E-commerce SEO analysis: Google Shopping visibility, Amazon marketplace intelligence, product schema validation, competitor pricing analysis, and marketplace keyword gaps. Combines on-page product SEO with marketplace data from DataForSEO Merchant API. Use when user says "ecommerce SEO", "product SEO", "Google Shopping", "marketplace SEO", "product schema", "Amazon SEO", "product listings", "shopping ads", or "merchant SEO".
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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
seo-ecommerce
description
E-commerce SEO analysis: Google Shopping visibility, Amazon marketplace intelligence, product schema validation, competitor pricing analysis, and marketplace keyword gaps. Combines on-page product SEO with marketplace data from DataForSEO Merchant API. Use when user says "ecommerce SEO", "product SEO", "Google Shopping", "marketplace SEO", "product schema", "Amazon SEO", "product listings", "shopping ads", or "merchant SEO".
Before gathering, check .seo-cache/ for reusable context from related SEO skills.
Reference: ../seo/references/shared-data-cache.md for schemas and dependency map.
Check these cache files when present:
.seo-cache/site-meta.json for domain, business type, industry, and crawl context
.seo-cache/audit-scores.json for prior full-audit priorities
.seo-cache/pages/{url-slug}/page-analysis.json for page-level context when a URL is provided
If found: parse and use clearly valid fields (note "Using cached [X] from [date]")
If missing, corrupt, or irrelevant: continue with fresh evidence
If the user says "refresh" or "re-run": ignore cache reads and overwrite on write
Comprehensive product page optimization, marketplace intelligence, and
competitive pricing analysis. Works standalone (on-page + schema) and with
DataForSEO Merchant API for live Google Shopping and Amazon data.
Commands
Command
Purpose
DataForSEO?
/seo ecommerce <url>
Full e-commerce SEO analysis of a product page or store
Optional
/seo ecommerce products <keyword>
Google Shopping competitive analysis
Required
/seo ecommerce gaps <domain>
Keyword gap: organic vs Shopping visibility
Required
/seo ecommerce schema <url>
Product schema validation and enhancement
No
1. Product Page Analysis (No DataForSEO Needed)
Fetch and parse any product page for on-page SEO quality.
Workflow
1. python scripts/fetch_page.py <url> → raw HTML
2. python scripts/parse_html.py --url <url> → SEO elements
3. Analyze product-specific signals (below)
Product SEO Checklist
Title Tag
Contains primary product keyword
Includes brand name
Under 60 characters (no truncation in SERPs)
Format: [Product Name] - [Key Feature] | [Brand]
Meta Description
Contains product keyword + benefit
Includes price or "from $XX" (triggers rich snippet interest)
# Product search: who sells what at what price
python scripts/dataforseo_merchant.py search "<keyword>" --marketplace google
# Seller analysis: merchant ratings and dominance
python scripts/dataforseo_merchant.py sellers "<keyword>"# Normalize results for analysis
python scripts/dataforseo_normalize.py results.json --module merchant
Analysis Outputs
Pricing Intelligence
Price distribution: min, max, median, P25, P75
Price outliers (> 2 standard deviations from median)
Price-to-rating correlation
Currency normalization to USD (or user-specified)
Seller Landscape
Top 10 sellers by listing count
Merchant rating distribution
Free shipping prevalence
New vs established sellers
Product Listing Quality
Title keyword patterns in top listings
Average rating and review count benchmarks
Image count per listing
Availability status distribution
Load references/marketplace-endpoints.md for full API parameter details.
3. Amazon Marketplace (DataForSEO)
Cross-marketplace intelligence comparing Google Shopping and Amazon.
## E-commerce SEO Report: [URL or Keyword]
### Overall Score: XX/100
### Product Page SEO
- Schema Completeness: XX/100
- Title & Meta: XX/100
- Image Optimization: XX/100
- Content Quality: XX/100
- Internal Linking: XX/100
### Marketplace Intelligence (if DataForSEO available)
- Google Shopping Listings: N products found
- Price Range: $XX - $XX (median: $XX)
- Top Seller: [name] (XX% market share)
- Amazon Comparison: [available/not checked]
### Top Recommendations
1. [Critical] ...
2. [High] ...
3. [Medium] ...
Generate a PDF report? Use `/seo google report`
Write to shared data cache
After completing all work, write a concise JSON summary to .seo-cache/ when the workflow produced durable findings.
Use the schemas and naming rules in ../seo/references/shared-data-cache.md; include at least cache_type, analyzed_at, source URL/domain, key findings, issues, recommendations, and tool limitations. Add .seo-cache/ to .gitignore if it is missing.