381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Showing 12 of 32 skills
planetis-m

nimonyplugins

by planetis-m
star 43

Write and debug Nimony plugins for compile-time code generation and DSL rewrites, including plugin-backed templates, `NifCursor` traversal, `NifBuilder` construction, subtree reuse, and source-level plugin errors. Use when replacing Nim macros with Nimony plugins or building a compile-time rewrite in the Nimony plugin system.

navigation main article SKILL.md
schedule Updated 2 months ago
planetis-m

nim-debugging

by planetis-m
star 43

Debug Nim programs using echo-based inspection, stack traces, compiler expansion flags, and runtime memory sanitizers. Use when diagnosing runtime crashes, unexpected behavior, memory errors, macro output, or ARC/ownership issues in Nim code.

navigation main article SKILL.md
schedule Updated 2 months ago
planetis-m

nim-error-handling

by planetis-m
star 43

Design clear Nim error-handling flows; when to raise exceptions vs return `Option`/`bool`, how to define `raises` contracts, and where to translate or record failures. Use when reviewing failure behavior, parse errors, exception boundaries, or batch processing that needs per-item error reporting.

navigation main article SKILL.md
schedule Updated 1 month ago
planetis-m

nim-fuzzing

by planetis-m
star 43

Set up and run libFuzzer-based fuzz targets for Nim code, including harness wiring, compilation flags, corpus management, structure-aware mutators, and crash triage. Use when adding fuzzing to a Nim project, building a fuzz harness for a parser/protocol/format handler, or reproducing and minimizing a fuzzer-found crash.

navigation main article SKILL.md
schedule Updated 1 month ago
planetis-m

nim-ownership-hooks

by planetis-m
star 43

Implement and review Nim ARC/ORC ownership hooks for types that manually manage resources, including move-only, deep-copy, refcounted, and copy-on-write patterns. Use when a Nim type owns pointers, buffers, file descriptors, or custom heap memory and you need correct `=destroy`, `=copy`, `=dup`, `=sink`, or move semantics.

navigation main article SKILL.md
schedule Updated 2 months ago
planetis-m

nim-style-guide

by planetis-m
star 43

Write clear, consistent Nim code in a simple stdlib-aligned style, covering imports, naming, proc vs func vs template choices, local variables, constructors, formatting, and control flow. Use when writing new Nim code or reviewing a Nim module for readability, consistency, and low-noise style decisions.

navigation main article SKILL.md
schedule Updated 2 months ago
planetis-m

nim-testing

by planetis-m
star 43

Write and run deterministic Nim tests, including isolated test files, expected-exception checks, multi-configuration builds, and sanitizer integration. Use when setting up a Nim test suite, testing failure behavior, running tests across debug/release/danger modes, or adding AddressSanitizer support.

navigation main article SKILL.md
schedule Updated 1 month ago
planetis-m

nim-c-wrappers

by planetis-m
star 5

Turn low-level C FFI bindings into an idiomatic Nim API, with safer types, cleaner module boundaries, resource management, callbacks, enums, bitflags, and exception-based error handling. Use when building a high-level Nim wrapper around a C library or improving the usability of existing raw bindings.

navigation main article SKILL.md
schedule Updated 2 months ago
planetis-m

nim-code-organization

by planetis-m
star 5

Organize Nim code into clear modules and multi-step workflows, with explicit state, top-level helper procs, narrow exports, and easy-to-follow orchestration. Use when refactoring a large Nim file, splitting logic across modules, designing parser-style stateful code, or cleaning up nested helpers and hidden mutable state.

navigation main article SKILL.md
schedule Updated 2 months ago
planetis-m

nim-debugging

by planetis-m
star 5

Debug Nim programs using echo-based inspection, stack traces, compiler expansion flags, and runtime memory sanitizers. Use when diagnosing runtime crashes, unexpected behavior, memory errors, macro output, or ARC/ownership issues in Nim code.

navigation main article SKILL.md
schedule Updated 10 days ago
planetis-m

nim-defect-analysis

by planetis-m
star 5

Find and classify reliability defects in Nim code using deterministic triage, bounded confirmation, root-cause tracing, and evidence-backed reports. Use when reviewing Nim source for robustness issues in parsers, I/O handlers, FFI boundaries, async code, or ownership hooks, diagnosing unhandled defect paths, or reducing false positives in code-quality findings.

navigation main article SKILL.md
schedule Updated 1 month ago
planetis-m

nim-doc-comments

by planetis-m
star 5

Document exported Nim modules and APIs with doc comments that `nim doc` actually picks up, including module docs, proc and type docs, field docs, and runnable examples. Use when writing documentation for a Nim library or fixing docs that are missing, attached to the wrong symbol, or rendering incorrectly.

navigation main article SKILL.md
schedule Updated 2 months ago
Page 1 of 3

Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.