Repo Prompt

Product Introduction

1. Definition: Repo Prompt is a macOS-native context engineering tool designed for developers using AI coding assistants like Claude Code, ChatGPT, or Codex. It functions as an intelligent layer between a developer's codebase and Large Language Models (LLMs), optimizing the code context provided to these models.
2. Core Value Proposition: Repo Prompt exists to eliminate token waste and significantly improve the relevance of code context provided to AI models. It achieves this by intelligently selecting only the necessary files, functions, and structural summaries, ensuring dense, high-value context fits within model token limits, leading to faster, more accurate AI-generated code and reducing API costs when using paid subscriptions.

Main Features

1. Context Builder:
   • How it works: Users describe their coding task. Repo Prompt leverages connected AI agents (using the user's existing subscriptions like Claude Pro or ChatGPT Plus) to automatically analyze the project structure and identify the most relevant files and functions needed for the task. It dynamically assembles a concise context prompt optimized for the specified token budget.
   • Technology: Integrates with AI models via API (OpenAI, Anthropic, Gemini, etc.) or MCP protocol for agentic discovery. Performs semantic and structural analysis of the codebase.
2. CodeMaps:
   • How it works: Repo Prompt locally scans the entire codebase to generate structural summaries ("Codemaps"). These summaries extract and condense critical information like function signatures, type definitions, class structures, and API contracts into a highly token-efficient format.
   • Technology: Local parsing and static analysis to extract code structure without sending the entire codebase externally. Achieves up to 90% token reduction compared to raw code.
3. MCP Server Integration:
   • How it works: Repo Prompt acts as a backend server implementing the MCP (Model Control Protocol) standard. This allows external AI tools like Claude Desktop, Cursor editor, or Codex CLI to query Repo Prompt for context. Repo Prompt provides relevant files, Codemaps, or utilizes the Context Builder on-demand for these tools.
   • Technology: MCP server implementation enabling persistent context sync, agent-to-agent collaboration (e.g., Claude consulting Gemini), direct file operations (create/edit), and code structure analysis for connected clients.
4. Visual File Tree & Advanced Search:
   • How it works: Provides a native macOS file explorer interface with instant previews. Supports multi-repository workspaces. Users can manually select files using powerful search (including regex) and gitignore filtering for precise control over context.
   • Technology: Native macOS UI framework, regex processing, git integration for ignore patterns.
5. Repo Bench (Pro Feature):
   • How it works: A benchmarking suite that rigorously tests and ranks AI coding models (LLMs) on metrics critical for real-world development: large context reasoning, multi-file editing precision, and strict instruction adherence. Provides data-driven insights into model performance for specific codebases.
   • Technology: Automated test harness executing standardized coding tasks across hundreds of models, community-driven result aggregation, real-time leaderboard updates.

Problems Solved

1. Pain Point: Massive token waste and irrelevant context when feeding entire codebases or manually selected snippets to AI coding assistants, leading to high costs (for API usage) and reduced output quality/accuracy.
2. Target Audience: Software engineers, full-stack developers, AI engineers, engineering managers, and technical leads working with substantial codebases who regularly use AI coding assistants (Claude Code, ChatGPT, Cursor, Codex) for tasks like debugging, refactoring, feature development, or documentation. Particularly valuable for macOS-based developers in fast-paced environments.
3. Use Cases:
   • Efficiently generating code or documentation requiring understanding of multiple interconnected files.
   • Performing complex refactors where AI needs architectural context.
   • Onboarding AI assistants onto large, existing projects.
   • Reducing Claude/OpenAI/Gemini API costs by minimizing token usage per task.
   • Benchmarking AI models (Repo Bench Pro) to select the best performer for a specific codebase or task type.
   • Maintaining consistent context across different AI tools (Claude Desktop, Cursor) via MCP sync.
   • Working with multi-repo projects or monorepos.

Unique Advantages

1. Differentiation: Unlike manual context selection or basic IDE plugins, Repo Prompt offers automated, AI-powered context discovery (Context Builder) and dense structural summarization (CodeMaps). It uniquely functions as a centralized context server (MCP) for multiple AI tools, unlike competitors focused solely on single-editor integration. Its macOS-native design offers superior integration compared to web-based or cross-platform tools.
2. Key Innovation: The integration of automated, AI-driven context discovery (Context Builder) combined with local structural code summarization (CodeMaps) and the MCP Server protocol forms its core innovation. This trio allows Repo Prompt to dynamically find, condense, and serve the most relevant code understanding to any connected AI tool, drastically improving efficiency and capability beyond what the AI tools can achieve natively. The MCP protocol enables unprecedented interoperability between different AI agents and editors.

Frequently Asked Questions (FAQ)

1. How does Repo Prompt save tokens with AI like Claude or ChatGPT? Repo Prompt drastically reduces Claude token usage or ChatGPT token costs by intelligently selecting only relevant code files and functions (Context Builder) and generating ultra-dense structural summaries (CodeMaps) instead of including verbose raw code, often achieving 80-90% token reduction.
2. Is my code processed locally or sent to the cloud with Repo Prompt? Core Repo Prompt operations like file browsing, search, and CodeMap generation run locally on your Mac. Code context is only sent to external AI models (Claude, GPT, Gemini) when you explicitly use chat features, Context Builder (which uses your connected AI), or when serving connected MCP clients, using your existing API keys.
3. Can Repo Prompt integrate with Claude Code directly? Yes, Repo Prompt offers deep Claude Code integration. You can use your Claude subscription within Repo Prompt's chat, and crucially, the MCP Server allows Claude Desktop to dynamically pull optimized context (files, Codemaps) from Repo Prompt, significantly enhancing Claude Code's understanding of your codebase.
4. What is the MCP Server in Repo Prompt used for? The Repo Prompt MCP Server transforms it into a backend for AI tools like Claude Desktop, Cursor editor, or Codex CLI. It enables persistent context sync across tools, allows AI agents to collaborate, provides intelligent context discovery on-demand, and allows direct file operations from connected clients using the standard MCP protocol.
5. Is there a Windows or Linux version of Repo Prompt available? Repo Prompt is currently a native macOS application. A Windows/Linux version is under development, and users can join the waitlist via the Repo Prompt website for updates on cross-platform availability.