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Get Started Free →Turn sales history and current stock counts into reorder points, order quantities, and a this-week purchase list -- so a product business stops stocking out of winners and stops burying cash in losers. Handles seasonality, supplier lead times, and the dead-stock cleanup.
.claude/skills/onewave-ai-inventory-reorder-planner/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 20% | 0% |
Stockouts burn revenue on the products that sell; overstock buries cash in the ones that do not. Both come from ordering by gut. Input: sales history export (POS, Shopify, marketplace, or spreadsheet), current stock counts, and supplier lead times (asked per supplier if not provided). Output: reorder points per SKU and the purchase list for this week.
WINNERS (high velocity, healthy margin -- never stock out), STEADY, SLOW, DEAD (no sales in 90+ days with stock on hand).reorder-plan.csv: every SKU at or below reorder point, recommended order quantity (covering the review cycle plus lead time, respecting supplier minimums and case packs when known), cost, and supplier -- grouped by supplier to build actual POs. Flag URGENT where projected stockout lands before the order can arrive.NEW -- judgment order, clearly separated from computed recommendations.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 6,087 | 15,682 | +158% | 1 | 1 | 0% | 923 | 1,829 | +98% | 0 | 0 | — |
case-02 | fail→fail | 26,610 | 11,919 | -55% | 1 | 1 | 0% | 2,223 | 2,569 | +16% | 0 | 0 | — |
case-03 | fail→pass | 17,022 | 11,018 | -35% | 1 | 1 | 0% | 2,612 | 2,040 | -22% | 0 | 0 | — |
case-04 | fail→fail | 12,311 | 10,979 | -11% | 1 | 1 | 0% | 2,060 | 2,330 | +13% | 0 | 0 | — |
case-05 | fail→pass | 12,532 | 10,761 | -14% | 1 | 1 | 0% | 1,738 | 2,214 | +27% | 0 | 0 | — |
case-06 | pass→pass | 7,013 | 7,826 | +12% | 1 | 1 | 0% | 1,144 | 2,028 | +77% | 0 | 0 | — |
case-07 | fail→pass | 13,518 | 12,845 | -5% | 1 | 1 | 0% | 2,151 | 2,923 | +36% | 0 | 0 | — |
case-08 | fail→pass | 12,229 | 7,128 | -42% | 1 | 1 | 0% | 1,858 | 1,725 | -7% | 0 | 0 | — |
case-09 | fail→pass | 30,511 | 7,841 | -74% | 1 | 1 | 0% | 1,570 | 1,889 | +20% | 0 | 0 | — |
case-10 | fail→fail | 4,187 | 3,274 | -22% | 1 | 1 | 0% | 656 | 1,156 | +76% | 0 | 0 | — |
case-11 | pass→pass | 10,228 | 5,626 | -45% | 1 | 1 | 0% | 1,665 | 1,640 | -2% | 0 | 0 | — |
case-12 | pass→pass | 9,410 | 5,063 | -46% | 1 | 1 | 0% | 1,542 | 1,585 | +3% | 0 | 0 | — |
case-13 | pass→pass | 12,263 | 12,106 | -1% | 1 | 1 | 0% | 2,167 | 2,679 | +24% | 0 | 0 | — |
case-14 | fail→fail | 10,415 | 7,119 | -32% | 1 | 1 | 0% | 1,790 | 1,939 | +8% | 0 | 0 | — |
case-15 | fail→fail | 10,400 | 7,634 | -27% | 1 | 1 | 0% | 1,837 | 1,950 | +6% | 0 | 0 | — |
case-16 | fail→pass | 9,700 | 11,561 | +19% | 1 | 1 | 0% | 1,831 | 2,286 | +25% | 0 | 0 | — |
case-17 | pass→pass | 13,475 | 10,475 | -22% | 1 | 1 | 0% | 2,039 | 2,225 | +9% | 0 | 0 | — |
case-18 | fail→fail | 12,170 | 11,143 | -8% | 1 | 1 | 0% | 1,893 | 2,329 | +23% | 0 | 0 | — |
case-19 | pass→pass | 12,324 | 9,534 | -23% | 1 | 1 | 0% | 1,959 | 2,290 | +17% | 0 | 0 | — |
case-20 | pass→pass | 11,510 | 2,653 | -77% | 1 | 1 | 0% | 1,851 | 1,071 | -42% | 0 | 0 | — |
case-21 | pass→pass | 10,811 | 7,399 | -32% | 1 | 1 | 0% | 2,084 | 2,088 | +0% | 0 | 0 | — |
case-22 | pass→pass | 11,064 | 9,932 | -10% | 1 | 1 | 0% | 1,774 | 2,259 | +27% | 0 | 0 | — |
case-23 | pass→pass | 11,407 | 6,731 | -41% | 1 | 1 | 0% | 2,021 | 1,924 | -5% | 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. 23 cases were attempted. The headline lift of +26 percentage points is the difference between those two pass rates over the 23 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.