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Get Started Free →Supply Chain Bottleneck Analyzer for Walmart Marketplace sellers. Diagnose cash flow, inventory, WFS costs, and referral fees. Includes comparison with Amazon FBA, lower storage fee optimization, and Walmart Connect ad spend analysis. No API key required for basic analysis.
.claude/skills/nexscope-ai-supply-chain-optimization-walmart/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -29% | 0% |
Supply chain bottleneck analyzer for Walmart Marketplace sellers. Diagnose cash flow, inventory, WFS costs, and referral fees.
bashnpx skills add nexscope-ai/eCommerce-Skills --skill supply-chain-optimization-walmart -g
| Feature | Walmart | vs Amazon | |---------|---------|-----------| | Fulfillment | WFS (Walmart Fulfillment Services) | FBA | | Commission | 6-15% (by category) | 8-15% | | Payment cycle | 14-21 days | 14 days | | Storage fees | Lower | Higher | | Long-term storage | No extra fee | Yes |
Selling Price $XX
├── Product Cost
├── Inbound Shipping
├── WFS Fulfillment Fee (similar to FBA)
├── WFS Storage Fee (lower than FBA)
├── Referral Fee (6-15%)
├── Advertising (Walmart Connect)
└── Net ProfitpythonBENCHMARKS = { "walmart": { "gross_margin": { "healthy": 0.35, # Walmart commission lower, benchmark can be lower "warning": 0.25, "danger": 0.15 }, "shipping_ratio": { "healthy": 0.06, # WFS shipping slightly higher "warning": 0.10, "danger": 0.15 }, "inventory_days": { "healthy": 45, "warning": 60, "danger": 90 }, "cash_cycle": { "healthy": 100, # Payment cycle slightly longer "warning": 130, "danger": 160 }, "net_margin": { "healthy": 0.18, "warning": 0.10, "danger": 0.05 } } }
bashexport WALMART_CLIENT_ID="xxx" export WALMART_CLIENT_SECRET="xxx"
| Data | API | |------|-----| | Orders | Orders API | | Inventory | Inventory API | | Fee Reports | Reports API |
Same 4-step process as Amazon version:
**Sales (Walmart-specific)**
• Average Selling Price: $___
• WFS Fulfillment Fee: $___/unit
• Referral Fee Rate: ___%
• Walmart Connect Ad Spend Ratio: ___%
**Inventory**
• Current Inventory Days: ___ days
• (Walmart has no long-term storage fees)| Item | Amazon | Walmart | |------|--------|---------| | Fulfillment | FBA | WFS | | Storage fees | High | Low | | Long-term storage | Yes | No | | Commission | 8-15% | 6-15% | | Payment cycle | 14 days | 14-21 days | | Traffic | High | Medium |
Part of Nexscope AI — AI tools for e-commerce sellers.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 23,534 | 18,004 | -23% | 1 | 1 | 0% | 4,520 | 4,468 | -1% | 0 | 0 | — |
case-02 | fail→pass | 18,741 | 12,984 | -31% | 1 | 1 | 0% | 3,556 | 3,466 | -3% | 0 | 0 | — |
case-03 | pass→pass | 18,778 | 17,518 | -7% | 1 | 1 | 0% | 3,444 | 4,381 | +27% | 0 | 0 | — |
case-04 | pass→pass | 10,939 | 7,282 | -33% | 1 | 1 | 0% | 1,914 | 2,192 | +15% | 0 | 0 | — |
case-05 | pass→pass | 10,192 | 9,356 | -8% | 1 | 1 | 0% | 1,710 | 2,417 | +41% | 0 | 0 | — |
case-06 | pass→pass | 14,024 | 12,694 | -9% | 1 | 1 | 0% | 2,294 | 2,955 | +29% | 0 | 0 | — |
case-07 | fail→pass | 15,977 | 12,241 | -23% | 1 | 1 | 0% | 2,708 | 2,926 | +8% | 0 | 0 | — |
case-08 | fail→pass | 19,225 | 13,233 | -31% | 1 | 1 | 0% | 2,928 | 3,115 | +6% | 0 | 0 | — |
case-09 | fail→fail | 15,463 | 13,801 | -11% | 1 | 1 | 0% | 2,440 | 3,116 | +28% | 0 | 0 | — |
case-10 | pass→pass | 12,443 | 8,064 | -35% | 1 | 1 | 0% | 2,100 | 2,293 | +9% | 0 | 0 | — |
case-11 | pass→pass | 12,525 | 7,033 | -44% | 1 | 1 | 0% | 2,056 | 2,122 | +3% | 0 | 0 | — |
case-12 | fail→pass | 9,976 | 6,843 | -31% | 1 | 1 | 0% | 1,651 | 1,991 | +21% | 0 | 0 | — |
case-13 | pass→pass | 12,570 | 10,017 | -20% | 1 | 1 | 0% | 2,092 | 2,733 | +31% | 0 | 0 | — |
case-14 | pass→pass | 12,268 | 7,235 | -41% | 1 | 1 | 0% | 1,994 | 2,093 | +5% | 0 | 0 | — |
case-15 | pass→pass | 10,153 | 4,680 | -54% | 1 | 1 | 0% | 1,645 | 1,741 | +6% | 0 | 0 | — |
case-16 | pass→pass | 9,275 | 6,451 | -30% | 1 | 1 | 0% | 1,498 | 1,905 | +27% | 0 | 0 | — |
case-17 | pass→pass | 12,460 | 6,095 | -51% | 1 | 1 | 0% | 2,161 | 1,965 | -9% | 0 | 0 | — |
case-18 | pass→pass | 12,895 | 5,159 | -60% | 1 | 1 | 0% | 2,033 | 1,753 | -14% | 0 | 0 | — |
case-19 | pass→pass | 12,587 | 6,623 | -47% | 1 | 1 | 0% | 2,117 | 1,969 | -7% | 0 | 0 | — |
case-20 | fail→pass | 14,372 | 4,918 | -66% | 1 | 1 | 0% | 2,363 | 1,683 | -29% | 0 | 0 | — |
case-21 | pass→pass | 11,446 | 5,785 | -49% | 1 | 1 | 0% | 1,693 | 1,841 | +9% | 0 | 0 | — |
case-22 | fail→pass | 14,301 | 6,590 | -54% | 1 | 1 | 0% | 2,333 | 1,889 | -19% | 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.
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.