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Get Started Free →Design an out-of-office that actually protects the time off — the auto-reply that routes instead of apologizes, the coverage map behind it, and the pre-departure handoff that prevents the beach laptop. Use when asked write my out of office message, going on vacation what do I set up, cover my work while I'm out, or I always come back to chaos. Produces the OOO message with routing, the coverage assignments confirmed, the pre-departure checklist, and the re-entry buffer plan.
.claude/skills/mohitagw15856-out-of-office-designer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 73% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 112% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 85% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 116% | 0% |
An out-of-office message is the visible 10% of a system whose real job is making the absence survivable without you — and most OOO setups are an apology with dates. The working version routes: every likely need maps to a named human who agreed to it, the auto-reply states who-for-what, urgent has a real path that isn't your phone, and the return date in the message is padded a day so re-entry isn't a 400-email ambush during back-to-back meetings.
Ask for these if not provided:
The auto-reply, verbatim: padded dates · routing lines · the urgent path · zero apology]
| Likely need | Coverer (confirmed?) | Briefed | Escalation | |---|---|---|---|
Deadlines moved · stakeholders pre-told · briefs sent · calendar blocked for the buffer day]
Buffer day: triage-first (the email-triage pass), coverage debrief, no meetings before noon]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 12,781 | 12,096 | -5% | 1 | 1 | 0% | 2,333 | 2,940 | +26% | 0 | 0 | — |
case-02 | fail→fail | 11,124 | 10,204 | -8% | 1 | 1 | 0% | 2,125 | 2,962 | +39% | 0 | 0 | — |
case-03 | fail→fail | 13,262 | 12,916 | -3% | 1 | 1 | 0% | 2,529 | 3,384 | +34% | 0 | 0 | — |
case-04 | fail→pass | 6,811 | 9,402 | +38% | 1 | 1 | 0% | 1,491 | 2,581 | +73% | 0 | 0 | — |
case-05 | pass→pass | 6,092 | 8,270 | +36% | 1 | 1 | 0% | 1,199 | 2,612 | +118% | 0 | 0 | — |
case-06 | fail→fail | 8,467 | 10,921 | +29% | 1 | 1 | 0% | 1,559 | 2,953 | +89% | 0 | 0 | — |
case-07 | fail→fail | 11,983 | 11,002 | -8% | 1 | 1 | 0% | 2,277 | 2,877 | +26% | 0 | 0 | — |
case-08 | pass→pass | 8,868 | 10,820 | +22% | 1 | 1 | 0% | 1,388 | 2,964 | +114% | 0 | 0 | — |
case-09 | fail→pass | 9,448 | 5,784 | -39% | 1 | 1 | 0% | 1,708 | 1,945 | +14% | 0 | 0 | — |
case-10 | fail→pass | 11,431 | 16,193 | +42% | 1 | 1 | 0% | 1,806 | 3,828 | +112% | 0 | 0 | — |
case-11 | pass→pass | 12,856 | 12,676 | -1% | 1 | 1 | 0% | 1,985 | 2,900 | +46% | 0 | 0 | — |
case-12 | pass→pass | 14,732 | 14,621 | -1% | 1 | 1 | 0% | 2,208 | 3,234 | +46% | 0 | 0 | — |
case-13 | fail→pass | 5,818 | 5,662 | -3% | 1 | 1 | 0% | 1,084 | 2,004 | +85% | 0 | 0 | — |
case-14 | pass→pass | 10,020 | 13,434 | +34% | 1 | 1 | 0% | 1,734 | 3,386 | +95% | 0 | 0 | — |
case-15 | fail→pass | 5,446 | 5,027 | -8% | 1 | 1 | 0% | 885 | 1,909 | +116% | 0 | 0 | — |
case-16 | pass→fail | 11,557 | 9,727 | -16% | 1 | 1 | 0% | 1,868 | 2,665 | +43% | 0 | 0 | — |
case-17 | fail→pass | 3,001 | 8,440 | +181% | 1 | 1 | 0% | 612 | 2,615 | +327% | 0 | 0 | — |
case-18 | fail→pass | 12,745 | 8,065 | -37% | 1 | 1 | 0% | 2,215 | 2,420 | +9% | 0 | 0 | — |
case-19 | pass→pass | 9,924 | 10,069 | +1% | 1 | 1 | 0% | 1,664 | 2,513 | +51% | 0 | 0 | — |
case-20 | pass→pass | 16,107 | 17,550 | +9% | 1 | 1 | 0% | 2,638 | 3,817 | +45% | 0 | 0 | — |
case-21 | pass→pass | 11,614 | 13,277 | +14% | 1 | 1 | 0% | 1,881 | 3,237 | +72% | 0 | 0 | — |
case-22 | pass→pass | 7,869 | 9,225 | +17% | 1 | 1 | 0% | 1,327 | 2,471 | +86% | 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. 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.