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Code2prompt
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Code2Prompt Documentation

Transform Your Code into AI-Optimized Prompts in Seconds
Get Started 🚀 Installation 📥

Quick Start

Section titled “Quick Start”
Getting Started 🚀
Installation 📥
API Reference
Learn Filtering 🔍
Learn Templating 📝
Vision 🔮

code2prompt is a powerful code ingestion tool designed to generate prompts for code analysis, generation, and other tasks. It works by traversing directories, building a tree structure, and gathering informations about each file.

It simplifies the process of combining and formatting code, making it easy to analyze, document, or refactor code using LLMs

You can use code2prompt the following ways:

Core

Core library blazingly fast for code ingestion

CLI

Command Line Interface specially designed for humans

SDK

Software Development Kit for AI agents and automation scripts

MCP

Model Context Protocol server for LLMs on steroids

Key Features

Section titled “Key Features”
  • Generate LLM Prompts: Quickly convert entire codebases into structured LLM prompts.
  • Glob Pattern Filtering: Include or exclude specific files and directories using glob patterns.
  • Customizable Templates: Tailor prompt generation with Handlebars templates.
  • Token Counting: Analyze token usage and optimize for LLMs with varying context windows.
  • Git Integration: Include Git diffs and commit messages in prompts for code reviews.
  • Respects .gitignore: Automatically ignores files listed in .gitignore to streamline prompt generation.

Why code2prompt?

Section titled “Why code2prompt?”

Instead of just blindly concatenating files together, code2prompt is built to curate the perfect context window for your LLM, saving you money and preventing model hallucination.

  1. Smart Data Extraction: Stop blowing up your token limits on raw data. When code2prompt encounters a .csv, .tsv, .jsonl, or Jupyter Notebook (.ipynb), it intelligently extracts only the structural schema and sample rows/cells, giving the LLM the context it needs without the bloat.

  2. Precision Context Filtering: It natively respects your .gitignore and uses an advanced glob pattern engine (—include and —exclude) so you can isolate exactly which modules you want to send.

  3. Zero-Overhead Token Math: It uses native Tiktoken encodings (like o200k and cl100k) to calculate exact token counts locally before you send a massive payload to an API.

  4. Template-Driven Workflows: Use powerful Handlebars templates to automatically inject your project’s directory tree, git diffs, and source code into specialized, repeatable prompts.

  5. Built for Any Environment: Navigate visually with the built-in TUI, automate tasks via the CLI, or give LLM agents direct repository access using the MCP server.

Example Use Cases

Section titled “Example Use Cases”

  • One-Shot PR & Commit Generation: Leverage built-in Git integration to compare branch diffs and let your LLM write highly detailed, context-aware PR descriptions automatically.

  • CTF & Security Analysis: Use built-in templates explicitly designed for Web, Cryptography, or Binary Exploitation CTF solving, handing the LLM the exact file constraints it needs to spot vulnerabilities.

  • Architectural Refactoring: Feed a specific module—along with the visual directory tree—into a cleanup template to get refactoring suggestions that respect your project’s overall structure.

  • Instant Onboarding & Documentation: Generate a full repository token map and structural overview to quickly understand massive new codebases, or auto-generate robust README.md files.