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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moonbit-docs-maintainer
by moonbitlangUse when maintaining the moonbitlang/moonbit-docs repository, including Sphinx docs under next/, MoonBit examples under next/sources/, error-code documentation, gettext translations, or the moonbit-tour web app. This skill routes to focused references and validation commands for repo maintenance work.
snapbox-testing
by moonbitlangWrite and maintain Rust CLI/integration tests with snapbox 0.6+ using command assertions, inline snapshots, and minimal wildcard/redaction patterns. Use when replacing manual `get_output` parsing, stabilizing cross-platform snapshots, or refining existing snapbox assertions.
ocaml2moonbit-migration
by moonbitlangGuide for migrating OCaml projects, libraries, modules, and test suites to idiomatic MoonBit. Use when translating OCaml code to MoonBit, planning a large OCaml-to-MoonBit port, preserving byte/string-heavy behavior, replacing OCaml variants/records/exceptions/refs/arrays, mapping OCaml APIs to MoonBit packages, or building verification and test strategy for a migration.
moonbit-proof
by moonbitlangUse when writing or refactoring proof-carrying code in MoonBit, especially for Why3-backed specifications, abstraction functions, representation invariants, proof assertions, recursive verified data structures, or reducing trusted proof bridges.
moonbit-refactoring
by moonbitlangRefactor MoonBit code to be idiomatic: shrink public APIs, convert functions to methods, use pattern matching with views, add loop invariants, and ensure test coverage without regressions. Use when updating MoonBit packages or refactoring MoonBit APIs, modules, or tests.
moonbit-agent-guide
by moonbitlangGuide for writing, refactoring, and testing MoonBit projects. Use when working in MoonBit modules or packages, organizing MoonBit files, using moon tooling (build/check/run/test/doc/ide etc.), or following MoonBit-specific layout, documentation, and testing conventions.
moonbit-c-binding
by moonbitlangGuide for writing MoonBit bindings to C libraries using native FFI. Use when adding extern "c" declarations, writing C stubs with moonbit.h, configuring native-stub and link.native in moon.pkg, choosing
moonbit-extract-spec-test
by moonbitlangExtract formal spec and comprehensive test suites from existing MoonBit implementations. Use when asked to "extract spec from implementation", "generate tests from code", or "create spec-driven tests for existing package". Analyzes existing code to produce spec.mbt with `declare` keyword stubs and organized test files (valid/invalid).
moonbit-agent-guide
by moonbitlangGuide for writing, refactoring, and testing MoonBit projects. Use when working in MoonBit modules or packages, organizing MoonBit files, using moon tooling (build/check/run/test/doc/ide etc.), or following MoonBit-specific layout, documentation, and testing conventions.
moonbit-orientation
by moonbitlangUse this skill when the user needs help solving MoonBit language, code, compiler diagnostic, package, toolchain, backend, FFI, test, or "does MoonBit have X?" questions. Use it even when MoonBit is only implied by .mbt files, moon.mod.json, moon.pkg.json, moon commands, wasm/js/native targets, or mooncakes packages. This skill helps choose the right MoonBit source of truth, discover APIs with moon ide, avoid stale assumptions, and validate fixes.
make-moonbit-c-bindings
by moonbitlangGuides agents through complete, maintainable MoonBit bindings for C/C++ libraries, from upstream source survey through vendoring, safe API design, documentation tests, and ASan validation. Use when creating or hardening MoonBit native FFI bindings, wrapping C APIs, vendoring C sources into native-stub, or turning a C library into a MoonBit package.
blog-adding
by moonbitlangAdd a new blog post to this Docusaurus site from a staging folder.
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.