superpowers-zh is a Chinese community edition of superpowers, a collection of practical skills and development methods for AI coding tools. It helps users apply workflows such as brainstorming, test-driven development, debugging, code review, and other programming tasks across supported coding agents. The catalogue add-ons are the project's translated and original skills, instructions, hook, and plugin components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jnmetacode/superpowers-zh/receiving-code-reviewnpx skills add jnMetaCode/superpowers-zh --skill receiving-code-reviewgit clone --depth 1 https://github.com/jnMetaCode/superpowers-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jnmetacode/superpowers-zh/receiving-code-review)<a href="https://agentmods.dev/skills/jnmetacode/superpowers-zh/receiving-code-review"><img src="https://agentmods.dev/badge/skills/jnmetacode/superpowers-zh/receiving-code-review.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.01891 |
| Opus 5 | $0.00025 | $0.00945 |
| Sonnet 5 | $0.00010 | $0.00378 |
| Haiku 4.5 | $0.00005 | $0.00189 |
Grade A, and why
receiving-code-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
接收代码审查
概述
代码审查需要的是技术评估,不是情绪表演。
核心原则: 先验证再实施。先提问再假设。技术正确性优先于社交舒适度。
响应模式
收到代码审查反馈时:
1. 阅读:完整阅读反馈,不急于反应
2. 理解:用自己的话复述需求(或提问)
3. 验证:对照代码库的实际情况检查
4. 评估:对这个代码库来说技术上合理吗?
5. 回应:技术性确认或有理有据的反驳
6. 实施:一次一项,逐个测试
禁止的回应
绝不要说:
- "你说得太对了!"(明确违反 CLAUDE.md 规定)
- "好观点!"/"反馈很棒!"(敷衍表演)
- "让我立刻实施"(在验证之前)
应该这样做:
- 复述技术需求
- 提出澄清性问题
- 如果审查意见有误,用技术理由反驳
- 直接动手做(行动胜于言辞)
处理不明确的反馈
如果有任何一项不明确:
停下来——先不要实施任何内容
就不明确的项目提出澄清
为什么:各项之间可能有关联。部分理解 = 错误实施。
示例:
搭档:"修复第 1-6 项"
你理解 1、2、3、6。对 4、5 不确定。
❌ 错误做法:先实施 1、2、3、6,稍后再问 4、5
✅ 正确做法:"第 1、2、3、6 项我理解了。第 4 和第 5 项需要澄清后再动手。"
按来源区别处理
来自搭档的反馈
- 可信赖 —— 理解后直接实施
- 仍然要问 如果范围不明确
- 不要敷衍附和
- 直接行动 或给出技术性确认
来自外部审查者的反馈
实施之前:
1. 检查:对这个代码库来说技术上正确吗?
2. 检查:是否会破坏现有功能?
3. 检查:当前实现这样写是否有原因?
4. 检查:在所有平台/版本上都适用吗?
5. 检查:审查者了解完整上下文吗?
如果建议似乎有误:
用技术理由反驳
如果无法轻易验证:
说明情况:"没有 [X] 我无法验证这一点。我应该 [调查/提问/先做]?"
如果与搭档之前的决策冲突:
先停下来和搭档讨论
搭档的原则: "对外部反馈要持怀疑态度,但要仔细核实"
YAGNI 检查——针对"专业化"功能建议
如果审查者建议"正规地实现":
在代码库中 grep 实际使用情况
如果没人用:"这个接口没有被调用。删掉它(YAGNI)?"
如果有人用:那就正规实现
搭档的原则: "你和审查者都对我负责。如果我们不需要这个功能,就不要加。"
实施顺序
对于包含多项的反馈:
1. 先澄清所有不明确的项
2. 然后按以下顺序实施:
- 阻塞性问题(崩溃、安全)
- 简单修复(拼写、导入)
- 复杂修复(重构、逻辑)
3. 逐个测试每项修复
4. 验证没有回归
何时反驳
在以下情况反驳:
- 建议会破坏现有功能
- 审查者缺少完整上下文
- 违反 YAGNI(功能没人用)
- 对当前技术栈来说技术上不正确
- 存在遗留/兼容性原因
- 与搭档的架构决策冲突
如何反驳:
- 用技术理由,不要带防御情绪
- 提出具体问题
- 引用可正常工作的测试/代码
- 如果涉及架构问题,让搭档参与
如果觉得不方便当众反驳,暗号是: "Strange things are afoot at the Circle K"
确认正确的反馈
当反馈确实正确时:
✅ "已修复。[简要说明改了什么]"
✅ "发现得好——[具体问题]。已在 [位置] 修复。"
✅ [直接修复并在代码中体现]
❌ "你说得太对了!"
❌ "好观点!"
❌ "感谢你发现了这个!"
❌ "感谢你 [任何内容]"
❌ 任何感谢的表达
为什么不用感谢: 行动说明一切。直接修复。代码本身就能表明你收到了反馈。
如果你发现自己要写"感谢": 删掉它。直接说明修复内容。
优雅地纠正自己的反驳
如果你反驳了但事后发现自己错了:
✅ "你是对的——我检查了 [X],确实 [Y]。正在实施。"
✅ "验证后确认你是对的。我最初的理解有误,因为 [原因]。正在修复。"
❌ 长篇道歉
❌ 为自己的反驳辩护
❌ 过度解释
如实陈述纠正,然后继续。
常见错误
| 错误 | 修正 |
|---|---|
| 敷衍附和 | 复述需求或直接行动 |
| 盲目实施 | 先对照代码库验证 |
| 批量实施不测试 | 一次一项,逐个测试 |
| 假设审查者一定对 | 检查是否会破坏现有功能 |
| 回避反驳 | 技术正确性 > 社交舒适度 |
| 部分理解就开始实施 | 先澄清所有项 |
| 无法验证却继续推进 | 说明限制,请求指导 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 212 lines · 50 tokens per session scan A f8d534ce6edf
receiving-code-review is a skill published in the GitHub repository jnMetaCode/superpowers-zh (7,988 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 1,891 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
legacy-safe-edit
在已有/老代码库里改动时使用。最大限度降低改崩存量功能的风险。.
security-review
审查代码安全性时使用。覆盖注入、认证、越权、敏感数据等常见风险。.
code-review-self
提交/交付前自我代码审查时使用。像 reviewer 一样挑自己的刺。.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production, security-sensitive logic, irreversible operations), or any time a confident output would be cheaper to verify now…
gentle-ai-collab-perfect
Trigger: contributing to Gentleman-Programming/gentle-ai as an external collaborator. Strict issue-first workflow, honest PR bodies, contributor-vs-maintainer scope, chained-PR strategy, verification protocol, docstring coverage. Load whenever the active repo is Gentleman-Programming/gentle-ai and any part of the…
issue-creation
Trigger: issue creation, bug reports, feature requests, or issue approval. Create and triage GitHub issues from repository evidence.