multi-model-meta-analysis
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/petekp/agent-skills --skill multi-model-meta-analysis- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads multi-model-meta-analysis automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
- Identify the specific disagreement
- Examine the actual code
- Determine which model (if any) is correct
- 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"
FAQ
What does the multi-model-meta-analysis skill do?
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.
How do I install multi-model-meta-analysis?
Run npx skills add https://github.com/petekp/agent-skills --skill multi-model-meta-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/multi-model-meta-analysis/ (personal) or .claude/skills/multi-model-meta-analysis/ (project).
Which AI agents can use multi-model-meta-analysis?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is multi-model-meta-analysis safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains multi-model-meta-analysis?
It is published by petekp in petekp/agent-skills. It has 49 recorded installs.
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