gemini-vault-fusion

name	gemini-vault-fusion
description	After completing reverse engineering vault, use a high-reasoning LLM (Gemini 3.1 Pro Preview or similar) to review all documentation, identify inconsistencies, generate a professional PRD/Blueprint structure, then FUSE the AI output with existing deep technical docs into a v3.0 definitive document.
tags	["reverse-engineering","document-review","gemini","ai-assisted","fusion-strategy","prd","blueprint"]

Gemini Vault Fusion — AI-Assisted Documentation Review & Consolidation

When to Use

After completing a large reverse engineering vault (30+ documents, 20+ modules, ~200KB+), when you need to:

1. Audit for inconsistencies across documents
2. Generate a professional-quality PRD/Blueprint with personas, journeys, acceptance criteria, metrics
3. Not lose technical depth in the process

The Problem

• AI-only output (feeding vault to Gemini and asking for PRD): produces professional structure but loses deep technical content — schemas get simplified, UX divergences disappear, state machines become generic, risks/implementation waves vanish.
• Human-only docs: have glorious technical depth (797-line schemas, 7 UX divergences, 4 state machines) but lack executive polish — no formal personas, no acceptance criteria section, no success metrics.

The solution is not to choose. It's to FUSE.

The Fusion Strategy

Phase 1 — Vault Consolidation

1. Gather ALL documentation files from the project
2. Consolidate into ONE file with clear source headers:

   ## Fonte: docs/caminho/arquivo.md
   [full content of file]
   

3. Save to /tmp/vault_completo.md

Phase 2 — AI Review (Gemini 3.1 Pro Preview)

Feed the consolidated vault to a high-reasoning model with a structured audit prompt:

Revise toda esta documentação de engenharia reversa do [SISTEMA].
Identifique:
1. Inconsistências entre documentos (com documento e linha)
2. Informações faltantes
3. Contradições
4. Sobreposições
5. No final: nota de 0-10 para completude e consistência

Save output as docs/AUDITORIA-GEMINI-[MODELO].md

Phase 3 — AI Generation (Second Prompt)

Com base em toda esta documentação, produza:
1. PRD completo e profissional — visão geral, personas, funcionalidades,
   regras de negócio, jornadas de usuário, critérios de aceitação
2. Blueprint arquitetural — stack, schema, multi-tenancy, API design,
   UI architecture, infraestrutura

Save outputs as draft v2.0.

Phase 4 — Human Cross-Validation

Deploy a dedicated auditor ({{AUDITOR}} role) to compare AI output vs existing docs:

Aspect	AI v2.0	Human v1.x	Veredict
Structure/Personas	✅ Professional	⚠️ Lacking	🏆 AI wins
Deep schemas	❌ 15 generic	✅ 30+ detailed	🏆 Human wins
UX divergences	❌ Zero	✅ 7 documented	🏆 Human wins
State machines	⚠️ Incomplete	✅ Complete	🏆 Human wins

Phase 5 — Fusion (v3.0)

Strategy: Use AI v2.0 as the shell (executive structure, personas, journeys, acceptance criteria, metrics), then inject the deep technical content from human v1.x (schemas, UX divergences, performance targets, state machines, risks, implementation waves).

Result: PRD-v3.0.md and BLUEPRINT-v3.0.md — professional AND implementable.

Phase 6 — Fix Real Gaps Found

The AI audit will find real gaps. Fix them:

Priority	Gap	Action
🔴	Schema contradictions	Update source docs immediately
🟡	Architecture gaps (Docker networking, auth)	Document resolution
🟢	Bloated files	Clean up

⚠️ Pitfalls

1. Model names change fast. Always verify available models via API before stating what exists. {{COMMANDER}} corrected us that Gemini 3.5 Flash and 3.1 Pro Preview existed when we thought they didn't.

   from google import genai
   client = genai.Client(api_key=key)
   for m in client.models.list():
       print(f'{m.name}: {m.supported_actions}')
   

   Don't guess model names — check the API.

2. AI loses technical depth. The v2.0 will simplify schemas, drop UX divergences, and genericize risks. Plan the fusion BEFORE generating, not after.

3. The shell is the easy part. 80% of the v3.0 fusion effort is identifying what the AI dropped and re-injecting it. Budget time for this.

4. Cross-validation is essential. Never trust AI-generated documentation without a human auditor comparing it point-by-point against the originals.

5. Context window management. A vault of 193KB+ fits in Gemini 2M context, but the response may truncate. Split into chunks if needed, or use a model with large context (Gemini 3.1 Pro Preview = 2M tokens).

Model Selection Guide

For	Model	Why
Deep review + generation	gemini-3.1-pro-preview	Best reasoning, 2M context, catches inconsistencies
Fast iteration / re-generation	gemini-3.5-flash	Faster, cheaper, still capable
Vision (screenshots)	gemini-2.5-flash (legacy) or Kimi K2.5 via opencode-go	For image analysis during RE

Example Output

This skill was developed and validated on the {{PROJECT_NAME}}/Dontus RE project:

• Input: 18 documents, 28 modules, 193KB vault
• AI Model: gemini-3.1-pro-preview
• Audit found: 5 real inconsistencies (Vue.js vs Alpine.js, FK direct vs N:N, Docker networking missing, auth cross-tenant undocumented, bloated cadastros doc)
• Fusion result: PRD v3.0 (7.7KB) + Blueprint v3.0 (7.2KB)
• Time: ~3 hours total (1h vault consolidation + 30min AI generation + 1h cross-validation + 30min fusion)

Related Skills

• re-audit-consolidation — For the pre-fusion audit phase (multi-agent coverage assessment)
• prd-clone-exhaustivo — For the initial mapeamento/discovery phase