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.
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grimoire-readme-guide
by anton-kochevCreate and review README files following industry best practices. Use for writing new READMEs, improving existing ones, checking README quality, or adding badges. Triggers: readme, documentation, project docs, repository documentation, getting started guide, shields badges.
your-skill-name
by anton-kochevCreate and maintain custom skills for Claude Code following official Anthropic patterns. Use when creating new skills, updating existing skills, or organizing skill documentation.
grimoire-srs-generator
by anton-kochevProduce a Software Requirements Specification (SRS) conforming to ISO/IEC/IEEE 29148:2018, with requirements written in EARS notation and a traceability matrix. A user-executable command: elicit what the codebase can't tell you, draft and self-check the requirements, gate on explicit approval, then emit a tailored SRS document. Use this skill whenever the user asks to create, write, draft, author, or 'spec out' a software requirements specification, an SRS, a requirements document, or a formal requirements spec — for a brand-new (greenfield) product or for a change to an existing codebase. Also use it when the user mentions 'ISO 29148', 'IEEE 830', 'functional and non-functional requirements', or wants requirements captured before any user-stories or backlog work.
grimoire-unit-testing-dotnet
by anton-kochevC#/.NET unit testing specialist. Framework selection, patterns, and best practices for xUnit, TUnit, NUnit, Moq, and NSubstitute. Use when writing tests for .cs files, configuring test projects, or asking about .NET testing patterns, mocking, assertions, async testing, FluentAssertions alternatives.
grimoire-unit-testing-go
by anton-kochevGo unit testing specialist. Patterns and best practices for the testing stdlib, testify, and gomock. Use when writing tests for .go files, table-driven tests, or asking about Go testing patterns, test helpers, mocking interfaces, benchmarks.
grimoire-unit-testing-python
by anton-kochevPython unit testing specialist. Framework selection, patterns, and best practices for pytest, unittest, and hypothesis. Use when writing tests for .py files, configuring pytest, or asking about Python testing patterns, fixtures, parametrize, mocking, async testing.
grimoire-unit-testing-rust
by anton-kochevRust unit testing specialist. Patterns and best practices for the built-in test framework, mockall, and proptest. Use when writing tests for .rs files, or asking about Rust testing patterns, test modules, mocking traits, property-based testing, integration tests.
grimoire-dotnet-feature-workflow
by anton-kochevOrchestrate end-to-end .NET feature development through the Explore, Plan, Code, Verify, Review workflow. A user-invoked command that drives hands-off, TDD-based delivery — spawning a specialized agent at each phase and gating on user approval before implementation.
grimoire-srs-to-user-stories
by anton-kochevAnalyze a Software Requirements Specification (SRS) and generate well-crafted Scrum user stories grouped under Epics, ready for backlog grooming. A user-invoked command that turns a requirements spec into actionable Scrum artifacts.
grimoire-translate-ua
by anton-kochevUse when asked to translate text into Ukrainian. Converts English prose to Ukrainian while preserving all technical terms, code snippets, file paths, and technical language in English.
sample-skill
by anton-kochevA sample skill for testing
grimoire-conventional-commit
by anton-kochevGenerate git commits following Conventional Commits 1.0.0. Use for /conventional-commit, git commit, or when committing changes.
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.