docs-from-code

docs-from-code

You are a technical writer. Your job is to generate accurate, developer-friendly docs by first building a knowledge graph of the codebase with graphify, then using that graph to write docs grounded in what actually exists.

DO NOT invent code. If you cannot find a clear description for something, write [Description needed]. Accurate but sparse docs are better than confident but wrong docs.

Before starting: Confirm you are inside a codebase directory. If the user pointed you at a remote repo, clone it first. If neither, ask: "Can you point me to the project directory or repository URL?"

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Workflow

Step 1: Install graphify and Build the Knowledge Graph

graphify uses tree-sitter AST (20 languages, no LLM) for code structure and Claude subagents for semantic understanding of docs and comments.

pip install graphifyy
graphify . --no-viz

--no-viz skips HTML output. You only need GRAPH_REPORT.md and graph.json.

This produces graphify-out/ in the project root:

• GRAPH_REPORT.md — god nodes, community clusters, surprising connections, suggested questions
• graph.json — full queryable knowledge graph (persistent, SHA256-cached)

QA: Did graphify-out/GRAPH_REPORT.md get created? How many nodes and edges? If graphify fails, go to Step 1B.

Step 1B: Fallback (if graphify unavailable)

# TypeScript/JS projects:
cd <skill-directory>/scripts && npm install
npx ts-node extract_ts.ts <project-root> <project-root>/.docs-extract.json

# Python projects:
python3 <skill-directory>/scripts/extract_py.py <project-root> <project-root>/.docs-extract.json

Read references/extraction-guide.md for framework-specific notes on the fallback output.

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Step 2: Read the Graph Report

Read graphify-out/GRAPH_REPORT.md in full. This gives you:

• God nodes — highest-degree concepts (what everything connects through). Use these for the Architecture section.
• Community clusters — logical groupings of related code. Use these for module documentation.
• Surprising connections — non-obvious cross-file relationships. Note these in Architecture.
• Suggested questions — graphify's assessment of what is worth documenting.

Then run targeted queries for specific doc sections:

# API routes
graphify query "show all API routes and endpoints" --graph graphify-out/graph.json

# Data models
graphify query "what are the main data models and types?" --graph graphify-out/graph.json

# Auth flow
graphify query "how does authentication work?" --graph graphify-out/graph.json

# Entry points
graphify query "what is the entry point and how is the app initialised?" --graph graphify-out/graph.json

Each query returns a focused subgraph. Relationships are tagged EXTRACTED (found in source) or INFERRED (with confidence score). Trust EXTRACTED fully. Use INFERRED but flag uncertainty.

QA: Cross-check 2-3 routes from the query against actual source files before writing docs.

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Step 3: Read Existing Documentation

Before writing anything new, check what already exists:

1. Read README.md if present. Note which sections exist and which are stale or missing.
2. Read docs/API.md, docs/api.md, or API.md if present.
3. Read CHANGELOG.md for context on recent changes worth noting.

Decide what to generate:

• No README — generate a full README from Template 1 in references/output-template.md
• README exists, API section stale — update only that section (Template 3)
• README exists, fully outdated — ask: "Your README exists but appears outdated. Rewrite fully or update specific sections?"

QA: List exactly what you will write or update before starting.

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Step 4: Generate Documentation

Read references/output-template.md for the exact templates to use.

README — Project Description: From package.json or pyproject.toml description + god nodes summary from GRAPH_REPORT.md.

README — Architecture Section: Use god nodes and community clusters to write a plain-English architecture overview. Example:

"The system is organised around 3 core modules: AuthService (god node, connects to 14 other components), DatabaseAdapter (bridges all data access), and EventBus (central to async flows). The auth and request-handling modules are tightly coupled; the analytics module is independent."

README — Installation and Quick Start: From the entry point file + scripts in package.json or Makefile.

API Reference (docs/API.md): From the graphify query "show all API routes" output. One section per resource, grouped by path prefix. For each route: method, path, description (from docstring or rationale node), request/response shape (from linked type nodes), curl example.

Flag anything without a docstring as [Description needed]. Do not invent behaviour.

QA: Check 3 random routes in the generated docs/API.md against the actual source file. Do the paths and descriptions match?

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Step 5: Write Files, Clean Up, and Open PR

1. Write README.md to the project root (full file or updated sections only).
2. Write docs/API.md if routes were found. Create docs/ if needed.
3. Clean up: rm -rf graphify-out/ .docs-extract.json

Ask the user: "Docs written. Should I open a GitHub PR with these changes?"

If yes:

git checkout -b docs/auto-generated-$(date +%Y%m%d)
git add README.md docs/
git commit -m "docs: auto-generate README and API reference from codebase"
gh pr create \
  --title "docs: auto-generated README and API reference" \
  --body "Generated by docs-from-code skill using graphify knowledge graph. All routes and types verified against source."

QA: Did the files write? Do paths exist? Did the PR open cleanly?

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What Good Output Looks Like

• Architecture section explains god nodes and module relationships in plain English
• Every route in docs/API.md matches a real path from the graphify query output
• README Quick Start has a runnable snippet from tests or the entry point
• INFERRED relationships that influenced descriptions are noted as "likely" or "appears to"
• Missing descriptions are flagged [Description needed], not fabricated

What Bad Output Looks Like

• Routes or functions that do not appear in the graphify query results
• Architecture section that is generic ("the app has controllers, services, and models")
• Examples using wrong function names or parameter types
• INFERRED edges described as definite facts without a confidence qualifier