go-deeplisted

Create a 3-tier layered documentation system from scratch. The goal is to keep context concise and well-organized while ensuring the AI agent has all necessary information. ## Information Gathering Process **Phase 1: Code Discovery** First, analyze the existing codebase to understand what's already there: - Scan the directory structure to identify main folders and organization - Read key configuration files (package.json, requirements.txt, etc.) to identify tech stack - Look for existing documentation (README, docs folders, inline comments) - Identify main code patterns, components, and modules - Search for entry points (main.py, index.js, app routes, etc.) - Find existing use case implementations or feature areas - Identify domain models, entities, or data structures **Phase 2: Targeted Questions** After code discovery, announce how many questions you have, then ask each one using the `AskUserQuestion` tool — one call per question. Only ask about information that could not be determined from code analysis. Announce first: > "I've analyzed the codebase and have [N] questions. I'll ask them one at a time." For each question: - Call `AskUserQuestion` with: - `question`: `"Question [X] of [N] · [Category]: [The question]\n\n[One sentence explaining why this matters.]"` — where `[Category]` is either `Product/domain` (questions 1–10) or `Architecture/technical` (questions 11–20). - `options`: up to 3 substantive pre-enumerated choices + always `{ label: "Skip — clear fro