multi-model-meta-analysis

# Multi-Model Synthesis

Combine outputs from multiple AI models into a verified, comprehensive assessment by cross-referencing claims against the actual codebase.

## Core Principle

Models hallucinate and contradict each other. The source code is the source of truth. Every significant claim must be verified before inclusion in the final assessment.

## Process

### 1. Extract Claims

Parse each model's output and extract discrete claims:
- Factual assertions about the code ("function X does Y", "there's no error handling in Z")
- Recommendations ("should add validation", "refactor this pattern")
- Identified issues ("bug in line N", "security vulnerability")

Tag each claim with its source model.

### 2. Deduplicate

Group semantically equivalent claims:
- "Lacks input validation" = "No sanitization" = "User input not checked"
- "Should use async/await" = "Convert to promises" = "Make asynchronous"

Create canonical phrasing. Track which models mentioned each.

### 3. Verify Against Source

For each factual claim or identified issue:

```
CLAIM: "The auth middleware doesn't check token expiry"
VERIFY: Read the auth middleware file
FINDING: [Confirmed | Refuted | Partially true | Cannot verify]
EVIDENCE: [Quote relevant code or explain why claim is wrong]
```

Use Grep, Glob, and Read tools to locate and examine relevant code. Do not trust model claims without verification.

### 4. Resolve Conflicts

When models contradict each other:

1. Identify the specific disagreement
2. Examine the actual code
3. Determine which model (if any) is correct
4. Document the resolution with evidence

```
CONFLICT: Model A says "uses SHA-256", Model B says "uses MD5"
INVESTIGATION: Read crypto.js lines 45-60
RESOLUTION: Model B is correct - line 52 shows MD5 usage
EVIDENCE: `const hash = crypto.createHash('md5')`
```

### 5. Synthesize Assessment

Produce a final document that:
- States verified facts (not model opinions)
- Cites evidence for significant claims
- Notes where verifi