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Get Started Free →Create or redesign enterprise AI, automation, security, and operations product pages that explain system boundaries, approvals, auditability, exceptions, and rollback. Use for dark cinematic heroes, hairline grids, metric pauses, expandable solution rows, case-study evidence, security proof, and qualified demo or waitlist handoffs.
.claude/skills/mengto-operational-enterprise-ai/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 80% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 50% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 67% | 0% |
Build credibility by showing what the system does, where it stops, who approves actions, and how failures recover.
Replace source brands, customers, numbers, security badges, screenshots, and claims. Do not invent compliance or performance evidence.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 32,710 | 35,836 | +10% | 1 | 1 | 0% | 6,067 | 7,112 | +17% | 0 | 0 | — |
case-02 | pass→pass | 31,855 | 32,987 | +4% | 1 | 1 | 0% | 5,954 | 7,120 | +20% | 0 | 0 | — |
case-03 | fail→pass | 31,330 | 28,142 | -10% | 1 | 1 | 0% | 5,345 | 7,093 | +33% | 0 | 0 | — |
case-04 | pass→pass | 21,134 | 29,580 | +40% | 1 | 1 | 0% | 4,097 | 7,080 | +73% | 0 | 0 | — |
case-05 | pass→pass | 28,181 | 31,008 | +10% | 1 | 1 | 0% | 4,704 | 7,074 | +50% | 0 | 0 | — |
case-06 | pass→fail | 17,105 | 29,110 | +70% | 1 | 1 | 0% | 2,909 | 7,068 | +143% | 0 | 0 | — |
case-07 | fail→pass | 21,723 | 33,289 | +53% | 1 | 1 | 0% | 3,938 | 7,098 | +80% | 0 | 0 | — |
case-08 | fail→pass | 18,303 | 18,402 | +1% | 1 | 1 | 0% | 3,028 | 4,018 | +33% | 0 | 0 | — |
case-09 | fail→fail | 14,332 | 16,083 | +12% | 1 | 1 | 0% | 2,907 | 3,923 | +35% | 0 | 0 | — |
case-10 | pass→pass | 14,499 | 14,103 | -3% | 1 | 1 | 0% | 2,809 | 3,653 | +30% | 0 | 0 | — |
case-11 | pass→pass | 18,646 | 20,064 | +8% | 1 | 1 | 0% | 3,594 | 4,482 | +25% | 0 | 0 | — |
case-12 | fail→pass | 14,455 | 14,249 | -1% | 1 | 1 | 0% | 2,485 | 3,716 | +50% | 0 | 0 | — |
case-13 | fail→pass | 15,202 | 20,506 | +35% | 1 | 1 | 0% | 2,805 | 4,692 | +67% | 0 | 0 | — |
case-14 | fail→pass | 15,521 | 18,801 | +21% | 1 | 1 | 0% | 2,850 | 4,286 | +50% | 0 | 0 | — |
case-15 | pass→pass | 18,704 | 19,686 | +5% | 1 | 1 | 0% | 3,117 | 4,015 | +29% | 0 | 0 | — |
case-16 | pass→pass | 11,139 | 14,337 | +29% | 1 | 1 | 0% | 1,967 | 3,560 | +81% | 0 | 0 | — |
case-17 | fail→fail | 20,230 | 32,598 | +61% | 1 | 1 | 0% | 3,627 | 7,059 | +95% | 0 | 0 | — |
case-18 | pass→pass | 12,192 | 19,136 | +57% | 1 | 1 | 0% | 2,079 | 4,690 | +126% | 0 | 0 | — |
case-19 | pass→pass | 12,347 | 12,039 | -2% | 1 | 1 | 0% | 2,189 | 3,015 | +38% | 0 | 0 | — |
case-20 | fail→pass | 13,730 | 15,752 | +15% | 1 | 1 | 0% | 2,418 | 3,562 | +47% | 0 | 0 | — |
case-21 | pass→pass | 13,589 | 11,066 | -19% | 1 | 1 | 0% | 2,256 | 2,760 | +22% | 0 | 0 | — |
case-22 | fail→pass | 15,491 | 22,925 | +48% | 1 | 1 | 0% | 2,415 | 4,157 | +72% | 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 +32 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is 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.