Orchestrate parallel multi-LLM reviews and synthesize consensus reports.
npx skills add https://github.com/coreyhulen/enviroment --skill multi-review
Please help me install this Agent Skill. Skill: multi-review Source: https://github.com/coreyhulen/enviroment/tree/main/claude-init/skills/multi-review Command: npx skills add https://github.com/coreyhulen/enviroment --skill multi-review
This Skill streamlines the review process for both code quality and architectural decisions by leveraging the consensus of multiple Large Language Models, ensuring more robust and well-rounded feedback.
/multi-review on the file to get a comprehensive review from multiple AI models. For a new feature, use /multi-review --arch to evaluate different design choices.Use the multi-review skill to analyze the code file server/app/item_core.go.
High-intent search queries and answers about installing and using this skill.
Multi-LLM code review analyzes your files by orchestrating parallel execution of multiple LLMs. It synthesizes their feedback into structured reports highlighting consensus points, unique findings, and disagreements for robust pre-merge evaluation.
Yes, you can evaluate software architecture decisions by running the tool with architecture flags on textual decisions. It weighs design options, trade-offs, and risks by aggregating feedback from various LLMs into a synthesized review report.
Multi-LLM consensus synthesis works by orchestrating parallel execution of multiple AI models to analyze code or architecture. It aggregates their feedback into structured reports that highlight common consensus points and unique disagreements.
No, you do not need to specify flags because the tool auto-detects the appropriate review mode for code or architecture. You can optionally pass explicit flags like --arch or --code to manually guide the analysis process.
The best way to find security vulnerabilities and bugs is to perform a multi-LLM code review on the file. This analyzes code for bugs, security vulnerabilities, performance issues, and adherence to best practices using consensus from multiple AI models.
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