Ponytail is a set of instructions and extensions that guides AI coding agents toward smaller, simpler code changes while retaining safety checks. It is intended for developers using agents such as Claude Code, and the catalogue entries are its skills, instructions, plugin, and rule.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/DietrichGebert/ponytailWrote 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/commands/dietrichgebert/ponytail/ponytail-review)<a href="https://agentmods.dev/commands/dietrichgebert/ponytail/ponytail-review"><img src="https://agentmods.dev/badge/commands/dietrichgebert/ponytail/ponytail-review/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/commands/dietrichgebert/ponytail/ponytail-review"><img src="https://agentmods.dev/badge/commands/dietrichgebert/ponytail/ponytail-review.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 | $0.00011 | $0.00113 |
| Opus 5 | $0.00005 | $0.00056 |
| Sonnet 5 | $0.00002 | $0.00023 |
| Haiku 4.5 | $0.00001 | $0.00011 |
Grade A, and why
ponytail-review 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 2d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- ponytail-review — 100% identical, 2 lines differ
- ponytail-review — 100% identical, 0 lines differ
What it actually says
Review the current code changes for over-engineering only, not correctness. One line per finding: L: . . Tags: delete (dead code/speculative feature), stdlib (reinvented standard library), native (dependency doing what the platform does), yagni (abstraction with one implementation), shrink (same logic, fewer lines). End with the net lines removable. If nothing to cut: 'Lean already. Ship.'
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.
- 2d ago First seen · 6 lines · 11 tokens per session scan A ff09bd42b1d2
ponytail-review is a command published in the GitHub repository DietrichGebert/ponytail (132,657 stars, last pushed 2d ago), licensed MIT. It adds 11 tokens to every session and 113 once invoked, about $0.0001 per session on Opus 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 2026-09-08.
Other commands, from other repositories
discover
Run a full product discovery cycle — from outcome definition through opportunity mapping, prioritisation, and experiment design. Use when the team isn't sure what to build next, or before writing a PRD for a complex feature space.
tax-review
Tax-filing compliance check — invokes tax-reviewer to produce TM-tax-{slug}.md with MeF e-file schema, Form 8879, PTIN/Circular 230, and IRC §7216 consent gaps.
voice-compliance
Voice/telephony compliance check — invokes voice-ai-reviewer to produce TM-voice-{slug}.md with TCPA, STIR/SHAKEN, state recording-consent, EU AI Act Art. 50, and synth-voice deepfake-law gaps.
simplify
The over-engineering review: five tags (delete, stdlib, native, yagni, shrink), a mandatory replacement per finding, and a real null result when there is nothing to cut.
git
The pre-finish status: branch, hygiene findings, message checks, workflow lint, template state.
init
Install the formatters this repository needs, with every command visible before it runs.