Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
中文简介正在准备中。当前显示原文说明。
Dispatch code-reviewer agent for two-stage review. Use after completing implementation tasks.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
中文简介正在准备中。当前显示原文说明。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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
中文简介正在准备中。当前显示原文说明。