Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Dispatch code-reviewer agent for two-stage review. Use after completing implementation tasks.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Dispatch code-reviewer subagent to catch issues before they cascade.
Core principle: Review early, review often.
Mandatory:
Optional but valuable:
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 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 summary3. Act on feedback:
[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]
WHAT_WAS_IMPLEMENTED: Verification and repair functions for conversation index
PLAN_OR_REQUIREMENTS: Task 2 from docs/plans/deployment-plan.md
BASE_SHA: a7981ec
HEAD_SHA: 3df7661
DESCRIPTION: Added verifyIndex() and repairIndex() with 4 issue types
[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]
Task({
task_id: 'task-1',
subagent_type: 'general-purpose',
model: 'sonnet',
description: 'Code review for {DESCRIPTION}',
prompt: `You are the CODE-REVIEWER agent.
## Instructions
1. Read your agent definition: .claude/agents/specialized/code-reviewer.md
2. Read memory: .claude/context/memory/learnings.md
## Review Request
### What Was Implemented
{WHAT_WAS_IMPLEMENTED}
### Requirements/Plan
{PLAN_OR_REQUIREMENTS}
### Git Range to Review
**Base:** {BASE_SHA}
**Head:** {HEAD_SHA}
Run these commands to see the changes:
\`\`\`bash
git diff --stat {BASE_SHA}..{HEAD_SHA}
git diff {BASE_SHA}..{HEAD_SHA}
\`\`\`
## Memory Protocol
Record findings to .claude/context/memory/learnings.md when done.
`,
});
Subagent-Driven Development:
Executing Plans:
Ad-Hoc Development:
Never:
If reviewer wrong:
See template at: requesting-code-review/code-reviewer.md
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Skipping review for "small" changes | Small changes introduce the same classes of bugs as large ones; "simple" is subjective and unreliable | Request review at every mandatory checkpoint regardless of perceived complexity |
| Dispatching reviewer without git SHAs | Reviewer cannot produce an accurate diff without a commit range; the review is inaccurate or incomplete | Capture BASE_SHA and HEAD_SHA before every review dispatch |
| Proceeding past Critical issues | Critical issues compound; later tasks build on broken foundations that are expensive to fix retroactively | Fix all Critical issues before advancing to the next task |
| Treating reviewer feedback as optional | Optional review degrades code quality over time and compounds technical debt that can't be traced | Follow severity escalation: Critical → fix now, Important → fix before next task, Minor → note for later |
| Requesting review after batches of tasks instead of after each task | Errors from Task 1 contaminate Tasks 2–N; reviewers cannot isolate which task introduced which issue | Review after each individual task before starting the next one |
Before starting:
Read .claude/context/memory/learnings.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: If it's not in memory, it didn't happen.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
日本語の概要は準備中です。原文の説明を表示しています。
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
日本語の概要は準備中です。原文の説明を表示しています。