Repo Prompt
Repo Prompt · Coding
Repo Prompt is a native macOS application for context engineering: it helps you pick the right files, assemble dense context for a large language model, and hand that work off to a coding agent. As an AI coding context tool, its job is to keep the model focused on the files that matter. The current Community Edition is free and open source, and it connects MCP clients, coordinates sub-agents across providers, and keeps every change reviewable. If you write code with AI and keep hitting context limits, it exists to fix exactly that.
About Repo Prompt
What Is Repo Prompt
Repo Prompt started as a manual prompt builder: you selected files from a repository, assembled a prompt, and copied it into whatever AI tool you preferred. That Classic workflow is now archived. Active development moved to the Community Edition, which is open source and built around coding agents rather than copy and paste.
The core problem it addresses is context quality. A model only reasons well over the code you actually feed it, and dumping an entire repository into a chat window wastes tokens on files that don't matter. Repo Prompt focuses on choosing that context deliberately, then keeping the resulting edits in a form you can review before they land.
The main constraint is platform. It runs on macOS only, and the most valuable features assume you already work with a coding agent or an MCP client. Weigh this before you commit: it's more of an AI coding context tool than a chat app. If you just want a chatbot to explain a snippet, it's more machinery than you need.
Getting Started
- Download the app from the official Repo Prompt website and open it on a Mac.
- Point it at a local repository so it can index the files.
- Use Context Builder and CodeMaps to select the files that matter for your task.
- Connect an MCP client or a CLI agent, or let the orchestrator dispatch sub-agents.
- Review the plan and the handoffs before accepting any edits.
Product Information
A quick look at Repo Prompt's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Developers working in large codebases
- Teams running multiple coding agents
- Solo builders who want reviewable AI edits
Tasks
- Scoping a multi-file refactor
- Feeding context to an external chat model
- Connecting MCP tools to a code session
- Reviewing agent output before it ships
Scenarios
- Planning a feature, then splitting it across parallel agents
- Onboarding into an unfamiliar repository
- Running a long build-and-fix loop
Key features
Context Builder
Context Builder is what you reach for when a repository is too big to feed to a model wholesale. You select the files that matter, and the app assembles them into dense, structured context. The point is a smaller, sharper prompt that costs fewer tokens and produces more relevant answers.
CodeMaps
CodeMaps give you a map of a codebase rather than a flat file list. It maps structure. That's the difference. For large or unfamiliar projects, it means you hand the model an outline instead of a wall of raw text, and onboarding into someone else's repo gets less painful.
Agent Orchestration
Agent orchestration is the headline feature here. The orchestrator takes a plan, breaks it into tasks, then dispatches sub-agents across providers, each scoped to its own files. A shared plan document is the source of truth. Before continuing, the orchestrator checks that work followed the plan. You get parallel output without agents stepping on each other's edits.
MCP and CLI Integrations
Repo Prompt connects MCP clients and CLI agents, so it fits into a toolchain you already run instead of replacing it. For many people this is their MCP client for macOS. That matters more than it sounds. If your setup already speaks the Model Context Protocol, this is the bridge that brings those tools into a macOS workspace, and you keep the editor and agents you like rather than switching stacks.
Reviewable Handoffs
Handoffs are built to be inspected. Instead of an agent quietly rewriting files, the app keeps changes in a reviewable form so you can accept or reject them. For anyone nervous about handing a real codebase to an agent, that's the safety net. It's the feature that turns orchestration from a leap of faith into a controlled process.
Open Source Community Edition
The Community Edition is free, open source, and the active home for new development on GitHub. You can read the code, report issues, and contribute. The older Classic edition stays available for established manual workflows, but it no longer gets updates. New work should start on the Community Edition.
Pros and cons
Pros
- Free and open source, so there is no subscription barrier to trying it.
- Context Builder and CodeMaps target the real bottleneck: choosing which code a model should see.
- Agent orchestration with scoped sub-agents reduces collisions when several agents work in parallel.
- MCP and CLI integrations mean it slots into existing AI coding setups.
- Reviewable handoffs keep a human in the loop before edits land.
Cons
- macOS only, so Windows and Linux developers can't use it at all.
- There is no public API, which limits automation beyond the app's own surfaces.
- The most useful features assume you already run a coding agent or an MCP client, so absolute beginners face a steeper start.
- The Classic workflow is archived, meaning anyone still relying on it gets no future updates or support.
Frequently asked questions
It's a macOS app for context engineering: choosing the right files, building dense prompts, and coordinating AI coding agents. Call it a context engineering tool for developers who want models to work on large codebases without drowning in irrelevant files.
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