docs-from-code

$npx mdskill add Varnan-Tech/opendirectory/docs-from-code

Generates accurate README and API docs by analyzing your codebase.

  • Solves missing or outdated documentation in any codebase.
  • Uses graphify to build a knowledge graph from AST and semantics.
  • Reads functions, routes, types, and architecture to ground docs.
  • Outputs README.md and API reference files directly into the project.

SKILL.md

.github/skills/docs-from-codeView on GitHub ↗
---
name: docs-from-code
description: Generates and updates README.md and API reference docs by reading your codebase's functions, routes, types, schemas, and architecture. Uses graphify to build a knowledge graph first, then writes accurate docs from it. Use when asked to write docs, generate a README, document an API, update stale docs, create an API reference from code, add an architecture section, or document a project in any language. Trigger when a user says their docs are missing, outdated, or wants to document their codebase without writing it manually.
compatibility: [claude-code, gemini-cli, github-copilot]
author: OpenDirectory
version: 1.0.0
---

# 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?"

---

## 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.

```bash
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)

```bash
# 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.

---

### 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:

```bash
# 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.

---

### 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.

---

### 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?

---

### 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:
```bash
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?

---

## 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


More from Varnan-Tech/opendirectory

SkillDescription
app-store-review-arbitrageFetches low-star App Store and Google Play reviews, clusters them into broken-promise patterns, and generates a ranked copy brief with positioning opportunities.
blog-cover-generatorUse when the user asks to generate a blog cover image, thumbnail, or article header. Automatically uses modern typography, brand logos, and Google Search grounding to create beautiful 16:9 images with Gemini 3.1 Flash Image Preview.
brand-alchemyWorld-class brand strategist and naming expert. Uses an interrogation-led discovery phase to extract your brand's DNA, then applies scientific naming frameworks (Phonosemantics) and automated multi-TLD domain checking.
claude-md-generatorUse when the user asks to generate or update a project's CLAUDE or AGENTS context file from a codebase scan. Writes a focused file under 100 lines containing only the non-obvious build commands, conventions, and gotchas Claude Code needs.
cold-email-verifierUse when the user wants to verify cold emails, enrich a lead list, or autonomously guess email addresses from a CSV using ValidEmail.co or the open-source Reacher engine.
company-radarCompetitive intelligence orchestrator tracking companies across 8+ platforms (GitHub, Twitter, Reddit, HN, PH, YC Jobs) with heat scores and AI briefings.
competitor-pr-finderGive it your product URL or description. It finds your top 5 competitors, runs three-track PR research across all of them (editorial, podcasts, communities), identifies which channels appear most frequently, looks up the journalist or host for each, and returns a tiered outreach list with story angles and ready-to-send cold pitch drafts tailored to your product. Use when asked to find PR opportunities, discover where competitors got featured, build a media outreach list, find which journalists cover my space, or get pitch templates for press coverage.
cook-the-blogGenerate high-converting, deep-dive growth case studies in MDX format. Use this skill when asked to write a case study or blog post about a company's growth, tech stack, or product-led strategy. It handles the full pipeline (researching the company via Tavily, generating a 16:9 cover image, quality checking the draft, uploading assets to cloud storage, and pushing directly to the target repository).
dependency-update-botScans your project for outdated npm, pip, Cargo, Go, or Ruby packages. Runs a CVE security audit. Fetches changelogs, summarizes breaking changes with Gemini, and opens one PR per risk group (patch, minor, major). Includes Diagnosis Mode for install conflicts. Use when asked to update dependencies, check for outdated packages, open dependency PRs, scan for package updates, audit for CVEs, or flag breaking changes in upgrades. Trigger when a user says "check for outdated packages", "update my dependencies", "open PRs for dependency updates", "scan for CVEs", or "which packages need upgrading".
domain-expired-opportunity-finderEvaluates expired domain candidates against a target niche, scores them by topical relevance, historical activity level, and history cleanliness, then outputs a ranked shortlist with explainable reasoning and risk flags.