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Convert codebases to LLM-ready text with token counting, smart formatting, and context optimization Are you tired of manually copy-pasting code into ChatGPT, Claude, or Gemini? Do you struggle to stay within token limits when asking for code reviews or refactors? Codebase to String is the essential developer utility that converts your entire project—whether it's a ZIP file or a local folder—into a single, optimized text formatted specifically for Large Language Models. Key Features: 🚀 Dual Upload Modes Drag & Drop ZIPs: Instantly process archived projects without extracting. Folder Selection: Pick any directory from your machine to process immediately. 📊 Real-Time Token Intelligence Live Stats: See file counts, character counts, and accurate token estimations (based on GPT-4 tokenization rules) as you work. Smart Budgeting: Set a token limit (e.g., 32k, 128k). If your codebase exceeds it, the extension automatically splits the output into perfectly sized chunks, ready for sequential prompting. ✨ LLM-Ready Optimization Comment Stripping: Option to remove comments from code (JS, Python, Java, etc.) to save up to 30% of your token usage. Noise Reduction: Automatically removes extra blank lines and unnecessary whitespace. Sensitive Data Watchdog: Scans for potential API keys, secrets, and AWS credentials before you paste, keeping your data safe. 🎨 Flexible Output Formats Markdown: Syntax-highlighted code blocks with file paths (Best for context). XML: Tag-based structure for tools that prefer structured data. Plain Text: Simple, clean output. 💻 Developer-Centric UX Dark Mode: Sleek, glassmorphic UI that matches your IDE. Filter Control: Exclude node_modules, .git, or specific file types easily. One-Click Actions: Copy to clipboard or download as .md file. Why use Codebase to String? Prepping context is the most tedious part of AI-assisted programming. This extension automates the formatting, security checking, and token management so you can focus on the logic. Perfect for refactoring legacy code, generating documentation, or onboarding an AI to your project architecture. Privacy-First: All processing happens locally in your browser. Your code never leaves your machine until you choose to verify it.