I Built 15 CLI Tools in One Day (And You Can Too) How I went from zero to a complete developer toolkit using AI-assisted development The Challenge Last week, I set myself an ambitious goal: build 15 useful CLI tools in a single day. Not toy projects. Not "hello world" scripts. Real tools that solve real problems developers face every day. The result? 15 production-ready CLI tools, complete with documentation, installation scripts, and a unified distribution strategy. Here's how I did it — and how you can replicate this approach for your own projects. Why CLI Tools? Command-line tools are the unsung heroes of developer productivity. They're: Fast: No GUI overhead, instant execution Composable: Pipe them together for complex workflows Automatable: Perfect for CI/CD pipelines Universal: Work on any platform with a terminal But building 15 in one day? That sounds impossible. Unless you have a system. The System: AI-Assisted Rapid Development Phase 1: Problem Identification (30 minutes) I started by listing every repetitive task that annoys me as a developer: Setting up new projects (dependencies, configs, boilerplate) Writing README files (boring but necessary) Generating .gitignore files (always look up the same patterns) Creating Docker Compose configurations Writing GitHub Actions workflows Managing environment variables across projects Analyzing log files Testing APIs without opening Postman Generating commit messages Code review before committing Finding GitHub bounties to work on Formatting JSON Managing API keys securely Initializing projects with best practices Reviewing code with AI assistance Each of these became a tool. Phase 2: Architecture Decisions (15 minutes) To move fast, I needed consistency. Every tool follows the same pattern: #!/usr/bin/env python3 """Tool Name - One-line description.""" import argparse import sys from pathlib import Path def main(): parser = argparse.ArgumentParser(description="Tool description") parser.add_argument("--flag", help="What it does") args = parser.parse_args() # Tool logic here if __name__ == "__main__": main() Key decisions: Python 3.8+ for universal compatibility Click for CLI frameworks (consistent UX) Single-file executables where possible pip-installable packages for complex tools MIT license for everything Phase 3: Rapid Development (6 hours) Here's where AI assistance becomes crucial. I used a structured approach: For each tool: Write a detailed specification (5 minutes) Generate core logic with AI assistance (10 minutes) Add error handling and edge cases (10 minutes) Write tests (10 minutes) Create documentation (5 minutes) Total per tool: ~40 minutes Let me show you three examples from the toolkit: Tool Showcase 1. DevSetup CLI - Project Initialization Made Easy Problem: Every new project requires the same setup: virtual environment, git init, pre-commit hooks, initial commit. Solution: One command does it all. $ devsetup --python --node --git --pre-commit ✓ Created Python virtual environment ✓ Initialized git repository ✓ Installed pre-commit hooks ✓ Created initial commit Project ready in 12 seconds Key features: Auto-detects project type (Python, Node, Rust, Go) Sets up virtual environments Configures pre-commit hooks Creates sensible .gitignore Generates initial README Code snippet: def detect_project_type(path: Path) -> str: """Detect project type based on files present.""" if (path / "Cargo.toml").exists(): return "rust" elif (path / "package.json").exists(): return "node" elif (path / "requirements.txt").exists() or (path / "pyproject.toml").exists(): return "python" elif (path / "go.mod").exists(): return "go" return "unknown" 2. Code Review CLI - AI-Powered Pre-Commit Review Problem: Bugs slip through code review. By the time someone catches them, they're already in the codebase. Solution: AI review before every commit. $ codereview --staged Analyzing staged changes... ⚠️ Potential SQL Injection in auth.py:42 cursor.execute(f"SELECT * FROM users WHERE id = user_id") Suggested fix: cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,)) ✓ No other issues found Commit anyway? [y/N]: n Key features: Analyzes staged changes before commit Detects security issues, bugs, and anti-patterns Integrates with pre-commit hooks CI/CD integration for automated review Supports custom rule sets Real-world impact: This tool caught a potential SQL injection in my own code that would have cost hours to fix in production. 3. GitHub Bounty Hunter - Find Paid Issues Fast Problem: Finding GitHub issues that pay bounties requires manual searching across multiple repositories. Solution: Automated bounty discovery. $ bounty-hunter --min-reward 50 --language pyth