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Codebase Index

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cris-achiardi
codebase-index

Automatically generate relationship and dependency maps for any component-based codebase (React, Vue, Svelte, Astro, Next.js, Angular, Solid). Auto-detects framework. Supports TOON format for 30-60% token savings. Use when indexing codebases, mapping component relationships, documenting dependencies, or understanding unfamiliar projects. Generates JSON/TOON files mapping component usage, imports, npm dependencies, utilities, CSS, and data queries.

Overview

Publishercris-achiardi
Repositoryclaude-skills
Skill namecodebase-index
Stars
72
Forks
8
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cris-achiardi on GitHub. Read the source before you install it.

Installation

Install the Codebase Index AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cris-achiardi/claude-skills/tree/main/.claude/skills/codebase-index
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cris-achiardi/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/.claude/skills/codebase-index .claude/skills/codebase-index
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codebase Index in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Codebase Index on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Codebase Index is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Codebase Index

Generate comprehensive, framework-agnostic relationship and dependency maps for component-based codebases with optional TOON format for token-efficient AI consumption.

What This Skill Does

Scans any component-based codebase to automatically generate relationship maps:

  • Component relationships: Import graph (usedBy/uses)
  • NPM dependencies: Package usage tracking
  • Utility functions: Shared code in lib/utils
  • Data queries: CMS and API patterns
  • CSS structure: Classes and design tokens

Framework Support: React, Vue, Svelte, Astro, Next.js, Angular, Solid (auto-detected)

Output Formats:

  • TOON (default): Token-efficient format, 30-60% smaller than JSON
  • JSON: Standard format for tooling compatibility

Quick Start

bash
# Auto-detect framework, output TOON
python scripts/index_codebase.py /path/to/project

# Specify framework and format
python scripts/index_codebase.py /path/to/project --framework react --format json

# Use tab delimiters (even more token-efficient)
python scripts/index_codebase.py /path/to/project --delimiter "\t"

Framework Auto-Detection

The skill automatically detects your framework from package.json:

FrameworkDetectionExtensions
Astroastro dependency.astro
Reactreact dependency.jsx, .tsx
Next.jsnext dependency.jsx, .tsx
Vuevue dependency.vue
Sveltesvelte dependency.svelte
Angular@angular/core.ts
Solidsolid-js.jsx, .tsx

Override auto-detection:

bash
python scripts/index_codebase.py /path/to/project --framework vue

Output Formats

TOON Format (Recommended)

Why TOON?

  • 30-60% fewer tokens than JSON
  • Better LLM comprehension (70% vs 65% accuracy)
  • Self-documenting structure with explicit lengths
  • Perfect for uniform data (our use case!)

Example TOON output:

toon
components[3]{name,path,type,framework,uses,usedBy}:
Button,src/components/atoms/Button.tsx,atom,react,[0]:,[2]: Card,Page
Card,src/components/molecules/Card.tsx,molecule,react,[1]: Button,[1]: Page
Page,src/pages/index.tsx,page,react,[2]: Button,Card,[0]:

statistics:
  totalComponents: 3
  atoms: 1
  molecules: 1
  pages: 1

vs JSON (same data):

json
{
  "components": {
    "Button": {
      "path": "src/components/atoms/Button.tsx",
      "type": "atom",
      "framework": "react",
      "uses": [],
      "usedBy": ["Card", "Page"]
    },
    ...
  }
}

Format Comparison

AspectTOONJSON
Token count40-70% smallerBaseline
LLM accuracy70.1%65.4%
Human readable✅✅
Tool compatible⚠️ (new format)✅
Best forAI consumptionTooling

Recommendation: Use TOON for AI context, JSON for tooling integration.

Output Structure

All files generated in src/.ai/ (or first source directory found):

src/.ai/
├── index.toon                      # Entry point
└── relationships/
    ├── component-usage.toon        # Component graph
    ├── dependencies.toon           # NPM + utilities + CSS
    └── data-flow.toon              # Data queries

Component Type Detection

Auto-detects component types from directory structure:

PatternType
/atoms/atom
/molecules/molecule
/organisms/organism
/templates/template
/ui/ui
/layouts/layout
/pages/, /views/, /routes/page
/hooks/hook
/contexts/context
Othercomponent

Advanced Options

Alternative Delimiters

For tabular data, choose delimiter for optimal tokenization:

bash
# Comma (default) - most readable
python scripts/index_codebase.py /path/to/project --delimiter ","

# Tab - often fewer tokens
python scripts/index_codebase.py /path/to/project --delimiter "\t"

# Pipe - middle ground
python scripts/index_codebase.py /path/to/project --delimiter "|"

Tab output example:

toon
components[2	]{name	path	type}:
Button	src/Button.tsx	atom
Card	src/Card.tsx	molecule

Dual Output

Generate both formats for different use cases:

bash
# Generate TOON for AI
python scripts/index_codebase.py /path/to/project --format toon

# Generate JSON for tools
python scripts/index_codebase.py /path/to/project --format json

Reading Index Files

For AI Assistants

After indexing, read files to understand codebase:

  1. Start with index.toon/json - Overview and metadata
  2. Component relationships - relationships/component-usage.toon
  3. Dependencies - relationships/dependencies.toon
  4. Data flow - relationships/data-flow.toon

TOON Syntax Quick Reference

toon
# Primitive field
name: Button

# Nested object
user:
  id: 123
  name: Ada

# Primitive array
tags[3]: react,typescript,ui

# Tabular array (uniform objects)
items[2]{id,name,price}:
1,Widget,9.99
2,Gadget,14.50

# List array (mixed)
items[3]:
- 1
- name: Item
- [2]: sub,array

Framework-Specific Features

Astro

  • Detects .astro components
  • Scans for Sanity CMS client.fetch() queries
  • Maps layouts and pages

React/Next.js

  • Supports .jsx, .tsx
  • Detects hooks, contexts
  • Maps fetch() and axios patterns

Vue

  • Scans .vue components
  • Maps views and composables

Svelte

  • Detects .svelte components
  • Scans routes and stores

Angular

  • Scans .component.ts files
  • Detects http.get() patterns

Solid

  • Supports .jsx, .tsx
  • Maps Solid-specific patterns

Project Structure Requirements

Flexible directory discovery:

  • Checks framework-specific directories (e.g., app/ for Next.js)
  • Falls back to src/ if present
  • Works with various project structures

Typical structures supported:

# React/Next.js
app/
components/
pages/

# Vue
src/
  components/
  views/

# Astro
src/
  components/
  layouts/
  pages/

Integration with Metadata

Complementary Design

Component Metadata (manual .metadata.json):

  • Purpose: "How to USE this component"
  • Contains: Props, variants, examples, guidelines
  • Created: Manually

Codebase Index (this skill):

  • Purpose: "WHERE is this used"
  • Contains: Relationships, dependencies, usage
  • Created: Auto-generated

No duplication - These systems complement each other.

Performance

  • Speed: 1-5 seconds for typical projects (50-200 components)
  • Output size:
    • TOON: 30-150KB (typical)
    • JSON: 50-250KB (typical)
  • Memory: Minimal (streaming processing)

Large projects (500+ components):

  • Consider directory-specific scans
  • Use tab delimiter for better tokenization
  • May take 10-30 seconds

Error Handling

The indexer is resilient:

  • Skips unparseable files (logs warning)
  • Handles missing directories gracefully
  • Continues on errors to maximize coverage
  • Reports errors in summary

Example Usage

First-time Index

bash
# Let it auto-detect everything
python scripts/index_codebase.py ~/projects/my-app

# Output:
# 🔍 Scanning react codebase...
# ✅ Generated component-usage.toon
# ✅ Generated dependencies.toon
# ✅ Generated data-flow.toon
# ✅ Generated index.toon
#
# Summary:
# 🎯 Framework: react
# 📝 Format: TOON
# ✅ 42 components indexed
# ✅ 156 relationships mapped
# ✅ 8 utilities tracked
# ✅ 3 queries documented
# 💡 TOON format: 30-60% more token-efficient than JSON

Re-index After Changes

bash
# Same command - overwrites previous index
python scripts/index_codebase.py ~/projects/my-app

Framework Override

bash
# For edge cases where auto-detect fails
python scripts/index_codebase.py ~/projects/my-app --framework vue

Troubleshooting

No components found?

  • Verify source directories exist
  • Check file extensions match framework
  • Look for node_modules in exclusions

Wrong framework detected?

  • Use --framework flag to override
  • Check package.json dependencies

Import relationships missing?

  • Verify import statements use standard ES6 format
  • Dynamic imports may not be detected
  • Check for path aliases (may need resolution)

TOON parse errors?

  • File is valid - TOON is for AI consumption
  • Use JSON format for tooling integration
  • Check TOON spec if implementing parser

Utilities not found?

  • Verify lib/, utils/, or helpers/ directories exist
  • Check for .ts files (not .d.ts)

Comparison with JSON

Example: Component Data

JSON (123 tokens):

json
{
  "Button": {
    "path": "src/components/Button.tsx",
    "type": "atom",
    "framework": "react",
    "uses": [],
    "usedBy": ["Card", "Header", "Footer"]
  }
}

TOON (52 tokens, 58% reduction):

toon
Button:
  path: src/components/Button.tsx
  type: atom
  framework: react
  uses[0]:
  usedBy[3]: Card,Header,Footer

Tabular TOON (even better for multiple components):

toon
components[3]{name,path,type,uses,usedBy}:
Button,src/components/Button.tsx,atom,[0]:,[3]: Card,Header,Footer
Card,src/components/Card.tsx,molecule,[1]: Button,[1]: Page
Footer,src/components/Footer.tsx,organism,[2]: Button,Icon,[0]:

CI/CD Integration

Git Hook Example

bash
# .husky/post-merge
#!/bin/sh
python scripts/index_codebase.py . --format toon
git add src/.ai/

GitHub Action

yaml
name: Update Codebase Index
on:
  push:
    branches: [main]
jobs:
  index:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Index codebase
        run: python scripts/index_codebase.py . --format toon
      - name: Commit changes
        run: |
          git add src/.ai/
          git commit -m "Update codebase index" || true
          git push

Future Enhancements

Potential additions:

  • Bundle size analysis
  • Circular dependency detection
  • Visual graph generation (mermaid)
  • More CMS platforms (Contentful, Strapi)
  • API route mapping
  • Environment variable tracking
  • Incremental updates

Credits

  • TOON Format: toon-format/toon
  • Token Efficiency: 30-60% reduction vs JSON
  • LLM Accuracy: 70.1% vs 65.4% (JSON baseline)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Codebase Index AI skill do?

Automatically generate relationship and dependency maps for any component-based codebase (React, Vue, Svelte, Astro, Next.js, Angular, Solid). Auto-detects framework. Supports TOON format for 30-60% token savings. Use when indexing codebases, mapping component relationships, documenting dependencies, or understanding unfamiliar projects. Generates JSON/TOON files mapping component usage, imports, npm dependencies, utilities, CSS, and data queries.

Why use Codebase Index on TypingMind?

Because you install it once and use it with any model. Codebase Index is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Codebase Index in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cris-achiardi/claude-skills/tree/main/.claude/skills/codebase-index. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Codebase Index?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Codebase Index?

As many as you like. As long as a model supports skills, you can use Codebase Index with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Codebase Index AI skill free?

Yes. It is published on GitHub by cris-achiardi under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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