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.
npx skills add DietrichGebert/ponytail --skill ponytail-reviewgit 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/skills/dietrichgebert/ponytail/ponytail-review)<a href="https://agentmods.dev/skills/dietrichgebert/ponytail/ponytail-review"><img src="https://agentmods.dev/badge/skills/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/skills/dietrichgebert/ponytail/ponytail-review"><img src="https://agentmods.dev/badge/skills/dietrichgebert/ponytail/ponytail-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00035 | $0.00520 |
| Opus 5 | $0.00017 | $0.00260 |
| Sonnet 5 | $0.00007 | $0.00104 |
| Haiku 4.5 | $0.00003 | $0.00052 |
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 11d 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
6 near-identical copies found in the catalogue:
- ponytail-review — 100% identical, 0 lines differ
- ponytail-review — 100% identical, 0 lines differ
- ponytail-review — 100% identical, 0 lines differ
- ponytail-review — 86% identical, 4 lines differ
- ponytail-review-skill — 84% identical, 14 lines differ
- ponytail-review — 75% identical
What it actually says
Review diffs for unnecessary complexity. One line per finding: location, what to cut, what replaces it. The diff's best outcome is getting shorter.
Format
L<line>: <tag> <what>. <replacement>., or <file>:L<line>: ... for
multi-file diffs.
Tags:
delete:dead code, unused flexibility, speculative feature. Replacement: nothing.stdlib:hand-rolled thing the standard library ships. Name the function.native:dependency or code doing what the platform already does. Name the feature.yagni:abstraction with one implementation, config nobody sets, layer with one caller.shrink:same logic, fewer lines. Show the shorter form.
Examples
❌ "This EmailValidator class might be more complex than necessary, have you considered whether all these validation rules are needed at this stage?"
✅ L12-38: stdlib: 27-line validator class. "@" in email, 1 line, real validation is the confirmation mail.
✅ L4: native: moment.js imported for one format call. Intl.DateTimeFormat, 0 deps.
✅ repo.py:L88: yagni: AbstractRepository with one implementation. Inline it until a second one exists.
✅ L52-71: delete: retry wrapper around an idempotent local call. Nothing replaces it.
✅ L30-44: shrink: manual loop builds dict. dict(zip(keys, values)), 1 line.
Scoring
End with the only metric that matters: net: -<N> lines possible.
If there is nothing to cut, say Lean already. Ship. and stop.
Boundaries
Scope: over-engineering and complexity only. Correctness bugs, security holes,
and performance are explicitly out of scope. Route them to a normal review
pass, not this one. A single smoke test or assert-based
self-check is the ponytail minimum, not bloat, never flag it for deletion.
Does not apply the fixes, only lists them.
"stop ponytail-review" or "normal mode": revert to verbose review style.
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.
- 11d ago First seen · 53 lines · 35 tokens per session scan A 76addbc1c529
ponytail-review is a skill published in the GitHub repository DietrichGebert/ponytail (134,118 stars, last pushed 3d ago), licensed MIT. It adds 35 tokens to every session and 520 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
review
5-pass structured code review — correctness, security, performance, readability, consistency.
critical-code-reviewer
Rigorously review code or pull requests for correctness, security, accessibility, maintainability, tests, and edge cases. Use when users request a critical code review, want a guided walkthrough of findings, need implementer-facing feedback, or want to prepare, create, or submit a GitHub pull request review.
brooks-sweep
Full-sweep mode: runs a unified analysis across all quality dimensions — code decay, architecture, tech debt, and test quality — then applies fixes directly to the codebase. Safe changes are auto-applied; risky changes are confirmed before execution. Drawing on twelve classic engineering books. Triggers when: user…
request-review
Dispatch a fresh reviewer agent with a clean context to critique the code after audit-code passes. The reviewer has no shared state with the coding agent and gives a genuine second opinion. Use after audit-code passes, before committing, or when user wants an independent code review.
second-pass-review
Independent audit of sanitized specs in workspace/output/. Three parallel LLM-based reviewer roles check structural leakage, content contamination, and behavioral completeness. Run AFTER Layer 5 sanitization, BEFORE implementation handoff.
check-pr
Read-only inspection of a single GitHub PR lifecycle — checks CI, review threads, description sync, and mergeability, and returns PASS or FAIL with per-gate findings. Never invokes the merge button. Use when verifying a PR is ready to merge, polling lifecycle progress, checking mergeability, or babysitting a GitHub PR…