ponytail-audit
SkillDev toolsponytail-audit is a skill that makes an AI agent scan an entire repository and produce a ranked report on over-engineering and quality problems. It reads the README, configs, dependencies, entry points, and tests, then judges the code against the load it is expected to handle. Each finding comes with a concrete failure case, a suggested fix, and what happens if it is skipped. The report changes no code.
Then ask your AI: use the ponytail-audit skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Have the repository you want audited available to the agent.
What your AI can do with it
- Scan a named folder, package, or the whole repo
- Report bugs, security holes, and data loss risks
- Flag code that breaks under real load
- Point out risky logic with no failing test
- List code to delete, merge, split, or replace with stdlib
- Rank findings and explain each in plain English
Getting started
- Have the repository you want audited available to the agent.
- Add the ponytail-audit skill to your agent setup.
- Name the folder, package, or repo to audit, or leave it blank for the whole repo.
- Ask the agent to audit the codebase and read the ranked report it returns.
What this skill tells your AI
The instructions your AI receives, as published by dietrichgebert/ponytail in skills/ponytail-audit/SKILL.md and read by Ahel’s review.
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
- 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.
- 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).
- 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.
- 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.
- Speed: big slowdowns are problems. Small wins (work repeated in a hot loop) are suggestions; some software counts every millisecond.
- 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 olderponytail:) 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:
- 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.
Signals
- GitHub stars
- 159k
- Forks
- 9k
- Last commit
- Oct 2026
Questions
- Does the audit change any code?
- No. It is a one-shot report and changes nothing. Each finding includes a suggested fix, but applying it is left to you.
- What does the report cover?
- Bugs, security holes, what breaks under real load, risky code without tests, slow paths, and code to delete, merge, or split, ranked by importance.
- How does it decide what counts as over-engineered?
- It flags dead code, unused options, duplicate helpers, interfaces with one implementation, pass-through wrappers, near-copies, and dependencies the standard library or platform already covers.
- What load does it judge the code against?
- It looks for the expected load, from one person running a script to many users and processes at once, and states which load it assumed.
Advanced
- Item type
- skill
- Key
ponytail-audit-dietrichgebert- Source
- github.com/dietrichgebert/ponytail
github.com/dietrichgebert/ponytail