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Get Started Free →When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle emails." For in-app onboarding, see onboarding-cro.
.claude/skills/dokhacgiakhoa-email-sequence/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 85% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 102% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 110% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 110% | 0% |
You are an expert in email marketing and automation. Your goal is to create email sequences that nurture relationships, drive action, and move people toward conversion.
Before creating a sequence, understand:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 21,200 | 45,035 | +112% | 1 | 1 | 0% | 3,201 | 4,455 | +39% | 0 | 0 | — |
case-02 | fail→pass | 19,888 | 14,300 | -28% | 1 | 1 | 0% | 2,628 | 3,354 | +28% | 0 | 0 | — |
case-03 | fail→pass | 10,729 | 12,974 | +21% | 1 | 1 | 0% | 1,813 | 3,348 | +85% | 0 | 0 | — |
case-04 | pass→pass | 16,468 | 20,419 | +24% | 1 | 1 | 0% | 2,794 | 4,414 | +58% | 0 | 0 | — |
case-05 | pass→pass | 15,200 | 12,374 | -19% | 1 | 1 | 0% | 2,252 | 3,007 | +34% | 0 | 0 | — |
case-06 | pass→pass | 15,219 | 14,142 | -7% | 1 | 1 | 0% | 2,365 | 3,080 | +30% | 0 | 0 | — |
case-07 | pass→pass | 11,963 | 10,807 | -10% | 1 | 1 | 0% | 1,848 | 2,861 | +55% | 0 | 0 | — |
case-08 | fail→pass | 16,273 | 19,152 | +18% | 1 | 1 | 0% | 2,170 | 4,386 | +102% | 0 | 0 | — |
case-09 | pass→pass | 14,381 | 16,981 | +18% | 1 | 1 | 0% | 2,094 | 4,235 | +102% | 0 | 0 | — |
case-10 | fail→pass | 10,045 | 13,147 | +31% | 1 | 1 | 0% | 1,554 | 3,261 | +110% | 0 | 0 | — |
case-11 | pass→pass | 17,735 | 18,213 | +3% | 1 | 1 | 0% | 2,260 | 3,686 | +63% | 0 | 0 | — |
case-12 | pass→pass | 9,085 | 10,823 | +19% | 1 | 1 | 0% | 1,381 | 2,479 | +80% | 0 | 0 | — |
case-13 | fail→pass | 11,346 | 18,110 | +60% | 1 | 1 | 0% | 1,955 | 4,103 | +110% | 0 | 0 | — |
case-14 | pass→pass | 15,038 | 20,329 | +35% | 1 | 1 | 0% | 2,320 | 3,900 | +68% | 0 | 0 | — |
case-15 | pass→pass | 20,653 | 22,267 | +8% | 1 | 1 | 0% | 2,552 | 4,123 | +62% | 0 | 0 | — |
case-16 | pass→pass | 16,633 | 12,794 | -23% | 1 | 1 | 0% | 2,017 | 3,216 | +59% | 0 | 0 | — |
case-17 | fail→fail | 14,353 | 20,043 | +40% | 1 | 1 | 0% | 2,197 | 3,838 | +75% | 0 | 0 | — |
case-18 | pass→pass | 11,916 | 17,279 | +45% | 1 | 1 | 0% | 1,981 | 3,399 | +72% | 0 | 0 | — |
case-19 | pass→pass | 28,738 | 23,039 | -20% | 1 | 1 | 0% | 4,106 | 4,649 | +13% | 0 | 0 | — |
case-20 | fail→fail | 16,137 | 13,904 | -14% | 1 | 1 | 0% | 2,918 | 3,751 | +29% | 0 | 0 | — |
case-21 | fail→fail | 26,969 | 24,187 | -10% | 1 | 1 | 0% | 5,718 | 6,685 | +17% | 0 | 0 | — |
case-22 | fail→fail | 14,321 | 19,274 | +35% | 1 | 1 | 0% | 2,250 | 4,068 | +81% | 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 +23 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.