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Get Started Free →Set inventory policy for an item class: segmentation, safety stock, and replenishment method. Use when asked to set safety stock levels, segment items by ABC/XYZ, choose reorder points vs min-max, define stocking policy, or review excess and obsolete inventory. Produces a segmentation grid, per-segment service targets and safety-stock logic, a replenishment method choice per segment, and an E&O review cadence.
.claude/skills/mohitagw15856-inventory-policy/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-01 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 90% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 42% | 0% |
Inventory policy set item-by-item on gut feel produces the classic warehouse: too much of what doesn't sell, stockouts on what does. This skill sets policy by segment — classify items by value and demand variability, assign service targets and safety-stock logic per segment, choose the replenishment method that fits the demand pattern, and put excess & obsolescence review on a calendar so write-offs stop arriving as year-end surprises.
Ask for these if not provided:
From a thin brief, place the item in the grid using stated context, label placements [inferred — confirm with 12 months of usage data], and proceed.
ABC by annual usage value (A ≈ top 80% of value, B next 15%, C last 5%). XYZ by demand variability (X = steady/predictable; Y = variable but forecastable, e.g. seasonal; Z = lumpy/intermittent).
| | X (steady) | Y (variable) | Z (lumpy) | |---|---|---|---| | A (high value) | 97–99% service; lean SS; tight ROP, frequent review | 95–98%; SS sized to lead-time demand variability; ROP, monthly recalc | Do not blanket-stock: order-to-demand or contract supplier-held stock; each stocking decision is a named business call | | B | 95–97%; ROP with standard SS | 92–95%; ROP or min-max | Min-max with small max, or make-to-order | | C (low value) | 90–95%; min-max, generous max (cheap to hold, expensive to expedite) | 90%; min-max, quarterly review | Stock only if stockout stops a line or an A-item sale; else non-stocked |
Safety-stock logic (z-score framing, no heavy math): safety stock buffers demand and lead-time variability over the replenishment lead time. The service target sets a z multiplier on that variability — roughly z ≈ 1.28 at 90%, 1.65 at 95%, 2.05 at 98%, 2.33 at 99%. Two judgments matter more than the formula: the curve is nonlinear (95→99% costs far more stock than 90→95% — spend those points only on A-items), and for Z-items the variability estimate itself is unreliable, so formula-driven SS produces nonsense — use lead-time-demand coverage plus judgment, and say so.
Reorder point vs. min-max: ROP (order a fixed/economic quantity when stock hits demand-over-lead-time + SS) suits steady movers with continuous tracking — A/B items. Min-max (order up to max when stock falls to min) suits cheap, periodically reviewed, or lumpy items — most C and Z items. Respect MOQs: if MOQ ≫ the economic quantity, that's a supplier negotiation or a stocking-decision review, not a bigger max.
E&O cadence: monthly — flag items with >180 days of supply on hand or no usage in 90 days; quarterly — disposition review (rework / return / redeploy / discount / scrap) with finance, reserve recommendation per aging band; at lifecycle events — last-time-buy sizing when a supplier or product end-of-lifes.
1. Segmentation — the grid populated with item counts and value per cell; method used.
2. Policy table — Segment | Service target | Safety-stock logic | Replenishment method | Parameter review frequency.
3. Item-class recommendation — for the specific scope: segment, target, SS sizing, method, and the parameters to set, with assumptions labelled.
4. E&O cadence — triggers, review calendar, disposition paths, reserve approach.
5. Exceptions — items policy must not automate (shelf-life, LTB, contractual stock) and their handling.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→pass | 29,788 | 26,301 | -12% | 1 | 1 | 0% | 6,310 | 6,560 | +4% | 0 | 0 | — |
case-01 | fail→pass | 29,058 | 19,131 | -34% | 1 | 1 | 0% | 6,320 | 5,287 | -16% | 0 | 0 | — |
case-03 | fail→pass | 31,036 | 28,733 | -7% | 1 | 1 | 0% | 6,300 | 6,921 | +10% | 0 | 0 | — |
case-04 | fail→pass | 14,119 | 17,965 | +27% | 1 | 1 | 0% | 2,401 | 4,564 | +90% | 0 | 0 | — |
case-05 | pass→pass | 13,489 | 16,534 | +23% | 1 | 1 | 0% | 2,204 | 3,977 | +80% | 0 | 0 | — |
case-06 | fail→pass | 17,675 | 19,362 | +10% | 1 | 1 | 0% | 3,416 | 4,865 | +42% | 0 | 0 | — |
case-07 | fail→pass | 10,557 | 11,666 | +11% | 1 | 1 | 0% | 1,705 | 3,158 | +85% | 0 | 0 | — |
case-08 | pass→pass | 12,092 | 13,685 | +13% | 1 | 1 | 0% | 2,195 | 3,642 | +66% | 0 | 0 | — |
case-09 | pass→pass | 18,299 | 20,850 | +14% | 1 | 1 | 0% | 3,103 | 5,027 | +62% | 0 | 0 | — |
case-10 | fail→fail | 10,042 | 19,762 | +97% | 1 | 1 | 0% | 1,839 | 4,768 | +159% | 0 | 0 | — |
case-11 | fail→pass | 15,563 | 17,852 | +15% | 1 | 1 | 0% | 2,736 | 4,486 | +64% | 0 | 0 | — |
case-12 | pass→pass | 14,492 | 12,761 | -12% | 1 | 1 | 0% | 2,404 | 3,661 | +52% | 0 | 0 | — |
case-13 | fail→fail | 13,988 | 13,287 | -5% | 1 | 1 | 0% | 2,344 | 3,611 | +54% | 0 | 0 | — |
case-14 | fail→fail | 13,240 | 11,199 | -15% | 1 | 1 | 0% | 2,203 | 3,285 | +49% | 0 | 0 | — |
case-15 | fail→pass | 18,551 | 16,571 | -11% | 1 | 1 | 0% | 3,006 | 4,142 | +38% | 0 | 0 | — |
case-16 | pass→pass | 13,118 | 13,069 | -0% | 1 | 1 | 0% | 2,122 | 3,593 | +69% | 0 | 0 | — |
case-17 | pass→pass | 12,217 | 12,129 | -1% | 1 | 1 | 0% | 2,096 | 3,280 | +56% | 0 | 0 | — |
case-18 | pass→pass | 14,813 | 14,711 | -1% | 1 | 1 | 0% | 2,588 | 4,173 | +61% | 0 | 0 | — |
case-19 | pass→pass | 14,332 | 16,596 | +16% | 1 | 1 | 0% | 2,318 | 3,978 | +72% | 0 | 0 | — |
case-20 | pass→pass | 18,429 | 20,540 | +11% | 1 | 1 | 0% | 3,275 | 4,905 | +50% | 0 | 0 | — |
case-21 | pass→pass | 13,827 | 16,495 | +19% | 1 | 1 | 0% | 2,309 | 4,110 | +78% | 0 | 0 | — |
case-22 | pass→pass | 21,116 | 25,394 | +20% | 1 | 1 | 0% | 3,785 | 5,708 | +51% | 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 +36 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.