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 45 skills
nq-rdl

r-lib-mirai

by nq-rdl
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Help users write correct R code for async, parallel, and distributed computing using mirai. Use when users need to: run R code asynchronously or in parallel, write mirai code with correct dependency passing, set up local or remote parallel workers, convert code from future or parallel, use parallel map operations, integrate async tasks with Shiny or promises, or configure cluster/HPC computing.

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schedule Updated 2 months ago
nq-rdl

r-expert

by nq-rdl
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R language expert skill. Use when writing, reviewing, or debugging R code, or when the user asks for R best practices, idiomatic R, or guidance on the R ecosystem. Covers base R, tidyverse style, vectorization, pipe usage, error handling, and performance patterns. Complements r-lib (package dev) and shiny (web apps) — this skill focuses on the language itself.

navigation main article SKILL.md
schedule Updated 2 months ago
nq-rdl

r-lib-cli-app

by nq-rdl
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Build command-line apps in R using the Rapp package. Use when creating a CLI tool in R, adding argument parsing to an R script, turning an R script into a command-line app, shipping CLIs in an R package, or using Rapp (the alternative Rscript front-end). Also use for shebang scripts, exec/ directory in R packages, or subcommand-based R tools.

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schedule Updated 2 months ago
nq-rdl

r-lib-cli

by nq-rdl
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Comprehensive R package for command-line interface styling, semantic messaging, and user communication. Use this skill when working with R code that needs to: (1) Format console output with inline markup and colors, (2) Display errors, warnings, or messages with cli_abort/cli_warn/cli_inform, (3) Show progress indicators for long-running operations, (4) Create semantic CLI elements (headers, lists, alerts, code blocks), (5) Apply themes and customize output styling, (6) Handle pluralization in user-facing text, (7) Work with ANSI strings, hyperlinks, or custom containers. Also use when migrating from base R message/warning/stop, debugging cli code, or improving existing cli usage.

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schedule Updated 2 months ago
nq-rdl

r-lib-cran-extrachecks

by nq-rdl
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Prepare R packages for CRAN submission by checking for common ad-hoc requirements not caught by devtools::check(). Use when: (1) Preparing a package for first CRAN release, (2) Preparing a package update for CRAN resubmission, (3) Reviewing a package to ensure CRAN compliance, (4) Responding to CRAN reviewer feedback. Covers documentation requirements, DESCRIPTION field standards, URL validation, examples, and administrative requirements.

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schedule Updated 2 months ago
nq-rdl

r-lib-lifecycle

by nq-rdl
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Guidance for managing R package lifecycle according to tidyverse principles using the lifecycle package. Use when: (1) Setting up lifecycle infrastructure in a package, (2) Deprecating functions or arguments, (3) Renaming functions or arguments, (4) Superseding functions, (5) Marking functions as experimental, (6) Understanding lifecycle stages (stable, experimental, deprecated, superseded), or (7) Writing deprecation helpers for complex scenarios.

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schedule Updated 2 months ago
nq-rdl

r-lib-package-dev

by nq-rdl
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Orchestrates the full R package development lifecycle: project creation, directory structure, devtools workflow, documentation with roxygen2, code formatting with air, NEWS.md conventions, and CRAN readiness. Load this skill when the user is building, maintaining, or improving an R package. For deep-dive topics, this skill delegates to specialized siblings: r-lib-testing (testthat patterns), r-lib-cli (user-facing messages), r-lib-lifecycle (deprecation/versioning), and r-lib-cran-extrachecks (CRAN submission checklist).

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schedule Updated 2 months ago
nq-rdl

r-lib-testing

by nq-rdl
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Best practices for writing R package tests using testthat version 3+. Use when writing, organizing, or improving tests for R packages. Covers test structure, expectations, fixtures, snapshots, mocking, and modern testthat 3 patterns including self-sufficient tests, proper cleanup with withr, and snapshot testing.

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schedule Updated 2 months ago
nq-rdl

go-secure

by nq-rdl
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Secure Go error handling and information leakage prevention. Use whenever writing Go code that handles errors in APIs, services, or any code that crosses trust boundaries — HTTP handlers, gRPC services, CLI tools with user-facing output. Also trigger when reviewing Go error handling, implementing structured logging, or when the user mentions security, error sanitization, or preventing data leaks through error messages — even if they don't explicitly say "security". Covers domain error types, trust boundary translation, log redaction with slog, and safe API responses.

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schedule Updated 2 months ago
nq-rdl

report-skill-issue

by nq-rdl
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Report issues with skills to their upstream repository. Use when a skill produces errors, unexpected behavior, incorrect output, or fails silently. Also trigger when the user says things like "this skill is broken", "file a bug for this skill", "report this to the skill author", or when you notice a skill behaving incorrectly during normal use. Even if the user doesn't explicitly ask, offer to report the issue if you observe a clear skill defect.

navigation main article SKILL.md
schedule Updated 2 months ago
nq-rdl

rust-explain

by nq-rdl
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Teaching assistant for reading and navigating Rust code. Use when reading an unfamiliar Rust codebase, when Rust is pasted with a question, when the borrow checker or a rustc error is confusing, when a generated Rust kernel needs a correctness sanity-check, or when asked to explain a specific Rust construct (lifetimes, trait objects, Arc<Mutex<T>>, a macro, an iterator chain). Also triggers on "explain this Rust", "what does this Rust do", "why doesn't this compile", reading ownership/borrowing, FFI/unsafe blocks, or numeric crates (ndarray, nalgebra, faer, polars, rayon).

navigation main article SKILL.md
schedule Updated 24 days ago
nq-rdl

shiny-bslib-theming

by nq-rdl
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Advanced theming for Shiny apps using bslib and Bootstrap 5. Use when customizing app appearance with bs_theme(), Bootswatch themes, custom colors, typography, brand.yml integration, Bootstrap Sass variables, custom Sass/CSS rules, dark mode and color modes, dynamic theme switching, real-time theming, theme inspection, or making R plots match the app theme with thematic.

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schedule Updated 2 months ago
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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.