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retsohuang

code-review

by retsohuang

Custom marketplace for Claude Code plugins that fully customizable.

1🍴 0📅 2026年1月5日
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SKILL.md


name: code-review description: Review commits from a starting commit hash to HEAD. Use when user says "review commits from ", "code review starting from ", "review changes since ", or provides a commit hash for review. Detects AI slop, component issues, and missed reuse opportunities. license: MIT allowed-tools: Read Grep Glob Bash metadata: author: Retso Huang version: "1.0"

Code Review

Review changes from a starting commit hash to HEAD, analyzing each commit individually like a human code reviewer would on a merge request.

Usage

Provide a commit hash to start the review from. The review will analyze all commits from that hash to HEAD.

Review Principles

Follow Google's Code Review Standards:

  • Design: Well-designed and appropriate for the system?
  • Functionality: Behaves as intended? Good for users?
  • Complexity: Can others understand and use this easily?
  • Tests: Correct and well-designed automated tests?
  • Naming: Clear variable, function, class names?
  • Comments: Clear, useful, explain why not what?
  • Style: Follows style guidelines?

Philosophy: Code should improve overall code health, even if not perfect. Balance forward progress with quality.

Important Guidelines

  • Big picture first: Understand the commit's purpose before examining specific code
  • Context matters: A line change only makes sense when you understand why it changed
  • Cross-file awareness: Changes in one file often relate to changes in others
  • Look for what's missing: Sometimes the issue is what WASN'T changed
  • Be specific: Reference exact line numbers and file paths
  • Be actionable: Each issue should have clear description and optional suggestion
  • No false positives: Only flag genuine issues matching the rules

Workflow

Step 1: Prepare Review Data

Run the CLI to collect commit metadata:

python3 ./scripts/cli.py prepare "<provided-commit-hash>"

Output is in TOON format (Token-Oriented Object Notation) for efficient LLM processing:

commits[2]{hash,author,date,subject,body,filesChanged}:
  abc1234...,Alice,2025-01-15,Add new feature,null,3
  def5678...,Bob,2025-01-14,Fix bug in parser,null,1
commitList[2]: abc1234...,def5678...
branch: feature-branch
commitRange: abc123^..HEAD
totalCommits: 2

Parse the commits array from the CLI output and display a table:

#CommitMessageFiles
1abc1234Add new feature3
2def5678Fix bug in parser1

Use AskUserQuestion to ask which commits to review:

  • Option 1: "Review all" - review all commits in the table
  • Option 2: Free input for specific indices (e.g., "1,3,5" or "2-4")

Step 2: Load Review Rules

Read all rules from references/:

Step 3: Review Each Commit

For each commit in commitList:

  1. Get the diff using python3 ./scripts/cli.py get-diff <commit-hash>
  2. Apply the loaded rules to the changes
  3. Record issues found

Issue Structure:

issues[2]{file,startLine,endLine,category,icon,severity,description,suggestion}:
  path/to/file.tsx,42,45,AI Slop,🧹,medium,Brief actionable description,Remove unnecessary comment
  src/utils/helper.ts,18,22,Component Reuse,♻️,low,Existing utility available,Use shared/utils

Severity Guidelines:

  • high: Bugs, security issues, broken functionality
  • medium: Design problems, complexity issues, missing tests
  • low: Style issues, suggestions, minor improvements

Step 4: Aggregate and Deduplicate

After reviewing all commits:

  1. Deduplicate issues (same file + line range + category)
  2. Calculate totals by category and severity

Step 5: Generate Summary

Read assets/summary-template.md and fill in the values:

VariableDescription
{commitRange}Git commit range (e.g., abc123^..HEAD)
{commits}Total number of commits reviewed
{files}Total number of files changed
{issues}Total number of issues found
{humanReviewTime}Estimated human review time (2 min per 100 lines)
{dedupLine}Deduplication info or empty string
{topIssues}List of top issue categories with counts
{recommendations}List of key actionable recommendations

Output the formatted summary.

スコア

総合スコア

50/100

リポジトリの品質指標に基づく評価

SKILL.md

SKILL.mdファイルが含まれている

+20
LICENSE

ライセンスが設定されている

0/10
説明文

100文字以上の説明がある

0/10
人気

GitHub Stars 100以上

0/15
最近の活動

3ヶ月以内に更新がある

0/10
フォーク

10回以上フォークされている

0/5
Issue管理

オープンIssueが50未満

+5
言語

プログラミング言語が設定されている

+5
タグ

1つ以上のタグが設定されている

0/5

レビュー

💬

レビュー機能は近日公開予定です