multi-model-meta-analysis

name

multi-model-meta-analysis

description

Synthesize outputs from multiple AI models into a comprehensive, verified assessment. Use when: (1) User pastes feedback/analysis from multiple LLMs (Claude, GPT, Gemini, etc.) about code or a project, (2) User wants to consolidate model outputs into a single reliable document, (3) User needs conflicting model claims resolved against actual source code. This skill verifies model claims against the codebase, resolves contradictions with evidence, and produces a more reliable assessment than any single model.

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 verification wasn't possible
• Preserves valuable insights that don't require verification (e.g., design suggestions)

Output Format

# Synthesized Assessment: [Topic]

## Summary
[2-3 sentences describing the verified findings]

## Verified Findings

### Confirmed Issues
| Issue | Severity | Evidence | Models |
|-------|----------|----------|--------|
| [Issue] | High/Med/Low | [file:line or quote] | Claude, GPT |

### Refuted Claims
| Claim | Source | Reality |
|-------|--------|---------|
| [What model said] | GPT-4 | [What code actually shows] |

### Unverifiable Claims
| Claim | Source | Why Unverifiable |
|-------|--------|------------------|
| [Claim] | Claude | [Requires runtime testing / external system / etc.] |

## Consensus Recommendations
[Items where 2+ models agree AND verification supports the suggestion]

## Unique Insights Worth Considering
[Valuable suggestions from single models that weren't contradicted]

## Conflicts Resolved
| Topic | Model A | Model B | Verdict | Evidence |
|-------|---------|---------|---------|----------|
| [Topic] | [Position] | [Position] | [Which is correct] | [Code reference] |

## Action Items

### Critical (Verified, High Impact)
- [ ] [Item] — Evidence: [file:line]

### Important (Verified, Medium Impact)
- [ ] [Item] — Evidence: [file:line]

### Suggested (Unverified but Reasonable)
- [ ] [Item] — Source: [Models]

Verification Guidelines

Always verify:

• Bug reports and security issues
• Claims about what code does or doesn't do
• Assertions about missing functionality
• Performance or complexity claims

Trust but note source:

• Style and readability suggestions
• Architectural recommendations
• Best practice suggestions

Mark as unverifiable:

• Runtime behavior claims (without tests)
• Performance benchmarks (without profiling)
• External API behavior
• User experience claims

Anti-Patterns

• Blindly merging model outputs without checking code
• Treating model consensus as proof (all models can be wrong)
• Omitting refuted claims (document what was wrong - it's valuable)
• Skipping verification because claims "sound right"