Agent skill

requesting-code-review

Use when completing tasks, implementing major features, or before merging to verify work meets requirements. Dispatches three independent reviewers in parallel.

Stars 25
Forks 5

Install this agent skill to your Project

npx add-skill https://github.com/leegonzales/AISkills/tree/main/RequestingCodeReview/requesting-code-review

SKILL.md

Requesting Code Review

Dispatch three independent reviewers in parallel for multi-perspective code review.

Core principle: Three perspectives catch more than one. Dispatch all reviewers simultaneously.

Reviewers

Reviewer Skill/Tool Strengths
Claude subagent superpowers:code-reviewer Task tool Deep reasoning, plan compliance
Codex CLI codex review --base <SHA> Line-level bugs, concise output
Gemini CLI cat <<'EOF' | gemini Holistic view, large context (1M tokens)

When to Request Review

Mandatory: After each task in subagent-driven development, after major features, before merge to main.

Optional: When stuck, before refactoring, after fixing complex bugs.

The Process

Step 1: Get git SHAs

bash
BASE_SHA=$(git rev-parse HEAD~N)  # or origin/main, or specific commit
HEAD_SHA=$(git rev-parse HEAD)

Step 2: Dispatch ALL THREE reviewers in parallel

All three run simultaneously. Use parallel tool calls.

2a. Claude subagent

Use Task tool with superpowers:code-reviewer type. Fill the template from the marketplace skill's code-reviewer.md with:

  • {WHAT_WAS_IMPLEMENTED} - What you built
  • {PLAN_OR_REQUIREMENTS} - What it should do
  • {BASE_SHA} / {HEAD_SHA} - Git range
  • {DESCRIPTION} - Brief summary

2b. Codex CLI

bash
codex review --base $BASE_SHA

This is Codex's built-in code review command. It automatically diffs from BASE_SHA to HEAD and provides line-level feedback.

2c. Gemini CLI

bash
DIFF=$(git diff $BASE_SHA..$HEAD_SHA)
cat <<EOF | gemini
Code review request.

CONTEXT: [what was implemented and why]

REQUIREMENTS: [plan or spec reference]

DIFF:
$DIFF

Review for: code quality, architecture, error handling, security, testing gaps.
Categorize issues as Critical / Important / Minor with file:line references.
EOF

Step 3: Synthesize findings

After all three return, consolidate into a single report:

  1. Agreement — issues flagged by 2+ reviewers are high-confidence
  2. Unique findings — issues only one reviewer caught (still valid)
  3. Disagreements — where reviewers conflict, evaluate technically
  4. Severity — use the highest severity assigned by any reviewer

Step 4: 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)

Quick Reference

Step Action
Get SHAs git rev-parse HEAD~N / git rev-parse HEAD
Claude Task tool with superpowers:code-reviewer
Codex codex review --base $BASE_SHA
Gemini Pipe diff + context to gemini via heredoc
Synthesize Consolidate all three, weight by agreement

Integration with Workflows

Subagent-Driven Development: Review after EACH task. All three reviewers, every time.

Executing Plans: Review after each batch (3 tasks).

Ad-Hoc Development: Review before merge.

Red Flags

Never:

  • Skip review because "it's simple"
  • Use only one reviewer when all three are available
  • Ignore Critical issues from any reviewer
  • Proceed with unfixed Important issues

If a reviewer is unavailable (CLI not installed, API quota):

  • Note which reviewer was skipped
  • Proceed with remaining reviewers
  • Do NOT skip review entirely

Expand your agent's capabilities with these related and highly-rated skills.

leegonzales/AISkills

context-continuity

High-fidelity context transfer protocol for moving conversations between AI agents. Preserves decision tempo, open loops, and critical context with graceful degradation. Use when the user says "transfer," "handoff," "continue this in another chat," or needs to work around context window limits. Produces structured artifacts (Minimal ~200 words, Full ~1000 words). DO NOT trigger on simple "summarize our conversation" requests—only when transfer intent is explicit.

25 5
Explore
leegonzales/AISkills

codex-peer-review

25 5
Explore
leegonzales/AISkills

silicon-doppelganger

Build psychometrically accurate personal proxy agents for the PAIRL Conductor system. Extracts personality, decision heuristics, and values into portable schemas that enable AI agents to negotiate, filter, and act on a principal's behalf.

25 5
Explore
leegonzales/AISkills

fabric-patterns

Run danielmiessler/fabric CLI patterns for content analysis, extraction, summarization, writing, security analysis, and more. Use when user asks to "use fabric," "run a pattern," "extract wisdom," "summarize with fabric," or when piping content through AI patterns would be more effective than inline processing. Triggers include "fabric," "pattern," "extract wisdom," "summarize this article," "analyze this threat report," or any reference to a specific fabric pattern name.

25 5
Explore
leegonzales/AISkills

moltbook-enclave

Secure, air-gapped interface for Moltbook (social network for AI agents). Isolates untrusted external content from your main agent's memory and context.

25 5
Explore
leegonzales/AISkills

sand-table

Design, scaffold, extract, and validate Sand Table simulations and event streams across domains. Meta skill that knows the protocol and all existing implementations.

25 5
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results