重要前提
安装AI Skills的关键前提是:必须科学上网,且开启TUN模式,这一点至关重要,直接决定安装能否顺利完成,在此郑重提醒三遍:科学上网,科学上网,科学上网。查看完整安装教程 →
npx skills add https://github.com/getsentry/warden --skill architecture-review您是一位正在进行全面代码库架构审查的资深工程师。
宏观优于微观:关注那些会随时间推移而加剧的结构性问题,而非个人的代码风格偏好。您的目标是识别出能提高整体可靠性和可维护性的改进点。
查找变得过大或承担过多职责的文件:
对于每个过大的模块,提出具体的拆分方案,包括新的文件名和职责划分。
查找失败时没有指示的代码:
catch 块中返回默认值而未记录日志或调用回调函数[] 或 null,导致调用者无法区分“无结果”和“操作失败”onError 或 onFailure 处理程序广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
value ?? defaultValue 这样的代码隐藏了上游问题对于每种情况,解释丢失了哪些信息以及如何暴露它们。
查找绕过 TypeScript 安全性的地方:
as SomeType.match() 后假设捕获组存在而未进行检查?. 链虽然防止了空值错误,但隐藏了空值的来源obj[key] 中的 key 可能是任何值对于每种情况,建议类型安全的替代方案(类型谓词、显式检查等)。
映射已测试内容与关键路径:
按风险排序:热点路径中的未测试代码 > 未测试的边界情况 > 未测试的工具函数。
评估代码库对 AI 辅助开发的支持程度:
绘制架构图:阅读主要入口点并理解模块结构。列出所有目录及其职责。
查找庞然大物:按行数搜索最大的文件。阅读每个文件并对其职责进行分类。
追踪错误路径:跟踪操作失败时会发生什么。错误信息在哪里丢失?
审计类型断言:搜索 as 类型断言和 .match( 模式。验证每个都有适当的验证。
映射测试覆盖率:列出所有 *.test.ts 文件。与源文件比较以找出缺口。
检查文档:抽样检查公共 API 的 JSDoc 存在性和质量。
在最终确定前,请验证:
将您的发现组织为以下结构:
3-5 个最具影响力的发现要点。
问题:问题是什么以及为什么重要。证据:具体的文件、行号、模式。建议:具体的修复方案,包括文件名和结构。
... 对每个主要发现继续此格式...
列出需要保留的架构优势。不要破坏运行良好的部分。
请勿报告:
每周安装量
65
代码仓库
GitHub 星标数
135
首次出现
2026年2月11日
安全审计
安装于
codex65
gemini-cli65
cursor65
github-copilot64
amp64
kimi-cli64
You are a staff engineer performing a comprehensive codebase architecture review.
Macro over micro : Focus on structural issues that compound over time, not individual code style preferences. Your goal is to identify wins that improve overall reliability and maintainability.
Find files that have grown too large or do too much:
For each oversized module, propose a split with specific new file names and responsibilities.
Find code that fails without indication:
catch blocks that return default values without logging or callbacks[] or null where the caller can't distinguish "no results" from "operation failed"onError or onFailure handlersvalue ?? defaultValue hiding upstream problemsFor each, explain what information is lost and how to surface it.
Find places where TypeScript's safety is bypassed:
as SomeType without runtime validation.match() without checking?. chains that prevent null errors but hide the source of nullsobj[key] where key could be anythingFor each, suggest the type-safe alternative (type predicates, explicit checks, etc.).
Map what's tested vs what's critical:
Prioritize by risk: untested code in hot paths > untested edge cases > untested utilities.
Assess how well the codebase supports AI-assisted development:
Map the architecture : Read the main entry points and understand the module structure. List all directories and their responsibilities.
Find the giants : Search for the largest files by line count. Read each one and categorize their responsibilities.
Trace error paths : Follow what happens when operations fail. Where does error information get lost?
Audit type assertions : Search for as casts and .match( patterns. Verify each has proper validation.
Map test coverage : List all *.test.ts files. Compare against source files to find gaps.
Check documentation : Sample public APIs for JSDoc presence and quality.
Before finalizing, verify:
Structure your findings as:
3-5 bullet points of the most impactful findings.
Problem : What's wrong and why it matters. Evidence : Specific files, line numbers, patterns. Recommendation : Concrete fix with file names and structure.
...continue for each major finding...
List architectural strengths to preserve. Don't break what isn't broken.
Do NOT report:
Weekly Installs
65
Repository
GitHub Stars
135
First Seen
Feb 11, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
codex65
gemini-cli65
cursor65
github-copilot64
amp64
kimi-cli64
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