Requesting code review
Skill adriannoes/awesome-agentic-ai/cursor-claude-codex/skills/requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirementsFrom its SKILL.md
npx -y skills add adriannoes/awesome-agentic-ai --skill requesting-code-reviewAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
2.8 KB, 631 tokens by cl100k_base, as published. Nobody here has run it
Requesting Code Review
Dispatch a code reviewer subagent to catch issues before they cascade. The reviewer gets precisely crafted context for evaluation — never your session's history. This keeps the reviewer focused on the work product, not your thought process, and preserves your own context for continued work.
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:
Dispatch a general-purpose subagent, filling the template at code-reviewer.md
Placeholders:
{DESCRIPTION}- Brief summary of what you built{PLAN_OR_REQUIREMENTS}- What it should do{BASE_SHA}- Starting commit{HEAD_SHA}- Ending commit
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 code reviewer subagent]
DESCRIPTION: Added verifyIndex() and repairIndex() with 4 issue types
PLAN_OR_REQUIREMENTS: Task 2 from docs/superpowers/plans/deployment-plan.md
BASE_SHA: a7981ec
HEAD_SHA: 3df7661
[Subagent returns]:
Strengths: Clean architecture, real tests
Issues:
Important: Missing progress indicators
Minor: Magic number (100) for reporting interval
Assessment: Ready to proceed
You: [Fix progress indicators]
[Continue to Task 3]
Integration with Workflows
Subagent-Driven Development:
- Review after EACH task
- Catch issues before they compound
- Fix before moving to next task
Executing Plans:
- Review after each task or at natural checkpoints
- Get feedback, apply, continue
Ad-Hoc Development:
- Review before merge
- Review when stuck
Red Flags
Never:
- Skip review because "it's simple"
- Ignore Critical issues
- Proceed with unfixed Important issues
- Argue with valid technical feedback
If reviewer wrong:
- Push back with technical reasoning
- Show code/tests that prove it works
- Request clarification
See template at: code-reviewer.md
What ships with it: 1 file
5.1 KB alongside SKILL.md
- code-reviewer.md5.1 KB
Gives 4 of the 12 instructions most code review skills give in 631 tokens
Counted across 668 of the 814 authors here whose files we hold, read 2026-09-06
- Provide technical reasoning when pushing backin 84 of 668, across 70 files
- Fix critical issues immediatelyhere, and in 77 of 668, across 60 files
- Dispatch a code reviewer subagenthere, and in 76 of 668, across 59 files
- Fix important issues before proceedinghere, and in 73 of 668, across 56 files
- Ask for clarification on unclear itemsin 68 of 668, across 56 files
- Verify feedback against codebase before implementationin 66 of 668, across 55 files
- Implement fixes one at a timein 64 of 668, across 53 files
- Test each fix individuallyin 62 of 668, across 51 files
- Restate technical requirements in own wordsin 57 of 668, across 46 files
- Reply to inline comments in the specific threadin 51 of 668, across 40 files
- Note minor issues for laterhere, and in 49 of 668, across 34 files
- Group findings by severityin 48 of 668, across 47 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.