Borrowing it
Nothing to install: this file belongs to engasnm111/lnwjud. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/engasnm111/lnwjud/main/.agents/skills/ponytail-review/SKILL.mdgit clone --depth 1 https://github.com/engasnm111/lnwjudWrote 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/engasnm111/lnwjud/ponytail-review)<a href="https://agentmods.dev/skills/engasnm111/lnwjud/ponytail-review"><img src="https://agentmods.dev/badge/skills/engasnm111/lnwjud/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/engasnm111/lnwjud/ponytail-review"><img src="https://agentmods.dev/badge/skills/engasnm111/lnwjud/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 | $0.00100 | $0.00576 |
| Opus 5.5 | $0.00040 | $0.00230 |
| Sonnet 5.5 | $0.00020 | $0.00115 |
| Haiku 4.5 | $0.00010 | $0.00058 |
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 1mo 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
8 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 — 100% identical, 0 lines differ
- ponytail-review — 100% identical, 0 lines differ
- ponytail-review — 100% identical, 0 lines differ
- ponytail-review — 100% identical, 11 lines differ
- ponytail-review — 100% identical, 0 lines differ
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 ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 58 lines · 100 tokens per session scan A 40df33b58fc6
ponytail-review is a skill published in the GitHub repository engasnm111/lnwjud (207 stars, last pushed 4d ago), licensed MIT. It adds 100 tokens to every session and 576 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-11.
Other skills, from other repositories
simplify-code
Parallel 4-agent cleanup of recent code changes.
pr-lens
Draw code changes as animated architecture/data-flow SVGs.
opencode
Delegate coding to OpenCode CLI (features, PR review).
requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
security-review
Complete a security review of the pending changes on the current branch.
act-on-feedback
Act on user feedback attached to the current session. Use when the user submits feedback on the session's changes via the Submit Feedback button.