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Wiki architect

Skill Pyfagorass/bookofspells/skills/microsoft/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 understand a project's architecture at a high level.From its SKILL.md

Install
npx -y skills add Pyfagorass/bookofspells --skill wiki-architect

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What its file declares

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The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.0 KB, 825 tokens by cl100k_base, as published. Nobody here has run it

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

Source Repository Resolution (MUST DO FIRST)

Before any analysis, you MUST determine the source repository context:

  1. Check for git remote: Run git remote get-url origin to detect if a remote exists
  2. Ask the user: "Is this a local-only repository, or do you have a source repository URL (e.g., GitHub, Azure DevOps)?"
    • Remote URL provided → store as REPO_URL, use linked citations: [file:line](REPO_URL/blob/BRANCH/file#Lline)
    • Local-only → use local citations: (file_path:line_number)
  3. Determine default branch: Run git rev-parse --abbrev-ref HEAD
  4. Do NOT proceed until source repo context is resolved

Procedure

  1. Resolve source repo (see above — MUST be first)
  2. Scan the repository file tree and README
  3. Detect project type, languages, frameworks, architectural patterns, key technologies
  4. Identify layers: presentation, business logic, data access, infrastructure
  5. Generate a hierarchical JSON catalogue with:
    • Onboarding: Contributor Guide, Staff Engineer Guide, Executive Guide, Product Manager Guide (in onboarding/ folder)
    • Getting Started: overview, setup, usage, quick reference
    • Deep Dive: architecture → subsystems → components → methods
  6. Cite real files in every section prompt using linked or local citation format

Onboarding Guide Architecture

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

  1. Contributor Guide — For new contributors (assumes Python/JS). 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
  2. Staff Engineer Guide — For staff/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, decision log, dependency rationale, "where to go deep" reading order
  3. Executive Guide — For VP/director-level leaders. NO code snippets. Includes:

    • Capability map, risk assessment, technology investment thesis
    • Cost/scaling model, dependency map, actionable recommendations
  4. Product Manager Guide — For PMs. ZERO engineering jargon. Includes:

    • User journey maps, feature capability map, known limitations
    • Data/privacy overview, configuration/feature flags, FAQ

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.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most architecture codebase skills give in 825 tokens

Counted across 811 of the 1,134 authors here whose files we hold, read 2026-08-07

  • Ask the user which candidate to explorein 45 of 811, across 15 files
  • Apply the deletion test to suspected shallow modulesin 43 of 811, across 15 files
  • Read any relevant architecture decision records firstin 31 of 811, across 8 files
  • Use exact glossary terms in every suggestionin 30 of 811, across 10 files
  • Accept dependencies instead of creating themin 24 of 811, across 5 files
  • Include before and after visualisations for each candidatein 24 of 811, across 5 files
  • Read the domain glossary before exploringin 24 of 811, across 6 files
  • Return results instead of producing side effectsin 23 of 811, across 4 files
  • Explore the codebase for shallow modules and frictionin 23 of 811, across 3 files
  • Introduce seams only where things varyin 22 of 811, across 3 files
  • Reduce the number of methodsin 21 of 811, across 2 files
  • Design deep modules with small interfacesin 21 of 811, across 3 files

Said here and by no other author read

  • resolve repository context first
  • ask user for repository URL
  • determine default branch
  • scan repository file tree and readme
  • detect project type and frameworks
  • generate hierarchical catalogue

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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