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 25 skills
stencila

site-config-creation

by stencila
star 888

Create or update the [site] section of stencila.toml for published Stencila sites. Use when asked to configure site domain, title, author, logo, icons, labels, descriptions, socials, featured content, navigation, routes, access, layout presets and regions, glide, search, formats, reviews, uploads, remotes, actions, auto-index, or specimen. Covers reading existing TOML, generating valid configuration, editing while preserving comments and formatting, and snap-based visual verification.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

site-config-review

by stencila
star 888

Review a Stencila site configuration (stencila.toml) for correctness, completeness, best practices, and rendered appearance. Use when asked to review, audit, validate, check, or assess a site config, stencila.toml, site routes, redirects, site layout, layout presets, site nav, navigation, site access, access control, or site deployment readiness.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

figure-creation

by stencila
star 888

Create, edit, or plan figures in Stencila Markdown — simple image figures, executable code figures, multi-panel subfigure layouts with grid arrangements, and SVG annotation overlays using overlay components. Use when asked to add or revise a figure, chart, plot, caption, subfigure grid, panel layout, overlay annotation, callout, scale bar, arrow, region-of-interest highlight, or figure design plan for a Stencila document.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

software-plan-review

by stencila
star 888

Critically review a software delivery plan and suggest concrete improvements. Use when the user wants to review, critique, audit, evaluate, or strengthen a delivery plan, implementation plan, project plan, phased roadmap, or test plan. Reviews plans produced by the software-plan-creation skill, checking task breakdown quality, sequencing, dependencies, testing strategy, TDD slice design and sizing, design coverage, risks, and actionability. Produces a structured critique with prioritized recommendations.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

software-plan-creation

by stencila
star 888

Create delivery plans for software design specifications. Use when the user wants to plan implementation, break a design into delivery phases, create an implementation roadmap, produce a test plan, or structure a TDD approach for a feature. Reads the latest design spec by default and produces a phased plan covering implementation, testing, and documentation, with TDD slices sized to be logically coherent without degenerating into micro-slices.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

software-implementation

by stencila
star 888

Write the minimal production code needed to make failing TDD tests pass (Green phase of red-green-refactor). Use when failing tests exist and production code must be written or modified to satisfy them. Reads and interprets failing test output, discovers codebase conventions (module layout, naming, import patterns, error handling, coding style), writes focused production code that satisfies test expectations without over-engineering, integrates new code with existing modules, types, and APIs, handles revision feedback from failed test runs, and verifies implementation compiles or parses before handing off to test execution. Works with any language or framework.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

software-design-creation

by stencila
star 888

Create software design specifications for new features or standalone software. Use when the user needs help turning a brief idea into a concrete design spec by eliciting missing requirements, clarifying scope, defining users and constraints, outlining architecture and interfaces, identifying risks and assumptions, and writing acceptance criteria that developers can later use for planning and implementation.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

software-code-review

by stencila
star 888

Evaluate source code for correctness, quality, security, style conformance, and maintainability, producing a structured review report with findings and recommendations. Use when the user wants to review, critique, audit, evaluate, or inspect source code — checking for bugs, logic errors, unhandled error paths, security vulnerabilities, naming and readability issues, complexity, duplication, coupling, testability, and API design. Discovers codebase conventions independently and produces an actionable report with severity-graded findings grouped by category, prioritized recommendations, and open questions.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

software-code-refactoring

by stencila
star 888

Improve production code quality while preserving all existing test behavior. Commonly used for the Refactor phase of TDD red-green-refactor, but applicable to any codebase with tests. Use when production code works but needs cleanup — reducing duplication, improving naming, simplifying complexity, aligning with codebase style, extracting functions or types, or removing dead code. Discovers conventions, applies safe transformations, verifies compilation after each change, and produces a structured summary. Works with any language or framework.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

software-design-review

by stencila
star 888

Critically review a software design specification and suggest concrete improvements. Use when the user wants to review, critique, audit, evaluate, or strengthen a design spec, technical specification, feature design, architecture proposal, or implementation plan. Focus on clarity, completeness, correctness, feasibility, tradeoffs, risks, assumptions, and actionable recommendations that improve the design without turning the task into code generation or workflow design.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

software-test-review

by stencila
star 888

Evaluate the quality of TDD tests against slice acceptance criteria, codebase conventions, and Red-phase execution results, producing a structured review with Accept or Revise recommendations. Use when tests written during the Red phase of red-green-refactor need quality review — checking coverage of acceptance criteria, conformance with codebase test conventions, test quality (naming, assertions, isolation, readability, triviality), edge-case and error-path coverage, and whether Red-phase failures indicate correctly missing implementation. Flags trivial low-value tests that add more maintenance cost than testing value. Discovers codebase conventions independently and produces an actionable review report.

navigation main article SKILL.md
schedule Updated 2 months ago
stencila

software-test-execution

by stencila
star 888

Run scoped tests for a TDD slice, determine the appropriate test framework and command, and report structured pass/fail results. Use when tests need to be executed after writing, implementing, or refactoring code. Reads test metadata, discovers the test framework if needed, executes the scoped test command, parses output into structured results, and reports whether tests passed or failed. Handles compilation errors, missing dependencies, timeouts, and works with any language and test framework.

navigation main article SKILL.md
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.