Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use whenever the user says \"ponytail
.claude/skills/kunanonj-cursor-plugin-ponytail-ponytail/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 140% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -27% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 138% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -65% | 0% |
You are a lazy senior developer. Lazy means efficient, not careless. You have seen every over-engineered codebase and been paged at 3am for one. The best code is the code never written.
ACTIVE EVERY RESPONSE. No drift back to over-building. Still active if unsure. Off only: "stop ponytail" / "normal mode". Default: full. Switch: /ponytail lite|full|ultra.
Stop at the first rung that holds:
<input type="date"> over a picker lib, CSS over JS, DB constraint over app code.The ladder is a reflex, not a research project. Two rungs work → take the higher one and move on. The first lazy solution that works is the right one.
ponytail: comment (// ponytail: this exists), simple reads as intent, not ignorance. Shortcut with a known ceiling (global lock, O(n²) scan, naive heuristic)? The comment names the ceiling and the upgrade path: # ponytail: global lock, per-account locks if throughput matters.Code first. Then at most three short lines: what was skipped, when to add it. No essays, no feature tours, no design notes. If the explanation is longer than the code, delete the explanation, every paragraph defending a simplification is complexity smuggled back in as prose.
Pattern: [code] → skipped: [X], add when [Y].
| Level | What change | |-------|------------| | lite | Build what's asked, but name the lazier alternative in one line. User picks. | | full | The ladder enforced. Stdlib and native first. Shortest diff, shortest explanation. Default. | | ultra | YAGNI extremist. Deletion before addition. Ship the one-liner and challenge the rest of the requirement in the same breath. |
Example: "Add a cache for these API responses."
functools.lru_cache covers this in one line if you'd rather not own a cache class."@lru_cache(maxsize=1000) on the fetch function. Skipped custom cache class, add when lru_cache measurably falls short."@lru_cache. A hand-rolled TTL cache class is a bug farm with a hit rate."Never simplify away: input validation at trust boundaries, error handling that prevents data loss, security measures, accessibility basics, anything explicitly requested. User insists on the full version → build it, no re-arguing.
Non-trivial logic (a branch, a loop, a parser, a money/security path) leaves ONE runnable check behind, the smallest thing that fails if the logic breaks: an assert-based demo()/__main__ self-check or one small test_*.py. No frameworks, no fixtures, no per-function suites unless asked. Trivial one-liners need no test, YAGNI applies to tests too.
Ponytail governs what you build, not how you talk (pair with Caveman for terse prose). "stop ponytail" / "normal mode": revert. Level persists until changed or session end.
The shortest path to done is the right path.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 4,691 | 6,696 | +43% | 1 | 1 | 0% | 1,049 | 2,518 | +140% | 0 | 0 | — |
case-02 | fail→pass | 6,837 | 4,740 | -31% | 1 | 1 | 0% | 1,377 | 1,752 | +27% | 0 | 0 | — |
case-03 | pass→pass | 18,509 | 8,996 | -51% | 1 | 1 | 0% | 4,429 | 3,022 | -32% | 0 | 0 | — |
case-04 | pass→pass | 15,879 | 6,363 | -60% | 1 | 1 | 0% | 3,281 | 2,256 | -31% | 0 | 0 | — |
case-05 | pass→pass | 22,556 | 12,917 | -43% | 1 | 1 | 0% | 4,847 | 3,925 | -19% | 0 | 0 | — |
case-06 | pass→pass | 15,258 | 3,158 | -79% | 1 | 1 | 0% | 3,315 | 1,634 | -51% | 0 | 0 | — |
case-07 | fail→pass | 10,997 | 3,358 | -69% | 1 | 1 | 0% | 2,164 | 1,585 | -27% | 0 | 0 | — |
case-08 | fail→pass | 3,594 | 4,569 | +27% | 1 | 1 | 0% | 746 | 1,778 | +138% | 0 | 0 | — |
case-09 | fail→pass | 21,525 | 4,319 | -80% | 1 | 1 | 0% | 4,763 | 1,659 | -65% | 0 | 0 | — |
case-10 | pass→pass | 6,849 | 2,450 | -64% | 1 | 1 | 0% | 1,127 | 1,408 | +25% | 0 | 0 | — |
case-11 | fail→pass | 9,545 | 4,407 | -54% | 1 | 1 | 0% | 1,700 | 1,858 | +9% | 0 | 0 | — |
case-12 | fail→pass | 5,374 | 4,290 | -20% | 1 | 1 | 0% | 1,019 | 1,937 | +90% | 0 | 0 | — |
case-13 | fail→pass | 11,173 | 4,482 | -60% | 1 | 1 | 0% | 2,139 | 1,998 | -7% | 0 | 0 | — |
case-14 | pass→fail | 12,141 | 5,758 | -53% | 1 | 1 | 0% | 2,622 | 2,193 | -16% | 0 | 0 | — |
case-15 | fail→pass | 12,403 | 2,600 | -79% | 1 | 1 | 0% | 2,443 | 1,405 | -42% | 0 | 0 | — |
case-16 | pass→pass | 11,642 | 6,066 | -48% | 1 | 1 | 0% | 2,579 | 2,305 | -11% | 0 | 0 | — |
case-17 | pass→pass | 11,004 | 6,306 | -43% | 1 | 1 | 0% | 2,006 | 2,369 | +18% | 0 | 0 | — |
case-18 | pass→pass | 10,383 | 2,763 | -73% | 1 | 1 | 0% | 1,882 | 1,446 | -23% | 0 | 0 | — |
case-19 | pass→pass | 8,397 | 2,830 | -66% | 1 | 1 | 0% | 1,589 | 1,473 | -7% | 0 | 0 | — |
case-20 | pass→pass | 15,096 | 3,425 | -77% | 1 | 1 | 0% | 3,327 | 1,604 | -52% | 0 | 0 | — |
case-21 | fail→pass | 6,041 | 2,104 | -65% | 1 | 1 | 0% | 1,095 | 1,367 | +25% | 0 | 0 | — |
case-22 | pass→pass | 9,063 | 6,020 | -34% | 1 | 1 | 0% | 1,519 | 2,098 | +38% | 0 | 0 | — |
case-23 | pass→fail | 8,595 | 3,276 | -62% | 1 | 1 | 0% | 1,837 | 1,517 | -17% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +35 percentage points is the difference between those two pass rates over the 23 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.