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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.
Aggregate many LLMs for second opinions, arbitration, and more reliable decisions.
Automate pull request reviews with multi-LLM feedback across GitHub and Bitbucket.
Query multiple AI models for code reviews, debates, and diverse perspectives.
Connect Perplexity search to Claude Code for cited answers, research, and reviews.
Use xAI Grok in MCP hosts for code review and PR quality gates.
Run parallel multi-model code reviews and get one consensus summary.