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@dev-angsu/cli

v1.0.7

Published

A powerful CLI tool designed to streamline developer workflows, featuring project scaffolding, AI-powered code reviews, and codebase context extraction for LLMs.

Readme

@dev-angsu/cli

A powerful CLI tool designed to streamline developer workflows, featuring project scaffolding, AI-powered code reviews, and codebase context extraction for LLMs.

Installation

npm install -g @dev-angsu/cli

Or run directly with npx:

npx @dev-angsu/cli <command>

Configuration

For AI features (Review), you need to set up your environment variables.

  1. Create a .env file in your project root (or set these in your CI environment):
    OPENAI_API_KEY=sk-...
    AI_BASE_URL=https://api.openai.com/v1/ # Optional, defaults to custom endpoint
    AI_MODEL=gpt-4 # Optional

Commands

1. Create Project

Scaffold a new project structure quickly.

Usage:

dev-angsu create [name] [options]
# Alias
dev-angsu c [name]

Options:

  • -t, --type <type>: Specify project type (Node.js, Python, Go).

Examples:

dev-angsu create my-app --type Node.js
dev-angsu c # Interactive mode

2. AI Code Review

Analyze your code changes using AI. Works locally on staged changes and in CI/CD pipelines.

Usage:

dev-angsu review
# Alias
dev-angsu r

Local Mode:

  • Reviews changes currently staged in git (git add .).
  • Prints feedback to the console.

CI/CD Mode (GitHub Actions):

  • Automatically detects if running in a PR or Push event.
  • Posts comments directly to the Pull Request or Commit.

GitHub Action Usage: Add this to your .github/workflows/review.yml:

steps:
  - uses: actions/checkout@v3
  - uses: dev-angsu/cli@main
    with:
      openai_api_key: ${{ secrets.OPENAI_API_KEY }}

3. Copy Code Context

Prepare your codebase for Large Language Models (LLMs). Scans the directory, respects .gitignore, filters out binary files/noise, and copies the context to your clipboard.

Usage:

dev-angsu copycode [options]
# Alias
dev-angsu cp

Options:

  • -o, --output <file>: Save the result to a file instead of the clipboard.
  • -d, --dry-run: Preview the file list and token estimation without copying.

Examples:

# Copy current directory context to clipboard
dev-angsu cp

# Save context to a markdown file
dev-angsu cp --output context.md

# Check what files will be included
dev-angsu cp --dry-run

Features

  • Smart Filtering: Automatically ignores node_modules, .git, lock files, and binary files.
  • Safety Checks: Warns before copying massive payloads (>500KB) to the clipboard.
  • Token Estimation: Provides a rough token count for LLM context windows.