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Get Started Free →AI-powered online reputation management skill for e-commerce brands. Builds reputation monitoring frameworks, negative review response strategies, rating recovery plans, and crisis communication templates.
.claude/skills/nexscope-ai-online-reputation-management/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 118% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 13% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 38% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 29% | 0% |
| case-05 | ✓→✓ | ＝ Same ✓ | 34% | 0% |
AI-powered online reputation management skill for e-commerce brands. Builds reputation monitoring frameworks, negative review response strategies, rating recovery plans, and crisis communication templates.
clawhub install online-reputation-managementInput: Brand name, product name, or store URL
Output: Reputation monitoring plan, negative review response templates, rating recovery roadmap, crisis response playbook
> "I run a your business type] on platform]. Help me set up online reputation management for my business. Here's my current situation: describe context]."
Built by Nexscope AI — AI-powered e-commerce intelligence.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 10,339 | 10,138 | -2% | 1 | 1 | 0% | 1,470 | 1,666 | +13% | 0 | 0 | — |
case-02 | fail→fail | 10,267 | 11,659 | +14% | 1 | 1 | 0% | 1,626 | 2,113 | +30% | 0 | 0 | — |
case-03 | pass→pass | 12,276 | 14,312 | +17% | 1 | 1 | 0% | 1,810 | 2,497 | +38% | 0 | 0 | — |
case-04 | pass→pass | 12,450 | 14,318 | +15% | 1 | 1 | 0% | 1,944 | 2,507 | +29% | 0 | 0 | — |
case-05 | pass→pass | 11,807 | 14,865 | +26% | 1 | 1 | 0% | 1,885 | 2,524 | +34% | 0 | 0 | — |
case-06 | pass→pass | 8,519 | 8,496 | -0% | 1 | 1 | 0% | 1,262 | 1,639 | +30% | 0 | 0 | — |
case-07 | fail→pass | 5,271 | 9,769 | +85% | 1 | 1 | 0% | 786 | 1,713 | +118% | 0 | 0 | — |
case-08 | fail→fail | 13,368 | 11,549 | -14% | 1 | 1 | 0% | 1,982 | 1,981 | -0% | 0 | 0 | — |
case-09 | pass→pass | 14,890 | 17,827 | +20% | 1 | 1 | 0% | 2,530 | 3,380 | +34% | 0 | 0 | — |
case-10 | pass→pass | 10,121 | 11,729 | +16% | 1 | 1 | 0% | 1,528 | 2,057 | +35% | 0 | 0 | — |
case-11 | pass→pass | 10,503 | 10,290 | -2% | 1 | 1 | 0% | 1,675 | 1,758 | +5% | 0 | 0 | — |
case-12 | pass→pass | 16,005 | 15,695 | -2% | 1 | 1 | 0% | 2,394 | 2,715 | +13% | 0 | 0 | — |
case-13 | pass→pass | 11,788 | 14,434 | +22% | 1 | 1 | 0% | 1,855 | 2,472 | +33% | 0 | 0 | — |
case-14 | pass→pass | 13,397 | 16,259 | +21% | 1 | 1 | 0% | 2,125 | 2,540 | +20% | 0 | 0 | — |
case-15 | pass→pass | 12,496 | 14,960 | +20% | 1 | 1 | 0% | 2,019 | 2,464 | +22% | 0 | 0 | — |
case-16 | pass→pass | 13,822 | 10,567 | -24% | 1 | 1 | 0% | 2,179 | 1,989 | -9% | 0 | 0 | — |
case-17 | pass→pass | 13,391 | 14,420 | +8% | 1 | 1 | 0% | 1,993 | 2,583 | +30% | 0 | 0 | — |
case-18 | pass→pass | 12,991 | 11,958 | -8% | 1 | 1 | 0% | 1,995 | 2,230 | +12% | 0 | 0 | — |
case-19 | pass→pass | 15,441 | 15,507 | +0% | 1 | 1 | 0% | 2,355 | 2,772 | +18% | 0 | 0 | — |
case-20 | pass→pass | 13,025 | 14,339 | +10% | 1 | 1 | 0% | 2,073 | 2,546 | +23% | 0 | 0 | — |
case-21 | pass→pass | 7,042 | 6,808 | -3% | 1 | 1 | 0% | 1,136 | 1,364 | +20% | 0 | 0 | — |
case-22 | fail→fail | 13,232 | 12,228 | -8% | 1 | 1 | 0% | 2,545 | 2,592 | +2% | 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 +5 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.