Ponytail Audit

作者 dietrichgebertb088b2df6e08MIT158K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

Quality audit of the whole repo: bugs, security, real load, missing tests, speed, and what to delete. Most important first.

AI 產生的概覽

稽核整個程式碼儲存庫,回報缺陷、安全風險、負載問題、缺少的測試、效能問題以及可刪除的程式碼。

功能
產出一份一次性的儲存庫書面稽核報告,依重要性排序:正確、安全、能承受負載、有測試、快速、精簡。它先梳理程式碼結構,追蹤主要資料流,再把發現分成「必須修復」「應當修復」「可選優化」三組,每條都附上具體情境與最小修正建議。結尾給出結論、可刪除程式碼行數與相依套件數量的精簡估算,以及未檢查的部分。它不會修改任何程式碼。
適用情境
適用於在接手整個儲存庫、某個套件或某個資料夾前後,希望取得資深開發者觀點的審查時。適合找出缺陷、安全漏洞、負載相關弱點、缺少的測試與不必要的程式碼。不用於自行執行修正。
執行需求
不附帶指令稿或工具,僅為說明性指示。它依賴代理程式可讀取的儲存庫內容,包括 README、建置與部署設定、相依清單、進入點與測試。未說明需要憑證或網路存取。

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 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.

來源與署名

來源:dietrichgebert/ponytail位於.openclaw/skills/ponytail-audit提交b088b2d

授權條款: MIT

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