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Get Started Free →Walmart review authenticity analyzer. Detect fake reviews, suspicious patterns, and rating manipulation. Includes WFS verified badge analysis, incentivized review detection, and Walmart-specific red flag identification. No API key required.
.claude/skills/nexscope-ai-walmart-review-checker/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 36% | 0% |
Review authenticity analyzer for Walmart — detect fake reviews, suspicious patterns, and feedback manipulation.
bashnpx skills add nexscope-ai/eCommerce-Skills --skill walmart-review-checker -g
| Signal | Description | |--------|-------------| | WFS Badge | Verified fulfillment patterns | | Incentivized | "Received free product" indicators | | Review timing | Clustered reviews in short periods | | Generic comments | Templated review patterns |
| Score | Level | Description | |-------|-------|-------------| | 70-100 | ✅ Low Risk | Reviews appear authentic | | 50-69 | ⚠️ Medium Risk | Some concerns found | | 30-49 | 🔴 High Risk | Multiple red flags | | 0-29 | 💀 Critical | Likely manipulated reviews |
Check these Walmart reviews:
5 stars - Great product, fast shipping from WFS!
5 stars - Exactly as described, love it!
1 star - Arrived damaged.bashpython3 scripts/analyzer.py '[ {"content": "Great product!", "rating": 5, "date": "2024-01-15", "wfs_verified": true}, {"content": "Amazing!", "rating": 5, "date": "2024-01-15", "wfs_verified": false} ]'
bashpython3 scripts/analyzer.py --demo
📊 Walmart Review Authenticity Report
Product: Example Product
Reviews: 25
Analysis Level: L3
━━━━━━━━━━━━━━━━━━━━━━━━
Authenticity Score: 74/100 ✅
Low Risk - Reviews appear authentic.
━━━━━━━━━━━━━━━━━━━━━━━━
Detection Results
✅ Time Clustering: Normal
✅ WFS Verified Ratio: 68% (healthy)
⚠️ Generic Comments: 12%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→pass | 13,264 | 9,381 | -29% | 1 | 1 | 0% | 2,136 | 2,278 | +7% | 0 | 0 | — |
case-02 | fail→pass | 10,085 | 6,826 | -32% | 1 | 1 | 0% | 1,778 | 1,896 | +7% | 0 | 0 | — |
case-03 | fail→pass | 11,586 | 9,630 | -17% | 1 | 1 | 0% | 1,927 | 2,250 | +17% | 0 | 0 | — |
case-04 | fail→pass | 10,540 | 4,622 | -56% | 1 | 1 | 0% | 1,872 | 1,347 | -28% | 0 | 0 | — |
case-05 | pass→pass | 8,525 | 2,684 | -69% | 1 | 1 | 0% | 1,524 | 1,099 | -28% | 0 | 0 | — |
case-06 | fail→pass | 6,877 | 3,744 | -46% | 1 | 1 | 0% | 831 | 1,133 | +36% | 0 | 0 | — |
case-07 | fail→pass | 4,542 | 2,455 | -46% | 1 | 1 | 0% | 799 | 1,038 | +30% | 0 | 0 | — |
case-08 | pass→pass | 7,747 | 5,175 | -33% | 1 | 1 | 0% | 1,246 | 1,483 | +19% | 0 | 0 | — |
case-09 | pass→pass | 8,761 | 7,251 | -17% | 1 | 1 | 0% | 1,466 | 1,722 | +17% | 0 | 0 | — |
case-10 | pass→pass | 9,167 | 3,195 | -65% | 1 | 1 | 0% | 1,299 | 1,135 | -13% | 0 | 0 | — |
case-11 | pass→pass | 12,729 | 7,595 | -40% | 1 | 1 | 0% | 1,988 | 1,809 | -9% | 0 | 0 | — |
case-12 | pass→pass | 12,755 | 10,528 | -17% | 1 | 1 | 0% | 2,012 | 2,264 | +13% | 0 | 0 | — |
case-13 | pass→pass | 10,270 | 7,594 | -26% | 1 | 1 | 0% | 1,896 | 1,983 | +5% | 0 | 0 | — |
case-14 | pass→pass | 13,247 | 9,949 | -25% | 1 | 1 | 0% | 1,996 | 2,131 | +7% | 0 | 0 | — |
case-15 | pass→pass | 10,724 | 2,314 | -78% | 1 | 1 | 0% | 1,736 | 984 | -43% | 0 | 0 | — |
case-16 | pass→pass | 8,129 | 1,955 | -76% | 1 | 1 | 0% | 1,361 | 956 | -30% | 0 | 0 | — |
case-17 | pass→pass | 11,988 | 2,028 | -83% | 1 | 1 | 0% | 2,037 | 963 | -53% | 0 | 0 | — |
case-18 | pass→pass | 13,574 | 10,250 | -24% | 1 | 1 | 0% | 2,078 | 2,210 | +6% | 0 | 0 | — |
case-19 | fail→pass | 15,482 | 11,364 | -27% | 1 | 1 | 0% | 2,396 | 2,393 | -0% | 0 | 0 | — |
case-20 | pass→pass | 14,698 | 13,259 | -10% | 1 | 1 | 0% | 2,406 | 2,810 | +17% | 0 | 0 | — |
case-21 | pass→pass | 7,920 | 7,938 | +0% | 1 | 1 | 0% | 1,229 | 1,704 | +39% | 0 | 0 | — |
case-22 | pass→pass | 13,960 | 13,093 | -6% | 1 | 1 | 0% | 2,263 | 2,627 | +16% | 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 +32 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.