Code2Prompt

Transform Your Code into AI-Optimized Prompts in Seconds

Free

Description

Code2Prompt is designed to streamline the process of feeding code or text-based repositories into AI models. It efficiently transforms your local file structures, such as personal notes, recipe databases, or entire codebases, into clearly structured prompts. This structured output provides AI with the necessary context, enabling more accurate and relevant responses for various tasks like analysis, summarization, or generation.

The tool offers flexibility through multiple interfaces including a command-line interface (CLI) for quick terminal operations, a Python SDK for integration into custom applications, and a Model Context Protocol (MCP) for advanced agent interactions. By leveraging features like glob pattern filtering, Code2Prompt helps in focusing the AI's attention on relevant files, thereby reducing noise and improving the performance and cost-effectiveness of LLM queries. It is an open-source project, emphasizing speed and efficiency with its Rust-based core.

Key Features

High-Performance: Written in Rust for speed and efficiency, handling large codebases with minimal resource usage.
Handlebars-Powered Templates: Enables customizable prompt generation, giving users full control over the output format.
Smart Filtering: Supports include/exclude patterns with glob matching for precise code and text selection (planned feature).
Multi-Format Support: Exports structured prompts in JSON, Markdown, or XML with various formatting options.
Git Integration: Includes Git diff and log extraction for better contextual understanding of code changes over time.
Open-Source & Community-Driven: Developed under the MIT license, encouraging collaboration and contributions.
CLI Interface: Allows users to generate structured prompts directly from the terminal quickly.
Python SDK: Facilitates integration of Code2Prompt into Python applications for advanced automation.
Model Context Protocol (MCP): Designed for agents to enhance their contextual understanding.

Use Cases

Generating AI-powered study aids like flashcards from personal notes repositories.
Creating prompts for AI to suggest recipes based on ingredients listed in a local database.
Assisting AI models in understanding and analyzing software codebases for tasks like feature implementation or debugging.
Optimizing LLM queries by providing filtered, context-rich prompts from specific files or directories.
Automating content summarization or information extraction from text-based repositories.
Integrating dynamic prompt generation into Python-based AI workflows and applications.

Frequently Asked Questions

What is Code2Prompt?

Code2Prompt simplifies code ingestion, turning your repository into structured prompts for AI and automation. It transforms any text repositories into meaningful prompts.

Why use Code2prompt?

Code2Prompt introduces a new development workflow, enabling AI and human agents to interact with code efficiently. It leverages glob patterns to include or exclude only the relevant files. This allows you to query LLMs without extra noise, thus reducing hallucination and increasing performance.