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...
fsharp-persistence
by heimeshoffImplement data persistence using SQLite with Dapper, JSON file storage, or event sourcing patterns. Use when adding database tables, CRUD operations, file storage, or event logs. Creates code in src/Server/Persistence.fs with patterns for queries, transactions, relationships, and async I/O. Includes SQLite schema creation, parameterized queries, and proper connection management.
fsharp-frontend
by heimeshoffImplement F# frontend using Elmish MVU architecture with Feliz for React components. Use when creating UI, managing client state, or building interactive features with Elmish.React + Feliz. Creates state management in src/Client/State.fs (Model/Msg/update/Cmd) and UI in src/Client/View.fs. Follows strict MVU pattern with RemoteData for async operations and TailwindCSS/DaisyUI for styling.
fsharp-feature
by heimeshoffOrchestrates end-to-end F# full-stack feature development following Elmish MVU + Giraffe + Fable.Remoting patterns. Use when user requests "add X feature", "implement Y", or needs guidance through complete stack implementation. Guides through: Shared types → Backend (validation/domain/persistence/API) → Frontend (state/view) → Tests. Requires project with src/Shared, src/Server, src/Client structure.
fsharp-validation
by heimeshoffCreate comprehensive validation logic for F# backends with field validators, entity validation, error accumulation, and async validation. Use when implementing input validation, complex validation rules, or need to validate API requests before processing. Creates reusable validators in src/Server/Validation.fs with patterns for required fields, length checks, email, business rules, and database checks.
fsharp-backend
by heimeshoffImplement F# backend using Giraffe + Fable.Remoting with proper separation: Validation → Domain (pure logic) → Persistence (I/O) → API. Use when implementing server-side logic, API endpoints, or business rules. Ensures validation at boundaries, pure domain functions, and proper error handling with Result types. Creates code in src/Server/ files: Validation.fs, Domain.fs, Persistence.fs, Api.fs.
fsharp-tests
by heimeshoffWrite comprehensive tests using Expecto for F# applications including domain logic, validation, async operations, and state transitions. Use when implementing tests, ensuring code quality, or verifying functionality. Creates test files in src/Tests/ with patterns for unit tests, property tests, and async tests. Tests domain logic (pure functions), validation rules, persistence operations, and Elmish state management.
fsharp-shared
by heimeshoffDefine shared domain types and API contracts for F# full-stack applications using records, discriminated unions, and Fable.Remoting interfaces. Use when starting new features, defining data structures, or creating API contracts shared between client and server. Creates types in src/Shared/Domain.fs and API interfaces in src/Shared/Api.fs. Ensures type safety across the stack with compile-time checking.
business-research
by heimeshoffStructured business research for this team. Invoke with /business-research. Reads memory/project/INDEX.md to learn the active project and grounds in the active vision/research artifacts. Supports two modes — (1) competitive analysis: maps the competitor landscape, features, pricing, and positioning gaps; (2) customer journey: traces the target user's path, pain points, decision triggers, and emotional states. Delegates web searches to multiple parallel specialized agents. Always produces an unopinionated research.md artifact saved to the active session folder. Use whenever market intelligence, competitor data, user journey insight, or factual grounding is needed before making a product decision.
research
by heimeshoffGeneral-purpose research skill for this team. Invoke with /research. Reads memory/project/INDEX.md to learn the active project and grounds in the active vision. Complements /business-research (which covers competitive analysis and customer journeys) by handling technical, regulatory, UX pattern, and open-ended topic research. Always produces an unopinionated research.md artifact saved to the active session folder. Use when the team needs depth on a topic that is not a competitor landscape or customer journey map.
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.