Startidy

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AI-powered CLI tool to automatically organize your GitHub Stars into Lists.

Features

• Automatic Category Planning: Gemini AI analyzes your starred repositories and creates 32 optimal categories
• Smart Classification: Analyzes each repository's title, description, and README to place them in appropriate categories
• Hierarchical Naming: Uses Major: Minor format like Lang: Python, AI: LLM & Chatbot (20 char limit)
• Step-by-Step or Full Automation: Run individual steps or execute the entire workflow at once
• Batch Processing: Parallel processing of 20 repositories at a time for faster classification

Category Examples

Lang: Python       Lang: JS & TS      Lang: Go           Lang: Rust
Lang: Java         Lang: C & C++      Lang: ETC

AI: LLM & Chatbot  AI: Agent          AI: Image & Video  AI: RAG & Data
AI: Voice & Audio  AI: ETC

Web: Frontend      Web: Backend       Web: Crawler       Web: Mobile App
Web: ETC

Infra: Docker      Infra: Security    Infra: DB          Infra: Data & ML
Infra: ETC

Type: Self-Hosted  Type: App & Tool   Type: Starter      Type: Resource
Type: ETC

Installation

Global Install via npm (Recommended)

npm install -g startidy

After installation, you can use the startidy command directly:

startidy run

From Source

# Clone the repository
git clone https://github.com/hellosunghyun/startidy.git
cd startidy

# Install dependencies
npm install

# Build
npm run build

# Link globally
npm link

Configuration

You can configure Startidy in three ways:

Option 1: CLI Arguments (Recommended for one-time use)

startidy --token ghp_xxx --username your-name --gemini-key AIza_xxx run

Option 2: Environment Variables

# Linux/macOS
export GITHUB_TOKEN=ghp_xxxxxxxxxxxxxxxxxxxx
export GITHUB_USERNAME=your-username
export GEMINI_API_KEY=AIzaxxxxxxxxxxxxxxxxxxxxxxxx

# Windows (PowerShell)
$env:GITHUB_TOKEN="ghp_xxxxxxxxxxxxxxxxxxxx"
$env:GITHUB_USERNAME="your-username"
$env:GEMINI_API_KEY="AIzaxxxxxxxxxxxxxxxxxxxxxxxx"

# Windows (CMD)
set GITHUB_TOKEN=ghp_xxxxxxxxxxxxxxxxxxxx
set GITHUB_USERNAME=your-username
set GEMINI_API_KEY=AIzaxxxxxxxxxxxxxxxxxxxxxxxx

# Then run
startidy run

Option 3: .env File (Recommended for repeated use)

Create a .env file in your current directory:

GITHUB_TOKEN=ghp_xxxxxxxxxxxxxxxxxxxx
GITHUB_USERNAME=your-username
GEMINI_API_KEY=AIzaxxxxxxxxxxxxxxxxxxxxxxxx

Global CLI Options

| Option | Description | |--------|-------------| | --token <token> | GitHub Personal Access Token | | --username <username> | GitHub Username | | --gemini-key <key> | Google Gemini API Key | | --max-categories <n> | Maximum categories (default: 32) | | --batch-size <n> | Batch size for classification (default: 20) | | --private | Create private Lists | | --debug | Enable debug mode |

Getting a GitHub Token

1. Go to GitHub Settings > Developer settings > Personal access tokens
2. Click "Generate new token (classic)"
3. Select scopes: repo, read:user
4. Generate and copy the token

Getting a Gemini API Key

1. Go to Google AI Studio
2. Click "Create API Key"
3. Copy the API key

Usage

Full Automation (run command)

# Run the full workflow (plan → delete → create → classify)
startidy run

# With inline credentials
startidy --token ghp_xxx --username myname --gemini-key AIza_xxx run

# Process only newly starred repositories (keep existing Lists)
startidy run --only-new

# Simulation mode (preview categories only)
startidy run --dry-run

Step-by-Step Execution

1. Plan Categories (plan)

# Analyze Stars and plan categories (saved to file)
startidy plan

# View saved plan
startidy plan --show

# Delete saved plan
startidy plan --delete

2. Manage Lists (lists)

# View all Lists
startidy lists

# Create a new List
startidy lists --create "Lang: Python" -d "Python projects"

# Delete a specific List
startidy lists --delete "Lang: Python"

# Delete all Lists
startidy lists --delete-all

3. Create Lists (create-lists)

# Create Lists from planned categories
startidy create-lists

# Create Lists even if some already exist
startidy create-lists --force

4. Classify Stars (classify)

# Classify Stars into Lists
startidy classify

# Process only unclassified Stars
startidy classify --only-new

# Use existing Lists as categories (no plan file needed)
startidy classify --use-existing

# Classify new Stars using existing Lists
startidy classify --use-existing --only-new

# Reset: Remove all Stars from Lists
startidy classify --reset

Command Options Summary

| Command | Option | Description | |---------|--------|-------------| | run | (none) | Full automation | | run | --only-new | Process new Stars only | | run | --dry-run | Simulation mode | | plan | (none) | Plan categories | | plan | --show | View saved plan | | plan | --delete | Delete saved plan | | lists | (none) | View all Lists | | lists | --create <name> | Create new List | | lists | --delete <name> | Delete specific List | | lists | --delete-all | Delete all Lists | | lists | -d, --description | List description (with --create) | | create-lists | (none) | Create Lists from plan | | create-lists | --force | Create even if Lists exist | | classify | (none) | Classify Stars | | classify | --only-new | Process unclassified only | | classify | --use-existing | Use existing Lists as categories | | classify | --reset | Remove all Stars from Lists |

Manual Workflow Example

# 1. Plan categories
startidy plan

# 2. Review the plan
startidy plan --show

# 3. Delete existing Lists (if needed)
startidy lists --delete-all

# 4. Create Lists
startidy create-lists

# 5. Classify Stars
startidy classify

Execution Example

🚀 Starting GitHub Stars auto-organization.

✔ Fetched 523 starred repositories.
✔ 32 categories have been planned.

? Delete existing 32 Lists? Yes
✔ 32 Lists deleted
✔ 32 Lists created

📂 Classifying 523 repositories in batches of 20...

── Batch 1/27 (1-20) ──
✔ README fetched
✔ Classification complete
  ✅ facebook/react → Web: Frontend
  ✅ tensorflow/tensorflow → AI: Data & ML
  ...

📊 Results:
  ✅ Success: 520
  ❌ Failed: 3

✅ Done! Stars have been organized into Lists.

Project Structure

startidy/
├── package.json
├── tsconfig.json
├── .env.example
├── README.md
└── src/
    ├── index.ts              # CLI entry point
    ├── types.ts              # Type definitions
    ├── api/
    │   ├── index.ts          # API exports
    │   ├── client.ts         # GitHub API client
    │   ├── types.ts          # API types
    │   ├── lists.ts          # Lists CRUD
    │   ├── repos.ts          # Repository queries
    │   └── readme.ts         # README fetching
    ├── commands/
    │   ├── lists.ts          # lists command
    │   ├── plan.ts           # plan command
    │   ├── create-lists.ts   # create-lists command
    │   ├── classify.ts       # classify command
    │   └── run.ts            # run command (full automation)
    ├── services/
    │   ├── index.ts          # Services exports
    │   ├── gemini.ts         # Gemini AI service
    │   └── classifier.ts     # Classification service
    ├── prompts/
    │   ├── category-planner.ts
    │   └── classifier.ts
    └── utils/
        ├── config.ts         # Environment config
        ├── rate-limiter.ts   # Rate limiting
        └── plan-storage.ts   # Plan save/load

Environment Variables Reference

All available environment variables:

# Required
GITHUB_TOKEN=ghp_xxxxxxxxxxxx        # GitHub Personal Access Token
GITHUB_USERNAME=your-username         # Your GitHub username
GEMINI_API_KEY=AIzaxxxxxxxxxx         # Google Gemini API Key

# Category Settings
MAX_CATEGORIES=32                     # Maximum categories (GitHub limit: 32)
MAX_CATEGORIES_PER_REPO=3             # Max categories per repo
MIN_CATEGORIES_PER_REPO=1             # Min categories per repo

# Batch Processing
CLASSIFY_BATCH_SIZE=20                # Repos per batch for classification
BATCH_DELAY=2000                      # Delay between batches (ms)

# List Settings
LIST_IS_PRIVATE=false                 # Create private Lists

# Gemini Settings
GEMINI_MODEL=gemini-2.5-flash         # Model to use
GEMINI_RPM=15                         # Requests per minute (Free tier)

# Debug
DEBUG=false                           # Enable debug output
LOG_API_RESPONSES=false               # Log raw API responses

Tech Stack

• Runtime: Node.js / Bun
• Language: TypeScript
• AI: Google Gemini (gemini-2.5-flash)
• CLI: Commander.js, @inquirer/prompts, ora

Limitations

• GitHub Lists are limited to 32 maximum
• Each List name has a 20 character limit
• Gemini API Free tier: 15 requests per minute

License

MIT