Can I push back if the AI reviewer is wrong?

Yes, the workflow encourages technical push-back. If the reviewer is incorrect, provide technical reasoning or evidence from tests to justify your implementation.

How does the subagent know what to review?

The subagent uses Git SHAs (BASE_SHA and HEAD_SHA) to analyze specific code changes and compares them against your provided plan or requirements document.

What kind of issues does the AI reviewer identify?

The reviewer categorizes findings into Critical issues that must be fixed immediately, Important issues to address before proceeding, and Minor issues for future cleanup.

When is the best time to request a review?

It is recommended to request a review after completing each task in a plan, after a major feature implementation, or as a final check before merging into your main branch.

AI Code Review Subagent

Name: AI Code Review Subagent
Author: apexbusiness-systems

byapexbusiness-systems

Collaboration Tools

Dispatches an automated subagent to review code implementations against project requirements and catch potential issues before they cascade.

About

The AI Code Review Subagent skill streamlines the development lifecycle by automating the review process within the Claude Code environment. By comparing current progress against specific plans and requirements using Git SHAs, it identifies critical bugs, architectural inconsistencies, and missing features before they impact the main codebase. This skill is particularly effective in subagent-driven development, ensuring that every task is verified against the original intent, which prevents technical debt and reduces the need for manual oversight during complex implementations.

Key Features

0 GitHub stars
Categorized feedback loops for Critical, Important, and Minor issues
Automated subagent dispatch for deep code analysis
Seamless integration with multi-step subagent-driven development workflows
Context-aware review against project plans and requirements
Differential analysis using Git SHAs to isolate changes

Use Cases

Verifying task completion in multi-step feature implementations
Gaining a fresh technical perspective when stuck on a complex refactor
Identifying logic errors or edge cases before merging to the main branch

About

Key Features

0 GitHub stars
Categorized feedback loops for Critical, Important, and Minor issues
Automated subagent dispatch for deep code analysis
Seamless integration with multi-step subagent-driven development workflows
Context-aware review against project plans and requirements
Differential analysis using Git SHAs to isolate changes

Use Cases

Verifying task completion in multi-step feature implementations
Gaining a fresh technical perspective when stuck on a complex refactor
Identifying logic errors or edge cases before merging to the main branch