tobieapb

I used to whiteboard, then code. Now I markdown, then code. This is how I vibe-code: I can direct, guardrail, and understand the LLM-built code, come back to it 6 months later and actually understand it, and more importantly, craft user-facing documentation artifacts trivially.

100
11
100% credibility
Found Feb 02, 2026 at 26 stars 4x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A methodology with Markdown templates and guidelines for using AI to systematically interview, document, and plan software features.

How It Works

1
🔍 Discover the Guides

You stumble upon this helpful collection of writing guides that make planning and documenting projects super clear with AI help.

2
📥 Grab the Files

You download the simple folder of guide documents to your own computer to get started.

3
💡 Pick Your Project Idea

You choose a feature you want to build, like a login system for your app.

4
🗣️ Interview Your Idea

You chat back and forth with your smart AI helper using the interview guide to uncover every detail without any confusion.

5
📖 Review Your Docs

You get a polished write-up that perfectly explains what your feature is, why it matters, and how it fits everything.

6
📋 Craft the Plan

You follow the planning guide to turn your docs into a straightforward roadmap anyone could use to build it.

Perfect Plans Ready

Now your project has amazing, easy-to-understand documents and steps that save time and prevent mistakes later.

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Star Growth

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AI-Generated Review

What is claude-interactive-documentation-workflow?

This repo delivers a markdown-first workflow powered by Claude AI that replaces whiteboarding—think excalidraw whiteboard github or mermaid whiteboard github—with structured LLM interviews for specs, docs, and plans before coding. You kick off with a simple /interview command in Claude Code to extract feature details across four passes (shape, flow, detail, completeness), generating ready-to-use documentation artifacts and language-agnostic implementation plans. The result: code you can direct, guardrail, understand six months later, and craft user-facing docs from trivially.

Why is it gaining traction?

Unlike open source whiteboard github apps or microsoft whiteboard github clones that fade after the sketch, this enforces no-TBD rules and 4-pass refinement for ambiguity-free outputs, backed by git-verified production metrics like 7:1 plan-to-execution ratios and 67k lines of docs per project. Developers hook on the real-world examples from computer vision pipelines, plus easy adaptation for any LLM, making AI-assisted coding actually reliable without constant wrangling.

Who should use this?

Solo full-stack devs or small teams building backend services, ML pipelines, or auth systems who rely on Claude or similar LLMs for code gen. Ideal for those tired of online whiteboard github tools leaving vague notes—use it when you need to come back to code later or produce quickstarts, troubleshooting guides, and release notes without extra effort.

Verdict

Grab it if you're vibe-coding with AI and want guardrails; 87 stars and 1.0% credibility score signal early maturity with thin tests, but stellar docs and production proof make it worth forking now. Solid for LLM-heavy workflows, skip if you shun markdown over collaborative whiteboard github vibes.

(198 words)

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