increment-quality-judge-v2

# Increment Quality Judge v2.0

**LLM-as-Judge Pattern Implementation**

AI-powered quality assessment using the **LLM-as-Judge** pattern - an established AI/ML evaluation technique where an LLM evaluates outputs with chain-of-thought reasoning, BMAD-pattern risk scoring, and formal quality gate decisions (PASS/CONCERNS/FAIL).

## LLM-as-Judge: What It Is

**LLM-as-Judge (LaaJ)** is a recognized pattern in AI/ML evaluation where a large language model assesses quality using structured reasoning.

```
┌─────────────────────────────────────────────────────────────┐
│                 LLM-as-Judge Pattern                        │
├─────────────────────────────────────────────────────────────┤
│  Input:  spec.md, plan.md, tasks.md                        │
│                                                             │
│  Process:                                                   │
│  ┌─────────────────────────────────────────────────────┐   │
│  │ <thinking>                                          │   │
│  │   1. Read and understand the specification          │   │
│  │   2. Evaluate against 7 quality dimensions          │   │
│  │   3. Identify risks (P×I scoring)                   │   │
│  │   4. Form evidence-based verdict                    │   │
│  │ </thinking>                                         │   │
│  └─────────────────────────────────────────────────────┘   │
│                                                             │
│  Output: Structured verdict with:                          │
│  • Dimension scores (0-100)                                │
│  • Risk assessment (CRITICAL/HIGH/MEDIUM/LOW)              │
│  • Quality gate decision (PASS/CONCERNS/FAIL)              │
│  • Actionable recommendations                              │
└─────────────────────────────────────────────────────────────┘
```

**Why LLM-as-Judge works:**
- **Consistency**: Uniform evaluation criteria without human fatigue
- **Reasoning**: Chain-of-thought explains WHY something is an