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...
uno-csharpmarkup2
by VincentH-NetBuild or edit Uno Platform 6 UI in declarative C# with C# Markup 2 (CSharpForMarkup), instead of XAML. Use for Uno 6 apps on .NET 10/9 with MVVM or MVUX, Uno Extensions Navigation/Toolkit, and supported C# Markup 2 integrations such as LiveCharts2, ScottPlot, and Mapsui.
orleans-multiservice-pattern
by VincentH-NetModular-monolith pattern for Microsoft Orleans 10 — host multiple **logical services** inside one physical silo/microservice, with project/namespace/dependency rules that let any logical service later be extracted to its own physical microservice with minimal changes. Use when starting a new Orleans 10 backend for a single team, when deferring real microservices until genuinely needed (MonolithFirst), when organizing a codebase to follow Conway's Law, or when you want painless future migration from in-silo grain calls to generated OpenAPI HTTP clients. Scaffolds `Apis.<S>Api`, `Contracts.<S>Contract`, and `<S>Service` projects with strict dependency directions (Apis→Contracts, Apis→Service, Service→Contracts), plus a silo host. Generated by the `mcs-orleans-multiservice` template; add more logical services via `AddLogicalService.ps1 <name>` or `--Multiservice .`.
uno-agentic-support
by VincentH-NetIn-app support for agentic development of Uno Platform apps. Use when running or preparing a Uno Platform app for agent-driven execution with uno_app_start. Also use when the expected log file specified in AGENT_CONSOLE_LOG is missing or lacks "uno-agentic-support" entry, or Hot Reload / Hot Design remains visible during agent UI testing.
uno-fluent2
by VincentH-NetFluent 2 Design System for Uno Platform. Use when designing UI layouts, choosing colors, applying typography, setting elevation/shadows, using theme resources, applying lightweight styling, or implementing Fluent Design principles in WinUI/Uno XAML apps. Covers color, typography, geometry, materials, motion, iconography, spacing, elevation, and responsive breakpoints.
uno-livecharts2-theme-switching
by VincentH-NetExtends dotnet-livecharts2 for Uno Platform apps that need reliable in-app dark/light/system theme switching with LiveCharts2, shared theme palettes, central refresh of already-loaded charts, and rendered-pixel verification of chart text colors after theme changes.
uno-mvvm
by VincentH-NetUno Platform MVVM with CommunityToolkit.Mvvm: mutable ViewModels, ObservableObject, ObservableProperty on C# partial properties, RelayCommand, async commands, constructor dependency injection, x:Bind binding patterns, and Uno Navigation from ViewModels. Use when the user selected MVVM as the update model or asks for ViewModel, ICommand, INotifyPropertyChanged, ObservableObject, ObservableProperty, RelayCommand, or CommunityToolkit.Mvvm in an Uno Platform app. Do NOT use for MVUX models/feeds/states; use Studio MVUX skills instead. Do NOT use for C# Markup 2 binding syntax; combine with uno-csharpmarkup2 only when the selected markup type is C# Markup 2.
uno-test-resize-app-window
by VincentH-NetResize a running Uno Platform desktop app window on macOS for visual testing. Use when you need to verify responsive layout reflow, test breakpoints, or validate UI at different window sizes. Requires macOS Accessibility API access.
uno-xaml
by VincentH-NetUno Platform XAML correctness and performance guidance for XAML markup profiles: deferred loading with x:Load, virtualized item-template mechanics, text trimming and layout resilience, UI-bound lifecycle cleanup, UI-thread safety at the Page/control boundary, input scopes, keyboard accelerators, focus, and drag/drop caveats. Use when the selected markup type is XAML or when existing XAML needs performance, lifecycle, input, text overflow, or template fixes. Do NOT use for MVVM property/command patterns, Uno Navigation, Fluent or Material theming, visual design/layout composition, Uno C# Markup, or C# Markup 2; combine with the selected update-model, design-system, navigation, and markup skills instead.
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.