready scripts for Android app testing, building, and automation. Provides semantic UI navigation, build automation, log monitoring, and emulator lifecycle management. Optimized for AI agents with minimal token output.
kototoro-haze-sheet-artifacts
by Kototoro-appDiagnose and fix Kototoro-specific Compose Material3 Sheet/Dialog/Popup plus Haze/GlassSurface visual artifacts, especially extra light/gray rectangles, white haze plates, rectangular bounds around rounded glass panels, or backgrounds that appear to grow while dragging display options and other glass sheets.
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.