cross-model-peer-review
OfficialGet a robust second opinion on model outputs.
Authorm2ai-portfolio
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Cross-model peer review solves the problem of unreliable self-assessment by using a second model to validate outputs.
Core Features & Use Cases
- Structured rubric (4-6 dimensions) for evaluating outputs (factual accuracy, logical coherence, completeness, calibration, internal consistency).
- Phase-driven workflow: define target, build rubric, construct reviewer prompt, run review, delta analysis, and reporting.
- Use case: validate a complex analysis produced by Model A by having Model B critically review it before deployment.
Quick Start
Run a cross-model peer review on the latest output using the defined rubric to generate an evaluation report.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: cross-model-peer-review Download link: https://github.com/m2ai-portfolio/m2ai-skills-pack/archive/main.zip#cross-model-peer-review Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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