跳到正文
cccskills
免费在 GitHub 公开中文介绍

ponytail-audit

审查整个代码仓库的缺陷、安全风险、负载问题和测试缺失,同时检查性能及多余实现,按优先级报告修复方案与不修复的影响。

原文查看原文说明

Quality audit of a whole repo: bugs, security holes, what breaks under real load, risky code without tests, slow paths, and what to delete, merge or split. Ranked, each finding explained in plain English. One-shot report, changes nothing. Use for "audit this codebase", "review the whole repo", "find bloat", "what can I delete", /ponytail-audit.

AI 评分100分/ 100查看安装方法

适用场景

  • 了解接手代码库的整体质量
  • 检查安全和负载方面的风险
  • 找出适合删除、合并或拆分的代码
  • 确定整个代码仓库的修复顺序

中文介绍

功能

ponytail-audit检查整个代码仓库,或您指定的文件夹、软件包,按优先级报告质量问题。它读取README、配置文件、依赖项、程序入口和测试,追踪从输入到存储及输出的流程。除缺陷、安全风险、预期负载下的表现、测试缺失和缓慢操作外,还会寻找适合删除、合并或拆分的代码。

适用场景

适合了解刚接手代码的状态,或决定整个项目应先修复哪些问题。每项问题都附有具体出错示例、最小修复方案及不修复的影响,可作为安排处理顺序的依据。整理不必要的依赖项或重复逻辑时,也可以使用。

使用示例

  • “审计整个代码仓库,并确定修复优先级。”
  • “检查身份验证和数据存储流程是否存在问题。”
  • “找出可以删除或合并的代码。”

注意事项

此技能只生成一次报告,不修改代码。最多报告20项问题,使用浅显的英语。对于大型代码仓库,会重点阅读影响较大的流程,并明确列出未检查的范围及无法执行的检查。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

客观评估

AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。

指令清晰度能否看出何时使用、做什么
A良好明确说明审计整个代码仓库或指定范围,并对质量问题排序。
步骤具体性步骤、输入输出和示例是否具体
A良好规定了解仓库结构、追踪流程、验证问题,以及最多20项的报告格式。
安全性发送数据、删除文件、执行命令等是否有说明
A良好明确限定为不修改代码的一次性报告,删除候选也仅作为建议报告。
安装难易度所需工具和 API 密钥是否少且有说明
A良好主要读取和搜索README及配置文件,未要求外部服务或密钥。

总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月20日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(1)

  • SKILL.md5.3 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Audit the whole repo like the senior developer who just inherited it and will be paged when it breaks. Order of importance: correct, safe, holds under load, tested, fast, lean. Lean still matters: every extra line must be read, tested and fixed later. This is a report the user asked for, so give it in full.

1. Map first

  • Audit what the user names: a folder, a package, or the whole repo. Nothing named: the whole repo.
  • Read the README, the deploy and build config, the dependency list, the entry points (main, routes, handlers, jobs, CLI commands) and the tests.
  • Find the expected load: one person running a script, or many users and processes at once. Judge scale against that, and say which load you assumed.
  • Trace the main flows end to end: where data comes in, what is stored, what goes out. Read those paths fully: input from users, money, auth, data writes, background jobs, anything shared between processes.
  • Big repo: go deep where a mistake costs the most, not file by file. Say which parts you did not read.

2. Look for

  1. Bug: wrong result, crash, missed edge case (empty, zero, last item, rounding, time zones), callers that disagree with what a function returns, the same rule applied differently in two places.
  2. Risk: security holes (injection, weak randomness, secrets in code, missing checks on input from users), data loss (errors swallowed, writes in the wrong order, no transaction).
  3. Scale: fine for one user, wrong for many: check-then-write races, the same work done by every process, memory or lists that only grow, a query per item, O(n^2) on big input, per-process state that must be shared.
  4. Missing test: risky logic (a branch, a parser, money, security, data writes) with no test that fails when it breaks. One good test, not coverage.
  5. Speed: big slowdowns are problems. Small wins (work repeated in a hot loop) are suggestions; some software counts every millisecond.
  6. Lean: code that should not exist or should be smaller.
    • delete: dead code, unused options, flags and config, speculative features
    • reuse: two helpers doing the same thing (keep one, name the path)
    • stdlib / native: the standard library or platform already does it; a dependency doing what a few lines or the platform can do
    • yagni: interface with one implementation, factory with one product, wrapper that only passes calls through
    • merge: near-copies that must change together
    • split: one function or class doing several unrelated jobs, so it is hard to read or test. Split by job, never by line count, and never into helpers that exist only to make a function shorter.

3. Check before you report

  • Every finding needs a concrete case: "this input or situation leads to this wrong result". No case, no finding.
  • Before calling code unused, grep the whole tree for it, including tests, fixtures, config, and string or dynamic references.
  • A shortcut marked with a shortcut: (or older ponytail:) comment that names its limit is a decision, not a finding, unless the expected load already crosses it.
  • Propose the smallest fix that works. Prefer fixes that delete code. Never add layers, frameworks or config the problem does not need.
  • No style taste, no "consider", no vague worries.

4. Output

Very simple English: short sentences, everyday words. Explain a technical term the first time you use it. The reader may never have seen this code.

Start with What this repo does: in two or three sentences, and the load you assumed.

Then the findings in three groups, most important first, skip empty groups:

  • Must fix: bug, security, data loss, breaks at the expected load.
  • Should fix: risky code without a test, real slowness, duplication, a function that mixes jobs, code that should not exist.
  • Nice to have: small speed-ups, shorter forms.

Number findings across all groups, so the user can say "fix 2 and 5". At most 20 findings; if you left smaller ones out, say how many. Every finding has all four parts, each one or two short sentences:

  1. Orders land on the wrong day (billing/close_day.py:L40-52)
    • What this is: At midnight this job closes the day and bills all orders of that day.
    • Problem: It takes "today" from the server clock, which runs in UTC. An order placed at 00:30 in Berlin is billed on the day before.
    • Fix: Compute the day once in the shop's time zone: datetime.now(ZoneInfo("Europe/Berlin")).date(). One line, nothing else changes.
    • If we skip it: Late orders show the wrong date, and accounting fixes them by hand.

End with:

  • Verdict: one line: healthy, or what to fix first.
  • Lean: -<N> lines, -<M> dependencies possible. when lean findings exist.
  • Not checked: the parts you did not read or could not run.

Nothing found: What this repo does:, then Healthy. Nothing to fix. and one line on what you checked.

One-shot report, changes no code.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

ponytail

免费有简介AI 评分 100分

先确认开发需求涉及的修改范围,再优先使用现有代码、标准功能和已安装依赖,以最小实现完整解决问题,并重视相关位置的修复、必要测试和简短报告。

  • 用最小实现满足开发需求
  • 排查调用方并修复共用代码中的缺陷
  • 避免不必要的依赖和抽象
DietrichGebert/ponytail17.3万2026年10月18日 更新

ponytail

免费有简介AI 评分 90分

优先复用现有代码、标准功能和已安装的依赖,以满足请求的最小变更完成实现或修复,处理受影响的调用方并进行必要验证,简要报告省略工作、未核实事项和风险。

  • 实现满足请求的最小版本
  • 修复共用逻辑中的根本原因
  • 重构时减少不必要的新增内容
DietrichGebert/ponytail17.3万2026年10月18日 更新

ponytail-audit

免费有简介AI 评分 100分

审查整个代码库或指定范围,检查缺陷、安全风险、负载承受能力、测试缺失和多余代码,按重要程度报告,并附具体问题场景及最小修复建议。

  • 检查接手代码的质量
  • 排查多用户使用时的风险
  • 找出多余代码和依赖项
DietrichGebert/ponytail17.3万2026年10月18日 更新

ponytail-debt

免费有简介AI 评分 100分

收集代码中标记简化实现或待改进事项的注释,按文件整理成技术债务台账,列出限制与重新审视的条件,标记缺少条件的条目,不修改代码。

  • 盘点暂缓改进的实现
  • 查找未注明重新审视条件的事项
  • 按文件建立技术债务台账
DietrichGebert/ponytail17.3万2026年10月18日 更新

ponytail-debt

免费有简介AI 评分 90分

收集代码中标记暂缓改进的注释,按文件整理为台账,记录简化内容、适用上限和重新审视的条件,并标出未写明审视条件的条目。

  • 汇总代码仓库中的暂缓事项
  • 查找缺少审视条件的临时处理
  • 用自定义标记收集TODO注释
DietrichGebert/ponytail17.3万2026年10月18日 更新

ponytail-gain

免费有简介AI 评分 100分

用文字条形图展示Ponytail 5公开基准测试中的代码量、输出词元、费用、时间及检查结果,对比未使用技能的情况,不计算当前项目的节省量。

  • 查看公开的代码量与费用对比
  • 查看正确性和测试相关测量结果
  • 同时查看测量条件与结果
DietrichGebert/ponytail17.3万2026年10月18日 更新

查看 DietrichGebert 的全部技能

报告此技能的问题