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.
npx skills add majiayu000/claude-skill-registry --skill multi-model-meta-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Core Principle
- Process
- 1. Extract Claims
- 2. Deduplicate
- 3. Verify Against Source
- 4. Resolve Conflicts
- 5. Synthesize Assessment
- Output Format
- Verification Guidelines
- Anti-Patterns
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 it?
Run `npx skills add majiayu000/claude-skill-registry --skill multi-model-meta-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.