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Get Started Free →Design, implement, tune, or test readable tactical action combat for web games. Use for attack timing, guard and dodge windows, hit contact, posture, lock-on, weapons, boss phases, combat feedback, and deterministic combat tests.
.claude/skills/mengto-design-action-combat/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 0% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 156% | 0% |
Treat combat as explicit state machines with visible, testable timing rather than animation-driven guesses.
For each player or enemy action, define: startup, active window, recovery, cancellation rules, resource cost, contact shape, damage/posture outcome, cooldown, and feedback. Make telegraph, danger, contact, and recovery readable at normal camera distance.
Give each defense a distinct purpose: spacing, dodge, timed guard, interruption, or resource trade. Avoid unpunishable attacks, recovery loops, unavoidable damage, and dominant spam. Boss phases must alter decision pressure without invalidating learned timing.
Cover early/late timing, wrong direction, out-of-range contact, multiple targets, interrupted actions, phase changes, cooldown boundaries, pause/frame step, equip swaps, and repeated inputs. Use seeded or queryable review scenarios instead of requiring long campaign playthroughs.
Test at realistic frame rate and camera distance. Confirm that player intent, contact feedback, health/posture changes, sound/VFX, and target state agree. Check reduced motion and input alternatives before release.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 14,646 | 13,593 | -7% | 1 | 1 | 0% | 2,540 | 2,373 | -7% | 0 | 0 | — |
case-01 | fail→fail | 21,331 | 23,290 | +9% | 1 | 1 | 0% | 3,787 | 4,103 | +8% | 0 | 0 | — |
case-02 | fail→pass | 20,848 | 26,086 | +25% | 1 | 1 | 0% | 3,816 | 5,642 | +48% | 0 | 0 | — |
case-03 | fail→fail | 26,503 | 29,433 | +11% | 1 | 1 | 0% | 4,822 | 5,584 | +16% | 0 | 0 | — |
case-05 | fail→pass | 17,087 | 15,499 | -9% | 1 | 1 | 0% | 2,740 | 2,734 | -0% | 0 | 0 | — |
case-06 | pass→pass | 15,103 | 19,204 | +27% | 1 | 1 | 0% | 2,657 | 3,514 | +32% | 0 | 0 | — |
case-07 | pass→pass | 16,388 | 18,039 | +10% | 1 | 1 | 0% | 2,648 | 3,280 | +24% | 0 | 0 | — |
case-08 | pass→pass | 9,363 | 5,246 | -44% | 1 | 1 | 0% | 1,500 | 1,201 | -20% | 0 | 0 | — |
case-09 | fail→pass | 12,912 | 12,478 | -3% | 1 | 1 | 0% | 2,115 | 2,408 | +14% | 0 | 0 | — |
case-10 | fail→fail | 16,562 | 10,988 | -34% | 1 | 1 | 0% | 2,657 | 1,989 | -25% | 0 | 0 | — |
case-11 | pass→pass | 11,990 | 15,763 | +31% | 1 | 1 | 0% | 2,129 | 2,902 | +36% | 0 | 0 | — |
case-12 | pass→pass | 10,511 | 9,286 | -12% | 1 | 1 | 0% | 1,867 | 1,846 | -1% | 0 | 0 | — |
case-13 | fail→pass | 11,306 | 10,318 | -9% | 1 | 1 | 0% | 2,003 | 2,212 | +10% | 0 | 0 | — |
case-14 | pass→pass | 14,689 | 18,507 | +26% | 1 | 1 | 0% | 2,625 | 3,523 | +34% | 0 | 0 | — |
case-15 | fail→pass | 36,657 | 20,757 | -43% | 1 | 1 | 0% | 1,526 | 3,910 | +156% | 0 | 0 | — |
case-16 | fail→pass | 15,257 | 12,786 | -16% | 1 | 1 | 0% | 2,370 | 2,329 | -2% | 0 | 0 | — |
case-17 | pass→pass | 15,418 | 11,888 | -23% | 1 | 1 | 0% | 2,457 | 2,268 | -8% | 0 | 0 | — |
case-18 | pass→pass | 13,786 | 16,164 | +17% | 1 | 1 | 0% | 2,166 | 2,800 | +29% | 0 | 0 | — |
case-19 | pass→pass | 15,468 | 13,346 | -14% | 1 | 1 | 0% | 2,588 | 2,559 | -1% | 0 | 0 | — |
case-20 | pass→pass | 16,428 | 22,032 | +34% | 1 | 1 | 0% | 2,778 | 3,960 | +43% | 0 | 0 | — |
case-21 | pass→pass | 19,434 | 25,689 | +32% | 1 | 1 | 0% | 3,657 | 5,386 | +47% | 0 | 0 | — |
case-22 | pass→pass | 16,727 | 17,212 | +3% | 1 | 1 | 0% | 2,714 | 2,987 | +10% | 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 +27 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.