ce:review

Coordinate multi-agent code review workflows and generate structured findings.

Updated Apr 22, 2026
One-click install
npx skills add https://github.com/yxc023/agent-config-studio --skill ce-review-yxc023
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ce:review
Source: https://github.com/yxc023/agent-config-studio/tree/main/.opencode/skills/ce-review
Command: npx skills add https://github.com/yxc023/agent-config-studio --skill ce-review-yxc023

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

ce:review orchestrates multi-agent code reviews by coordinating tiered personas to analyze diffs, produce a structured findings report, and drive a merge/dedup pipeline with run artifacts for downstream workflows.

Core Features & Use Cases

  • Always-on reviewers: correctness, testing, maintainability, and project-standards, plus CE agents agent-native-reviewer and learnings-researcher.
  • Conditional reviewers: security, performance, api-contract, data-migrations, reliability, adversarial, cli-readiness, and previous-comments, selected based on diff content.
  • Structured outputs: findings conform to a formal schema with residual risks, testing gaps, and clear ownership/auto-fix routing.
  • Run artifacts and headless outputs: supports automated pipelines with per-run directories and JSON artifacts.
  • Standalone and PR reviews: intent discovery, plan discovery, and staged synthesis integrated into the compound-engineering workflow.

Quick Start

Run a ce:review session on a changed codebase to generate a structured report and associated run artifacts.

Frequently Asked Questions about ce:review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate structured code reviews for diffs in a CI pipeline?▼

Automated code review routes diff findings through tiered agents to generate structured reports. It classifies results into safe_auto, gated_auto, manual, and advisory categories, producing JSON artifacts for downstream pipeline consumption.

What is the best way to run multi-agent code reviews with specialized tiered personas?▼

Multi-agent code review coordinates always-on reviewers for correctness and testing alongside conditional agents for security and performance. This routes findings through an autofix pipeline, creating actionable next steps based on diff content.

Can I use automated code review for both standalone analysis and pull requests?▼

Automated code review supports both standalone and pull request workflows. It integrates intent discovery, plan discovery, and staged synthesis to produce structured findings with clear ownership and auto-fix routing.

How does multi-agent code review classify and route findings for autofix pipelines?▼

Multi-agent code review classifies findings into safe_auto, gated_auto, manual, and advisory tiers. This structured routing ensures residual risks and testing gaps are addressed with clear ownership for automated remediation.

Do I need to provide a run_id to generate artifacts during automated code review?▼

Providing a run_id generates per-run artifacts and headless JSON outputs. While the structured findings report is always produced, the run_id enables integration with automated compound-engineering workflows.

What limitations exist when selecting conditional reviewers for specific diff content?▼

Conditional reviewers for security, performance, api-contract, data-migrations, reliability, adversarial, cli-readiness, and previous-comments are selected based on diff content. The selection is automatic, limiting manual override of the reviewer composition.