superpowers-receiving-code-review
Use when receiving code review feedback - requires technical verification before implementing suggestions, with reasoned pushback when feedback is technically questionable; no performative agreement
Best use case
superpowers-receiving-code-review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when receiving code review feedback - requires technical verification before implementing suggestions, with reasoned pushback when feedback is technically questionable; no performative agreement
Teams using superpowers-receiving-code-review should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/openclaw-receiving-code-review/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How superpowers-receiving-code-review Compares
| Feature / Agent | superpowers-receiving-code-review | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when receiving code review feedback - requires technical verification before implementing suggestions, with reasoned pushback when feedback is technically questionable; no performative agreement
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
Cursor vs Codex for AI Workflows
Compare Cursor and Codex for AI coding workflows, repository assistance, debugging, refactoring, and reusable developer skills.
SKILL.md Source
# Superpowers Receiving Code Review
## 概述
代码审查需要技术评估，不是情感表演。
**核心原则：** 实现前验证。有疑问先问。技术正确性 > 社交舒适度。
## 响应模式
```
收到代码审查反馈时：
1. 读：完整反馈，不要立刻反应
2. 理解：用自己话复述要求（或提问）
3. 验证：对照代码库现实检查
4. 评估：对本代码库在技术上是合理的吗？
5. 响应：技术确认或合理反驳
6. 实现：一次一个，测试每个
```
## 禁止的回应
**永远不要：**
- "你说得对！"（明确违反准则）
- "好观点！" / "优秀的反馈！"（表演性的）
- "我现在来实现"（验证前不要行动）
**改为：**
- 复述技术要求
- 提问澄清
- 有技术理由地反驳
- 直接开始工作（行动 > 言语）
## 处理不清晰的反馈
```
如果任何项不清晰：
停止——暂不实现任何内容
提问澄清
原因：项可能相关。 partial 理解 = 错误实现。
```
## 来源特定处理
### 来自主人
- **可信** — 理解后实现
- **范围不清也问**
- **不要表演性同意**
- **直接行动**或技术确认
### 来自外部审查者
```
实现前检查：
1. 技术上对本代码库正确吗？
2. 破坏现有功能吗？
3. 当前实现的原因是什么？
4. 在所有平台/版本上工作吗？
5. 审查者理解完整上下文吗？
如果建议看起来不对：
用技术理由反驳
如果无法轻易验证：
说出来："没有 X 我无法验证。应该 [调查/提问/继续]？"
```
## YAGNI 检查"专业"功能
```
如果审查者建议"正确实现"某功能：
grep 代码库找实际用法
如果未使用："此端点没有被调用。YAGNI 移除？"
如果使用了：那就正确实现
```
## 实现顺序
```
多项目反馈：
1. 先澄清任何不清晰的项
2. 然后按此顺序实现：
- 阻塞问题（破坏、安全）
- 简单修复（typo、import）
- 复杂修复（重构、逻辑）
3. 每个单独测试
4. 验证无回归
```
## 何时反驳
在以下情况反驳：
- 建议破坏现有功能
- 审查者缺乏完整上下文
- 违反 YAGNI（未使用的功能）
- 对本技术栈在技术上不正确
- 存在遗留/兼容性原因
- 与主人的架构决策冲突
**如何反驳：**
- 用技术理由，不是防御
- 问具体问题
- 引用工作的测试/代码
- 如涉及架构则让主人参与
## 承认正确反馈
当反馈正确时：
```
✅ "已修复。[简要描述变更。]"
✅ "好发现——[具体问题]。已在 [位置] 修复。"
❌ "你说得太对了！"
❌ "好观点！"
❌ "谢谢纠正！"
```
**为什么不说谢谢：** 行动说话。直接修。代码本身表明听到了反馈。
## 常见错误
| 错误 | 修复 |
|------|------|
| 表演性同意 | 陈述要求或直接行动 |
| 未验证就实现 | 先对照代码库检查 |
| 不测试就批量实现 | 一次一个，每个测试 |
| 假设审查者对 | 检查是否破坏东西 |
| 回避反驳 | 技术正确性 > 舒适度 |
| partial 实现 | 澄清所有项后再实现 |
| 无法验证仍继续 | 说明限制，询问方向 |Related Skills
Post-Mortem & Incident Review Framework
Run structured post-mortems that actually prevent repeat failures. Blameless analysis, root cause identification, and action tracking.
Pitch Deck Reviewer
Reviews pitch decks and provides investor-ready feedback with scoring
Performance Review Engine
> Your AI-powered performance management system. Write reviews that develop people, not just evaluate them. From self-assessments to 360° feedback to calibration — complete frameworks for every review cycle.
Deal Desk — Structured Deal Review & Approval
Run every non-standard deal through a repeatable review process. Catch margin leaks, enforce discount guardrails, and close faster with pre-approved terms.
Contract Review Assistant
Analyze business contracts for risks, unfavorable terms, and missing clauses. Get a plain-English summary of what you're signing.
afrexai-code-reviewer
Enterprise-grade code review agent. Reviews PRs, diffs, or code files for security vulnerabilities, performance issues, error handling gaps, architecture smells, and test coverage. Works with any language, any repo, no dependencies required.
performance-review-cn
绩效面谈报告、OKR对齐度检测、校准辅助
clawdtm-review
Review and rate OpenClaw skills on ClawdTM. See what humans and AI agents recommend.
cyber-owasp-review
Map application security findings to OWASP Top 10 categories and generate remediation checklists. Use for normalized AppSec review outputs and category-level prioritization.
Contract Reviewer - AI Legal Document Risk Scanner
Upload any contract or legal document and get a structured risk analysis with flagged clauses, plain-language explanations, and negotiation suggestions.
serde-code-review
Reviews serde serialization code for derive patterns, enum representations, custom implementations, and common serialization bugs. Use when reviewing Rust code that uses serde, serde_json, toml, or any serde-based serialization format. Covers attribute macros, field renaming, and format-specific pitfalls.
rust-testing-code-review
Reviews Rust test code for unit test patterns, integration test structure, async testing, mocking approaches, and property-based testing. Use when reviewing _test.rs files,