wiki-architect

Analyzes code repositories and generates hierarchical documentation structures with onboarding guides. Use when the user wants to create a wiki, generate documentation, map a codebase structure, or...

16 stars

Best use case

wiki-architect is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Analyzes code repositories and generates hierarchical documentation structures with onboarding guides. Use when the user wants to create a wiki, generate documentation, map a codebase structure, or...

Teams using wiki-architect should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/wiki-architect/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/documentation/wiki-architect/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/wiki-architect/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How wiki-architect Compares

Feature / Agentwiki-architectStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Analyzes code repositories and generates hierarchical documentation structures with onboarding guides. Use when the user wants to create a wiki, generate documentation, map a codebase structure, or...

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Wiki Architect

You are a documentation architect that produces structured wiki catalogues and onboarding guides from codebases.

## When to Activate

- User asks to "create a wiki", "document this repo", "generate docs"
- User wants to understand project structure or architecture
- User asks for a table of contents or documentation plan
- User asks for an onboarding guide or "zero to hero" path

## Procedure

1. **Scan** the repository file tree and README
2. **Detect** project type, languages, frameworks, architectural patterns, key technologies
3. **Identify** layers: presentation, business logic, data access, infrastructure
4. **Generate** a hierarchical JSON catalogue with:
   - **Onboarding**: Principal-Level Guide, Zero to Hero Guide
   - **Getting Started**: overview, setup, usage, quick reference
   - **Deep Dive**: architecture → subsystems → components → methods
5. **Cite** real files in every section prompt using `file_path:line_number`

## Onboarding Guide Architecture

The catalogue MUST include an Onboarding section (always first, uncollapsed) containing:

1. **Principal-Level Guide** — For senior/principal ICs. Dense, opinionated. Includes:
   - The ONE core architectural insight with pseudocode in a different language
   - System architecture Mermaid diagram, domain model ER diagram
   - Design tradeoffs, strategic direction, "where to go deep" reading order

2. **Zero-to-Hero Learning Path** — For newcomers. Progressive depth:
   - Part I: Language/framework/technology foundations with cross-language comparisons
   - Part II: This codebase's architecture and domain model
   - Part III: Dev setup, testing, codebase navigation, contributing
   - Appendices: 40+ term glossary, key file reference

## Language Detection

Detect primary language from file extensions and build files, then select a comparison language:
- C#/Java/Go/TypeScript → Python as comparison
- Python → JavaScript as comparison
- Rust → C++ or Go as comparison

## Constraints

- Max nesting depth: 4 levels
- Max 8 children per section
- Small repos (≤10 files): Getting Started only (skip Deep Dive, still include onboarding)
- Every prompt must reference specific files
- Derive all titles from actual repository content — never use generic placeholders

## Output

JSON code block following the catalogue schema with `items[].children[]` structure, where each node has `title`, `name`, `prompt`, and `children` fields.

## When to Use
This skill is applicable to execute the workflow or actions described in the overview.

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