Predictive Intelligence

Phase 0.15: Proactive guidance that prevents problems before they occur.

The Concept

Predictive Intelligence adds a layer between prompt perfection (Phase 0) and execution. It analyzes your task to:

• Detect your current journey stage
• Identify domain-specific risks
• Recognize project patterns
• Map relationships to previous work
• Predict logical next steps

How It Works

Phase 0 Complete
      │
      ▼
┌─────────────────────────────────────────────┐
│           Phase 0.15: Predictive            │
│                                             │
│  1. Journey Stage Detection                 │
│     └─ Where are you in the workflow?       │
│                                             │
│  2. Domain Risk Analysis                    │
│     └─ What could go wrong?                 │
│                                             │
│  3. Pattern Recognition                     │
│     └─ What conventions exist?              │
│                                             │
│  4. Relationship Mapping                    │
│     └─ What connects to this work?          │
│                                             │
│  5. Next Steps Prediction                   │
│     └─ What will you need next?             │
└─────────────────────────────────────────────┘
      │
      ▼
Execution Phase

Journey Stages

The system detects which stage you're in:

Stage	Description	Indicators
Exploring	Understanding the codebase	"how does", "where is", "what does"
Planning	Designing the approach	"should we", "best way to", "architecture"
Implementing	Writing new code	"add", "create", "implement"
Debugging	Fixing issues	"fix", "error", "not working", "broken"
Refactoring	Improving existing code	"refactor", "clean up", "restructure"
Reviewing	Evaluating changes	"review", "check", "verify", "test"

Stage-Specific Guidance

Journey Stage: Implementing
You're adding a new feature to an existing codebase.

Context:
- 3 related files found in previous session
- Similar feature implemented last week (auth)
- Testing pattern: Jest with fixtures

Domain Risk Analysis

Automatic warnings for sensitive domains:

Security Domain

Triggered by: authentication, authorization, passwords, tokens, encryption

⚠️ Security Domain Detected

- Ensure password hashing (bcrypt, cost 12+)
- Add rate limiting to prevent brute force
- Use HttpOnly cookies for tokens
- Validate and sanitize all inputs
- Implement proper session management
- Log authentication events for audit

Payment Domain

Triggered by: payment, checkout, billing, subscription, credit card

⚠️ Payment Domain Detected

- Never store raw credit card numbers
- Use PCI-compliant payment processor
- Implement idempotency for transactions
- Add proper error handling for failures
- Test thoroughly in sandbox environment
- Log all transaction events

Database Domain

Triggered by: migration, schema, delete, truncate, production

⚠️ Database Domain Detected

- Always backup before migrations
- Test migrations on staging first
- Use transactions for multi-step operations
- Have rollback strategy ready
- Verify constraints and indexes

API Domain

Triggered by: api, endpoint, public, external, webhook

⚠️ API Domain Detected

- Implement authentication/authorization
- Add rate limiting
- Validate all input parameters
- Use proper HTTP status codes
- Document the API
- Version your endpoints

Pattern Recognition

Automatically detects and adapts to your codebase conventions:

Detected Patterns

Pattern Recognition:

Code Style:
- Your codebase uses async/await consistently
- Error handling follows ErrorResponse pattern
- Naming: camelCase for functions, PascalCase for classes

File Organization:
- Components in src/components/
- Services in src/services/
- Tests in __tests__/ folders

Dependencies:
- Express.js 4.18 for API
- Jest for testing
- Prisma for ORM

Pattern Application

The system uses detected patterns to:

1. Match code style - Generated code follows existing conventions
2. Place files correctly - New files go in appropriate directories
3. Use existing utilities - Leverage what's already there
4. Follow testing patterns - Tests match existing structure

Relationship Mapping

Connects current work to previous context:

Relationship Mapping:

Related Previous Work:
- Session 2026-01-08: Implemented user model
- Session 2026-01-09: Added authentication helpers
- Pending: Password reset feature

Dependent Components:
- UserService depends on this
- AuthMiddleware will use this
- ProfileController relates to this

Suggested Review:
- src/services/user.service.js (modified recently)
- src/middleware/auth.js (created by you)

Next Steps Prediction

Forecasts what you'll likely need next:

Next Steps Prediction:

After implementing authentication, you'll likely need:

1. Protected Route Middleware
   ├─ Verify JWT on protected endpoints
   ├─ Attach user to request
   └─ Handle unauthorized access

2. Role-Based Access Control
   ├─ Define user roles
   ├─ Create permission checks
   └─ Restrict admin routes

3. Password Reset Flow
   ├─ Email verification
   ├─ Reset token generation
   └─ Secure token validation

Scope Options

Choose how much to tackle:

Scope	Description	Includes
Focused	Just the core task	Main feature only
Balanced	Core + essential adjacent	Feature + critical dependencies
Comprehensive	Full implementation	Feature + all related items

Configuration

predictive-intelligence.json

{
  "enabled": true,
  "journey_detection": {
    "enabled": true,
    "stages": ["exploring", "planning", "implementing", "debugging", "refactoring", "reviewing"]
  },
  "domain_warnings": {
    "enabled": true,
    "domains": {
      "security": {
        "triggers": ["auth", "password", "token", "encryption"],
        "severity": "high"
      },
      "payment": {
        "triggers": ["payment", "billing", "checkout"],
        "severity": "critical"
      }
    }
  },
  "pattern_recognition": {
    "enabled": true,
    "scan_depth": 3,
    "cache_duration": "24h"
  },
  "relationship_mapping": {
    "enabled": true,
    "max_related": 5
  },
  "next_steps": {
    "enabled": true,
    "scope_options": true
  }
}

Integration

With Hybrid Intelligence

Predictive Intelligence works alongside complexity detection:

Prompt Received
      │
      ▼
┌─────────────┐     ┌─────────────┐
│ Complexity  │────▶│  Agent      │
│ Detection   │     │  Spawning   │
└─────────────┘     └─────────────┘
      │                   │
      ▼                   ▼
┌─────────────────────────────────┐
│    Predictive Intelligence      │
│    (uses agent context)         │
└─────────────────────────────────┘

With Learning System

Predictions improve over time:

• Tracks prediction accuracy
• Adjusts weights based on outcomes
• Learns user-specific patterns

Best Practices

Trust the Warnings

Domain-specific warnings exist for a reason. Even experienced developers benefit from security reminders.

Use Relationship Context

When the system identifies related work, review it. The connections often reveal important context.

Choose Appropriate Scope

• Focused for quick fixes
• Balanced for most features
• Comprehensive for new systems

Provide Feedback

Mark predictions as helpful or not. This trains the system for better future predictions.

Related

• Hybrid Intelligence - Complexity detection
• Learning System - Pattern tracking
• Phase 0 - Prompt perfection