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Get Started Free →Build or refactor reusable, data-driven enemy archetype and moveset systems for Three.js action games. Use for enemy content schemas, model and rig conventions, combat move timing and contact contracts, runtime state boundaries, placeholder fallbacks, deterministic fixtures, or production playthrough validation.
.claude/skills/mengto-build-threejs-enemy-systems/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 43% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 53% | 0% |
Make each enemy a portable authored definition consumed by shared runtime systems. Keep AI choice, combat resolution, rendering, and feedback as separate consumers of the same stable contract.
Define these fields before integrating a model:
Normalize at the asset boundary. Do not scatter scale fixes, pivot offsets, raw clip names, or compensating rotations through gameplay code.
For every move, define:
Let AI select only legal move IDs; let the combat system own timing and outcomes. Resolve damage from authoritative collision/contact events using stable action and target identifiers. Apply each contact once, then drive animation, VFX, audio, and UI from the resolved event.
When a production model is unavailable, use a deliberate placeholder that preserves footprint, height, pivot, collider, sockets, facing, move timing, and state readability. Log the fallback once and keep it visually unmistakable. Never silently substitute a mismatched asset or call placeholder visuals production-ready.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-10 | fail→pass | 13,555 | 10,193 | -25% | 1 | 1 | 0% | 2,300 | 2,677 | +16% | 0 | 0 | — |
case-01 | fail→pass | 22,033 | 25,759 | +17% | 1 | 1 | 0% | 4,735 | 6,609 | +40% | 0 | 0 | — |
case-02 | fail→fail | 26,757 | 28,550 | +7% | 1 | 1 | 0% | 5,428 | 7,054 | +30% | 0 | 0 | — |
case-03 | fail→fail | 30,073 | 26,383 | -12% | 1 | 1 | 0% | 6,209 | 7,048 | +14% | 0 | 0 | — |
case-04 | pass→pass | 10,070 | 14,515 | +44% | 1 | 1 | 0% | 2,055 | 3,473 | +69% | 0 | 0 | — |
case-05 | fail→fail | 9,238 | 10,088 | +9% | 1 | 1 | 0% | 1,698 | 2,772 | +63% | 0 | 0 | — |
case-06 | pass→pass | 13,185 | 16,471 | +25% | 1 | 1 | 0% | 2,815 | 4,355 | +55% | 0 | 0 | — |
case-07 | fail→fail | 16,126 | 16,867 | +5% | 1 | 1 | 0% | 3,190 | 4,124 | +29% | 0 | 0 | — |
case-08 | fail→pass | 18,450 | 21,927 | +19% | 1 | 1 | 0% | 3,420 | 5,171 | +51% | 0 | 0 | — |
case-09 | pass→pass | 13,220 | 8,925 | -32% | 1 | 1 | 0% | 2,162 | 2,331 | +8% | 0 | 0 | — |
case-11 | pass→pass | 15,735 | 17,190 | +9% | 1 | 1 | 0% | 3,079 | 4,220 | +37% | 0 | 0 | — |
case-12 | fail→pass | 13,788 | 14,682 | +6% | 1 | 1 | 0% | 2,425 | 3,464 | +43% | 0 | 0 | — |
case-13 | fail→fail | 15,347 | 17,929 | +17% | 1 | 1 | 0% | 3,343 | 4,619 | +38% | 0 | 0 | — |
case-14 | pass→pass | 13,018 | 14,345 | +10% | 1 | 1 | 0% | 2,399 | 3,544 | +48% | 0 | 0 | — |
case-15 | pass→pass | 14,862 | 15,252 | +3% | 1 | 1 | 0% | 2,961 | 3,873 | +31% | 0 | 0 | — |
case-16 | fail→pass | 11,299 | 11,479 | +2% | 1 | 1 | 0% | 1,943 | 2,965 | +53% | 0 | 0 | — |
case-17 | fail→pass | 29,592 | 13,091 | -56% | 1 | 1 | 0% | 2,715 | 3,445 | +27% | 0 | 0 | — |
case-18 | pass→pass | 11,283 | 8,524 | -24% | 1 | 1 | 0% | 1,967 | 2,265 | +15% | 0 | 0 | — |
case-19 | fail→pass | 11,724 | 9,032 | -23% | 1 | 1 | 0% | 1,945 | 2,392 | +23% | 0 | 0 | — |
case-20 | fail→fail | 20,822 | 26,007 | +25% | 1 | 1 | 0% | 3,868 | 6,015 | +56% | 0 | 0 | — |
case-21 | fail→fail | 17,297 | 15,436 | -11% | 1 | 1 | 0% | 3,278 | 3,591 | +10% | 0 | 0 | — |
case-22 | fail→pass | 19,053 | 20,339 | +7% | 1 | 1 | 0% | 3,378 | 4,144 | +23% | 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. 22 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.