$ loading_
Run cross-reviews across leading LLMs with convergence gates for more reliable outputs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "cross-review" yet — see the docs or source repo.
Submit the following Git diff to Claude, ChatGPT Codex, Gemini, DeepSeek, Grok, and Perplexity for cross-review. Focus on bugs, edge cases, security risks, and maintainability. Only return final recommendations after the models converge, and summarize findings by severity. Code: <paste diff>
A consolidated review report with consensus issues, disagreements, severity levels, and final fix recommendations.
Cross-evaluate this system design with multiple models. Compare architectural soundness, scalability, cost, risks, and alternatives. Only provide the final conclusion and improvements after the models meet the convergence threshold. Proposal: <paste design>
A cross-validated design assessment covering strengths, weaknesses, key risks, alternatives, and a recommended decision.
Have multiple LLMs independently review the following research summary and evidence. Check for reasoning gaps, factual inconsistencies, overclaims, and missing perspectives. Set a convergence gate and only output the final review once conclusions align. सामग्री: <paste content>
A more reliable research review listing consensus issues, unresolved disagreements, and revision suggestions.
Automate pull request reviews with multi-LLM feedback across GitHub and Bitbucket.
Query multiple AI models for code reviews, debates, and diverse perspectives.
Run multi-model code reviews to surface bugs, security risks, and improvements.
Run parallel multi-model code reviews and get one consensus summary.
Runs dual-model code reviews and synthesizes one unified report.
Provide code review, fixes, testing, and refactoring help in Claude Code.