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Generate a Draft Pull Request from a Figma Design with Reddit Proto

Use Reddit's internal toolchain to translate a high-fidelity Figma design into a structured Product Requirements Document (PRD) and then into production-shaped code for engineering review.

Illustrated workflow from Figma design to a reviewed PRD and a draft pull request with Reddit Proto.

It'll spit out a PRD. And so I'm gonna say, 'Yes, this is what you're looking at,' and I'll continue. And then it'll generate this PRD... here you've got your generated PRD, and it'll say, like, 'This is what the user goal is. This is the acceptance criteria.'

Before you start

What you need

  • A URL to a specific, high-fidelity frame in Figma.
  • A design system map connecting Figma components to production code.
  • Authorized access to Reddit’s internal Proto toolchain and repositories.

What you’ll make

A draft pull request in a sibling repository containing production-shaped code that implements the Figma design, ready for engineering review.

Tools used

  • Figma

    Collaborative design tool

    VisitFigma
  • Reddit Proto

    Reddit’s internal prototyping harness for building and reviewing interactive prototypes with its design system and authorized data. Available to authorized Reddit teams; not a public product.

  • Cursor

    AI-first code editor

    VisitCursor

The workflow

Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.

6 steps

Step 01

Create a Feature Capsule

In the Reddit Proto launchpad, start a new 'feature capsule' project. Paste the URL of the specific Figma frame you want to implement.

This workflow is most effective for high-fidelity UI, not broad 'kitchen sink' feature ideas. Focus on a specific frame.

Step 02

Analyze the Design

The system uses a custom 'Figma MCP' to analyze the Figma frame. It maps the design elements to known production components, tokens, and patterns from the Reddit Product Language (RPL) design system.

The quality of this step depends entirely on the accuracy of the underlying map between the design system and production code.

Step 03

Generate and Review the PRD

The tool generates a Product Requirements Document (PRD) that includes the inferred user goal, acceptance criteria, and technical details. You must review this document for accuracy.

This is the most important human checkpoint. Do not proceed if the PRD is incorrect. Correct the PRD to ensure the AI has the right instructions.

Step 04

Approve PRD and Send to IDE

Once you confirm the PRD is correct, accept it. The system saves the PRD file and sends it to your IDE (Cursor) as the primary context for code generation.

The PRD is the formal handoff artifact, ensuring scope and goals are explicit before the agent writes code.

Step 05

Generate Production-Shaped Code

In Cursor, using the custom 'Proto MCP Ultra' context, instruct the AI to generate code based on the approved feature capsule and PRD.

Example prompt
[Generate the feature described in the provided PRD using our production components]

The AI uses its knowledge of the design system map, component examples, and past engineering feedback to write code that is shaped like production code.

Step 06

Isolate, Test, and Submit PR

The generated code is written to a sibling repository to keep production clean. After testing, a draft pull request is created against the main repository for formal engineering review.

The PR may or may not be approved. The review process is the final quality gate.

What good looks like

  • The generated PRD accurately reflects the user goals and acceptance criteria from the design.
  • The generated code is written to a separate branch or repository.
  • The final draft PR passes automated CI checks and is deemed acceptable by human engineering reviewers.

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Runbook notes

How to recover when the loop fails and where human judgment helps.

If it goes sideways

The generated PRD misinterprets the user goal or acceptance criteria from the Figma design.
Manually edit the PRD before accepting it. This human review step is critical to ensure the AI is building the right thing before any code is written.
The generated code fails CI review or is rejected by engineers for quality reasons.
This is an expected part of the process. The feedback from the failed review is used to iterate on the code. This feedback also helps refine the underlying MCP for future use.

Human checkpoints

Let routine work keep moving.

  1. A mandatory review and approval of the auto-generated PRD before proceeding to code generation.
  2. A formal engineering code review of the final draft pull request before it can be merged into production.

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