Complete AI TrainingYourJobSkills for your job

Skills / workflow

requesting-code-review

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

IT & Software DevelopmentProject & Program ManagementOperations & Supply ChainAI & Automation

Requesting Code Review

Dispatch superpowers:code-reviewer subagent to catch issues before they cascade.

Core principle: Review early, review often.

When to Request Review

Mandatory:

  • After each task in subagent-driven development
  • After completing major feature
  • Before merge to main

Optional but valuable:

  • When stuck (fresh perspective)
  • Before refactoring (baseline check)
  • After fixing complex bug

How to Request

1. Get git SHAs:

BASE_SHA=$(git rev-parse HEAD~1)  # or origin/main
HEAD_SHA=$(git rev-parse HEAD)

2. Dispatch code-reviewer subagent:

Use Task tool with superpowers:code-reviewer type, fill template at code-reviewer.md

Placeholders:

  • {WHAT_WAS_IMPLEMENTED} - What you just built
  • {PLAN_OR_REQUIREMENTS} - What it should do
  • {BASE_SHA} - Starting commit
  • {HEAD_SHA} - Ending commit
  • {DESCRIPTION} - Brief summary

3. Act on feedback:

  • Fix Critical issues immediately
  • Fix Important issues before proceeding
  • Note Minor issues for later
  • Push back if reviewer is wrong (with reasoning)

Example

[Just completed Task 2: Add verification function]

You: Let me request code review before proceeding.

BASE_SHA=$(git log --oneline | grep "Task 1" | head -1 | awk '{print $1}')
HEAD_SHA=$(git rev-parse HEAD)

[Dispatch superpowers:code-reviewer subagent]
  WHAT_WAS_IMPLEMENTED: Verification a

Subscribers only

The full skill, its 2 bundled files and every download is included with every paid Complete AI plan.

Details

Sourcecommunity
License
Risk labelcritical ("critical" means the skill may run commands or touch files — read before use)
FilesSKILL.md, code-reviewer.md
Added2026-02-27

Related skills

acceptance-orchestrator

Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

address-github-comments

Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.

ai-loop

Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.

airflow-dag-patterns

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

antigravity-workflows

Use when asked to ship a SaaS MVP, audit application security, build an AI agent, run browser QA, or design a domain model with multiple skills and verified checkpoints.

ask-questions-if-underspecified

Clarify requirements before implementing. Use when serious doubts arise.