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Get Started Free →Optimize product titles for search visibility and click-through rate across e-commerce platforms. Platform-specific title rules for Amazon (200 chars), Etsy (140 chars), Walmart, Shopify SEO, and eBay.
.claude/skills/nexscope-ai-product-title-optimization/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-08 | ✓→✗ | ▼ Worse | 45% | 0% |
| case-12 | ✓→✗ | ▼ Worse | -34% | 0% |
| case-18 | ✓→✗ | ▼ Worse | -20% | 0% |
Optimize product titles for search visibility and click-through rate across e-commerce platforms. Platform-specific title rules for Amazon (200 chars), Etsy (140 chars), Walmart, Shopify SEO, and eBay.
Supported platforms: Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce.
Built by Nexscope — your AI assistant for smarter e-commerce decisions.
bashnpx skills add nexscope-ai/eCommerce-Skills --skill product-title-optimization -g
Optimize my product title for Amazon and Etsy. Product: handmade leather wallet, RFID blocking, bifold, for men. Current Amazon title: 'Leather Wallet for Men'.Step 1: Collect information from the user's message — product, platform, current situation, and goals.
Step 2: Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a").
Step 3: Research and analyze using the frameworks and methodology below.
Step 4: Deliver structured, actionable output with specific recommendations, not vague advice.
More e-commerce skills: nexscope-ai/eCommerce-Skills
Amazon-specific skills: nexscope-ai/Amazon-Skills
Built by Nexscope — your AI assistant for smarter e-commerce decisions.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 14,169 | 13,963 | -1% | 1 | 1 | 0% | 2,345 | 2,757 | +18% | 0 | 0 | — |
case-02 | fail→fail | 12,497 | 13,257 | +6% | 1 | 1 | 0% | 2,107 | 2,834 | +35% | 0 | 0 | — |
case-03 | fail→pass | 13,361 | 7,293 | -45% | 1 | 1 | 0% | 2,226 | 1,760 | -21% | 0 | 0 | — |
case-04 | fail→fail | 14,543 | 13,742 | -6% | 1 | 1 | 0% | 2,540 | 2,914 | +15% | 0 | 0 | — |
case-05 | fail→fail | 11,731 | 11,541 | -2% | 1 | 1 | 0% | 2,245 | 2,694 | +20% | 0 | 0 | — |
case-06 | fail→fail | 19,813 | 19,391 | -2% | 1 | 1 | 0% | 3,456 | 4,018 | +16% | 0 | 0 | — |
case-07 | pass→pass | 11,379 | 11,117 | -2% | 1 | 1 | 0% | 2,065 | 2,511 | +22% | 0 | 0 | — |
case-08 | pass→fail | 7,015 | 6,547 | -7% | 1 | 1 | 0% | 1,085 | 1,577 | +45% | 0 | 0 | — |
case-09 | pass→pass | 11,214 | 12,363 | +10% | 1 | 1 | 0% | 1,778 | 2,670 | +50% | 0 | 0 | — |
case-10 | pass→pass | 11,164 | 7,631 | -32% | 1 | 1 | 0% | 1,907 | 1,788 | -6% | 0 | 0 | — |
case-11 | pass→pass | 8,770 | 11,510 | +31% | 1 | 1 | 0% | 1,550 | 2,504 | +62% | 0 | 0 | — |
case-12 | pass→fail | 13,378 | 6,063 | -55% | 1 | 1 | 0% | 2,259 | 1,482 | -34% | 0 | 0 | — |
case-13 | pass→pass | 10,561 | 11,017 | +4% | 1 | 1 | 0% | 1,823 | 2,280 | +25% | 0 | 0 | — |
case-14 | fail→pass | 8,455 | 5,082 | -40% | 1 | 1 | 0% | 1,293 | 1,379 | +7% | 0 | 0 | — |
case-15 | pass→pass | 12,739 | 15,707 | +23% | 1 | 1 | 0% | 2,006 | 3,151 | +57% | 0 | 0 | — |
case-16 | pass→pass | 10,287 | 11,138 | +8% | 1 | 1 | 0% | 1,617 | 2,308 | +43% | 0 | 0 | — |
case-17 | pass→pass | 11,129 | 15,096 | +36% | 1 | 1 | 0% | 1,958 | 2,916 | +49% | 0 | 0 | — |
case-18 | pass→fail | 9,365 | 4,731 | -49% | 1 | 1 | 0% | 1,617 | 1,288 | -20% | 0 | 0 | — |
case-19 | pass→pass | 10,378 | 13,370 | +29% | 1 | 1 | 0% | 1,745 | 2,722 | +56% | 0 | 0 | — |
case-20 | pass→pass | 8,837 | 6,625 | -25% | 1 | 1 | 0% | 1,540 | 1,632 | +6% | 0 | 0 | — |
case-21 | pass→pass | 12,740 | 13,231 | +4% | 1 | 1 | 0% | 2,172 | 2,661 | +23% | 0 | 0 | — |
case-22 | pass→pass | 9,494 | 10,363 | +9% | 1 | 1 | 0% | 1,636 | 2,153 | +32% | 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 -20 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
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.