ce:review

Automated code review for a pull request is achieved by spawning multiple AI persona agents to analyze the diff, which then merge and deduplicate findings into a single structured report. You simply provide a PR URL, branch name, or standalone diff to trigger the workflow.

Multi-agent code review orchestration coordinates always-on reviewer personas and AI agents to evaluate correctness, testing, and maintainability across a diff. It performs intent discovery, plan verification, and cross-agent synthesis to produce a unified findings set with a synthesized verdict.

Yes, you can apply automated code review to standalone branch diffs or individual changes. The multi-agent workflow processes any provided diff, computing changes and generating a standardized findings report prior to PR creation.

The best way to structure code review findings is through a standardized schema that merges and deduplicates results from multiple agents. This approach provides confidence-gated findings and a synthesized verdict to guide PR decisions.

Yes, multi-agent code review checks project standards compliance, maintainability, correctness, and testing. It spawns dedicated reviewer personas to cover each area and synthesizes their findings into a single report.

When orchestrating AI agents for code review, you should expect findings to be gated by confidence levels and require cross-agent synthesis to deduplicate results. The workflow relies on a defined schema and frontmatter-defined Skill Units to produce a final synthesized verdict.