Borrowing it
Nothing to install: this file belongs to MisonL/Ling. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MisonL/Ling/main/.agents/skills/code-review-checklist/SKILL.mdgit clone --depth 1 https://github.com/MisonL/LingWrote 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/misonl/ling/code-review-checklist)<a href="https://agentmods.dev/skills/misonl/ling/code-review-checklist"><img src="https://agentmods.dev/badge/skills/misonl/ling/code-review-checklist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/misonl/ling/code-review-checklist"><img src="https://agentmods.dev/badge/skills/misonl/ling/code-review-checklist.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00020 | $0.01135 |
| Opus 5.5 | $0.00008 | $0.00454 |
| Sonnet 5.5 | $0.00004 | $0.00227 |
| Haiku 4.5 | $0.00002 | $0.00113 |
Grade A, and why
code-review-checklist 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 1mo 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
代码审查检查清单
快速审查清单
正确性
- 功能对齐:代码是否实现了预期的功能?
- 边缘情况:是否处理了所有的边缘情况?
- 错误处理:是否建立了完善的错误处理机制?
- 无明显 Bug:是否存在显而易见的逻辑漏洞?
安全性
- 输入校验:是否对所有输入进行了验证与净化(sanitized)?
- 注入防护:是否存在 SQL/NoSQL 注入风险?
- XSS/CSRF:是否存在跨站脚本或跨站请求伪造漏洞?
- 凭据安全:是否存在硬编码的密钥(secrets)或敏感凭据?
- AI 特定:是否针对提示词注入(Prompt Injection)进行了防护(如适用)?
- AI 特定:输出内容在进入关键接收端(Sinks)前是否已净化?
性能
- N+1 问题:是否存在数据库 N+1 查询问题?
- 循环优化:是否存在不必要的循环逻辑?
- 缓存策略:是否使用了适当的缓存机制?
- 包体积:是否考虑了对 bundle size(包体积)的影响?
代码质量
- 命名清晰:变量及函数命名是否意图明确?
- DRY(不要重复自己):是否遵循 DRY 原则,无冗余代码?
- SOLID(面向对象设计原则):是否遵循 SOLID 原则?
- 抽象层次:抽象层级是否恰当?
测试
- 单元测试:新代码是否配有相应的单元测试?
- 边缘测试:边缘情况是否包含在测试范围内?
- 可读性:测试用例是否易于阅读与维护?
文档
- 逻辑注释:复杂逻辑是否配有必要的说明注释?
- API 文档:公共 API 是否已记录?
- README:如有必要,是否已更新项目 README 文件?
AI/LLM(大语言模型)审查模式(2025)
逻辑与幻觉
- 思维链(Chain of Thought):其逻辑推理路径是否可验证?
- 边缘情况:AI 是否考虑了空状态、超时及部分失败的情况?
- 外部状态:代码对于文件系统或网络的假设是否安全?
提示词工程审查
// [FAIL] 代码中存在模糊的提示词
const response = await ai.generate(userInput);
// [OK] 结构化且安全的提示词
const response = await ai.generate({
system: "您是一个专业的解析器......",
input: sanitize(userInput),
schema: ResponseSchema
});
应标识的反模式
// [FAIL] 魔术数字
if (status === 3) { ... }
// [OK] 具名常量
if (status === Status.ACTIVE) { ... }
// [FAIL] 深度嵌套
if (a) { if (b) { if (c) { ... } } }
// [OK] 卫语句/早期返回
if (!a) return;
if (!b) return;
if (!c) return;
// 处理核心逻辑
// [FAIL] 长函数 (超过 100 行)
// [OK] 短小且专注的函数
// [FAIL] 使用 any 类型
const data: any = ...
// [OK] 使用正确的类型
const data: UserData = ...
审查评注指南
// [CRITICAL] 阻塞性问题:关键路径中存在 SQL 注入漏洞
[CRITICAL] BLOCKING(阻塞): 此处存在 SQL 注入风险
// [SUGGESTION] 重要建议:考虑使用 useMemo 优化性能
[SUGGESTION] SUGGESTION(建议): 考虑此处使用 useMemo 进行性能优化
// [NIT] 细节修饰 (Nits):对于不可变变量,优先使用 const
[NIT] NIT(细节): 对于不可变变量,建议优先使用 const 而非 let
// 疑问确认:如果此处用户(User)为空会怎样?
QUESTION(疑问): 如果此处 User(用户)为 null 会发生什么情况?
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.
- 1mo ago First seen · 126 lines · 20 tokens per session scan A 712c8b22c1c1
code-review-checklist is a skill published in the GitHub repository MisonL/Ling (8 stars, last pushed 7mo ago), licensed MIT. It adds 20 tokens to every session and 1,135 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…