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vibn-frontend/docs/PHASE_SYSTEM.md

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# Vibn Phase-Based Project System
⚠️ **LEGACY DOCUMENTATION** - This document describes the old phase system.
**The new system is documented in `/lib/ai/types.ts` and uses a workflow-based architecture.**
New phases:
1. **Collector** - Document intake (conversational)
2. **Extractor** - Insight extraction (JSON)
3. **Vision** - Vision synthesis (JSON)
4. **MVP** - MVP planning (JSON)
5. **Marketing** - Marketing strategy (JSON, parallel)
See `/lib/ai/orchestrator.ts` for the state machine implementation.
---
## The 5 Phases (Legacy)
### Phase 1: Gathering
**Agent:** Gathering Agent
**Goal:** Collect and analyze all project materials one item at a time
**What it does:**
- Goes through each context source (docs, GitHub, sessions)
- Extracts key insights from each
- Confirms each insight with user
- Stores confirmed insights
**Firestore Schema:**
```javascript
{
currentPhase: 'gathering',
phaseStatus: 'in_progress', // 'not_started' | 'in_progress' | 'completed'
phaseData: {
gathering: {
startedAt: timestamp,
insights: [
{
id: 'insight_1',
source: 'Patient History Overview',
sourceType: 'document',
sourceId: 'QeC8EIDSkud1jrt6xSyF',
insight: 'Using evidence-based diagnostic methods',
extractedAt: timestamp,
confirmed: true,
confirmedAt: timestamp,
usedInVision: false,
category: 'feature'
}
],
completedAt: timestamp
}
}
}
```
**Completion Criteria:**
- All context sources analyzed
- All insights confirmed by user
- User says "That's everything" or "Ready to proceed"
---
### Phase 2: Vision
**Agent:** Vision Agent
**Goal:** Extract Product Vision Board from confirmed insights
**What it does:**
- Reads all confirmed insights from Phase 1
- Extracts the 8 vision nodes
- Fills out 4-block Product Vision Board
- Gets user approval
**Firestore Schema:**
```javascript
{
currentPhase: 'vision',
phaseStatus: 'in_progress',
phaseData: {
vision: {
startedAt: timestamp,
data: {
vision: 'One-sentence vision',
who: 'Target users',
need: 'Problem they face',
product: 'Solution features',
validation: 'Go-to-market strategy'
},
approved: true,
approvedAt: timestamp,
completedAt: timestamp
}
}
}
```
**Completion Criteria:**
- Vision board complete
- User approves with "Yes" or "Looks good"
---
### Phase 3: Scope
**Agent:** Scope Agent
**Goal:** Define V1 MVP features
**What it does:**
- Takes vision board from Phase 2
- Helps user prioritize features (must-have, should-have, nice-to-have)
- Defines timeline estimate
- Creates V1 feature list
**Firestore Schema:**
```javascript
{
currentPhase: 'scope',
phaseStatus: 'in_progress',
phaseData: {
scope: {
startedAt: timestamp,
v1Features: ['Feature 1', 'Feature 2'],
timeline: '3-6 months',
priorities: {
mustHave: [],
shouldHave: [],
niceToHave: []
},
completedAt: timestamp
}
}
}
```
---
### Phase 4: Blueprint
**Agent:** Blueprint Agent
**Goal:** Technical architecture design
**What it does:**
- Takes V1 scope from Phase 3
- Defines tech stack
- Designs database schema
- Plans API structure
---
### Phase 5: Build
**Agent:** Build Agent (Future)
**Goal:** Implementation guidance
---
## Why Phase Tracking?
### ✅ Prevents Repetition
Once Phase 1 is complete, you never re-analyze documents. Phase 2 uses the stored insights.
### ✅ User Confidence
User sees clear progress: "Gathering: ✅ | Vision: ✅ | Scope: 🔄"
### ✅ Resumable
User can leave and come back. The agent knows exactly where they left off.
### ✅ Traceable
Every insight is linked to its source. Vision board items trace back to specific documents.
---
## API Usage
### Get Current Phase
```typescript
GET /api/projects/phase?projectId=xxx
Response:
{
currentPhase: 'gathering',
phaseStatus: 'in_progress',
phaseData: { /* phase-specific data */ },
phaseHistory: []
}
```
### Start a Phase
```typescript
POST /api/projects/phase
{
projectId: 'xxx',
action: 'start',
phase: 'gathering'
}
```
### Complete a Phase
```typescript
POST /api/projects/phase
{
projectId: 'xxx',
action: 'complete'
}
```
### Save Phase Data
```typescript
POST /api/projects/phase
{
projectId: 'xxx',
action: 'save_data',
data: { /* phase-specific data */ }
}
```
### Add Gathering Insight
```typescript
POST /api/projects/phase
{
projectId: 'xxx',
action: 'add_insight',
data: {
id: 'insight_1',
source: 'Document Name',
sourceType: 'document',
sourceId: 'doc_id',
insight: 'Key finding...',
extractedAt: timestamp,
confirmed: true,
confirmedAt: timestamp,
usedInVision: false,
category: 'feature'
}
}
```
---
## Agent Selection Logic
```typescript
// In /api/ai/chat/route.ts
// 1. Check project phase
const projectPhase = project.currentPhase || 'gathering';
const phaseStatus = project.phaseStatus || 'not_started';
// 2. Select appropriate agent
let agentPrompt;
switch (projectPhase) {
case 'gathering':
agentPrompt = GATHERING_AGENT_PROMPT;
break;
case 'vision':
agentPrompt = VISION_AGENT_PROMPT;
break;
case 'scope':
agentPrompt = SCOPE_AGENT_PROMPT;
break;
// ... etc
}
// 3. Pass phase data to agent
const contextPayload = {
...existingContext,
currentPhase: projectPhase,
phaseStatus: phaseStatus,
phaseData: project.phaseData || {}
};
```
---
## Migration Plan
### For Existing Projects
Run a migration script to add phase fields:
```javascript
// scripts/add-phase-tracking.ts
const projects = await adminDb.collection('projects').get();
for (const doc of projects.docs) {
await doc.ref.update({
currentPhase: 'gathering',
phaseStatus: 'not_started',
phaseData: {},
phaseHistory: []
});
}
```
---
## Next Steps
1. ✅ Phase tracking schema created
2. ✅ Phase API endpoints created
3. ✅ New projects auto-initialize with gathering phase
4. ⏳ Create Gathering Agent prompt
5. ⏳ Update chat route to use phase-based agent selection
6. ⏳ Build UI to show phase progress
7. ⏳ Create migration script for existing projects