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 37 skills
juicesharp

changelog

by juicesharp
star 397

Regenerate the [Unreleased] section of every affected CHANGELOG.md in Keep a Changelog style. Reads commits since the last release tag plus any uncommitted or staged changes, classifies them by Conventional Commit prefix, and rewrites each [Unreleased] block. Works in single-package repos and monorepos (one CHANGELOG.md per package). Use when preparing a release or drafting changelog entries. Idempotent — safe to re-run as work lands.

navigation main article SKILL.md
schedule Updated 20 days ago
juicesharp

annotate-guidance

by juicesharp
star 397

Generate architecture.md guidance files under .rpiv/guidance/ that document a project's architecture and patterns for AI assistants, written to a shadow tree alongside the source. Use when the user wants to onboard Claude, Cursor, or an AI agent to a codebase via the guidance system, document architecture, or asks to "annotate guidance". Prefer this over annotate-inline when the project uses the .rpiv/guidance/ shadow tree instead of inline CLAUDE.md files.

navigation main article SKILL.md
schedule Updated 20 days ago
juicesharp

annotate-inline

by juicesharp
star 397

Generate CLAUDE.md files placed inline next to source code across a project, documenting architecture and patterns for AI assistants. Use when the user wants to onboard Claude to a codebase via inline CLAUDE.md files, generate per-directory guidance, document architecture in-place, or asks to "annotate inline". Prefer this over annotate-guidance when CLAUDE.md should live alongside the code rather than in a shadow tree.

navigation main article SKILL.md
schedule Updated 20 days ago
juicesharp

architecture-review

by juicesharp
star 397

Conduct a top-down, layer-by-layer architecture review of a software module by reading every file in scope, running a uniform 10-dimension checklist per layer, and triaging each candidate finding through a structured developer checkpoint. Produces a phased polish plan in .rpiv/artifacts/architecture-reviews/ that blueprint can consume per phase. Language-agnostic — works on TypeScript, Java, .NET, Rust, Python, Go, or any other typed module. Use before a 1.0 release, after a major refactor, or when a module has grown enough to warrant a structural audit.

navigation main article SKILL.md
schedule Updated 20 days ago
juicesharp

blueprint

by juicesharp
star 397

Plan complex features by decomposing them into vertical slices (one slice equals one phase) with developer micro-checkpoints between phases, producing an implement-ready phased plan in .rpiv/artifacts/plans/. Use for complex multi-component features touching 6+ files across multiple layers when iterative review between slices is valuable. Optionally consumes a research/solutions artifact; can also run standalone with a free-text feature description for small tasks. Prefer blueprint over plan when mid-flight micro-checkpoints matter, and prefer plan when a straightforward phased breakdown is enough.

navigation main article SKILL.md
schedule Updated 20 days ago
juicesharp

code-review

by juicesharp
star 397

Conduct comprehensive code reviews of pending changes, a branch, or a PR using parallel specialist agents that audit the diff, compare against peer code, and verify claims. Use when the user asks to 'review this', wants pending changes, a PR, a branch, or a diff reviewed, or asks for a code review. Produces review documents in .rpiv/artifacts/reviews/. Internal mechanics like row-only agent contracts and Gap-Finder set arithmetic are documented in the skill body.

navigation main article SKILL.md
schedule Updated 21 days ago
juicesharp

commit

by juicesharp
star 397

Create structured git commits by analyzing staged and unstaged changes and grouping them logically into one or more commits with clear, descriptive messages. Use when the user asks to commit, says "commit this" or "commit my changes", wants help writing a commit message, or has finished a chunk of work that needs committing.

navigation main article SKILL.md
schedule Updated 21 days ago
juicesharp

create-handoff

by juicesharp
star 397

Create a context-preserving handoff document for session transitions, compacting the current task, decisions made, in-flight changes, and open questions into a single concise file so a fresh session can pick up where this one left off. Use when the user invokes /create-handoff, says context is getting large, asks to wrap up the session, or wants to hand off work to another session.

navigation main article SKILL.md
schedule Updated 20 days ago
juicesharp

design

by juicesharp
star 397

Design complex features by decomposing them into vertical slices, generating code slice-by-slice with per-slice verifier dispatch and post-finalization independent code review, and producing a design artifact (architecture decisions, slice breakdown, file map) in .rpiv/artifacts/designs/. The design feeds the plan or blueprint skill. Use for complex multi-component features touching 6+ files across multiple layers, when the user wants a feature designed before implementation. Requires a research artifact or a solutions artifact (from explore). Prefer design over plan or blueprint when the focus is architecture and decomposition rather than phased execution steps.

navigation main article SKILL.md
schedule Updated 20 days ago
juicesharp

discover

by juicesharp
star 397

Interview the developer one question at a time to extract feature intent and requirements, then synthesize into a Feature Requirements Document at .rpiv/artifacts/discover/. The first question is intent-only and runs before any codebase probe; subsequent questions ground in evidence the probe surfaces. Use as the canonical entry point of the pipeline before research, or to stress-test a feature idea before codebase discovery. The FRD's Decisions block is consumed by `research` and propagates through Developer Context into `design`.

navigation main article SKILL.md
schedule Updated 21 days ago
juicesharp

explore

by juicesharp
star 397

Analyze solution options for a feature or change, comparing approaches with pros, cons, trade-offs, and a recommended path. Use when the user is weighing approaches, asks "what are the options" or "how should we approach X", wants approaches compared, says "explore solutions", or faces a decision with multiple valid implementations. Produces solutions documents in .rpiv/artifacts/solutions/, which can feed the design skill.

navigation main article SKILL.md
schedule Updated 21 days ago
juicesharp

frontend-design

by juicesharp
star 397

Inject tailored visual design guidance for frontend work. Scope: web frontends (HTML/CSS/JS, React, Vue, Svelte, Astro, etc.). Aesthetic principles generalize to native/TUI but examples assume web. Use when the user asks to build a page, full layout, or new application, or explicitly wants design direction. SKIP for single-component requests in codebases with an established style system. The skill auto-adapts: empty scan → 2-question micro-interview; established system → scan-only injection; otherwise full 7-dimension checkpoint with skip logic.

navigation main article SKILL.md
schedule Updated 20 days 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.