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Ecommerce seo

Skill nowork-studio/NotFair/seo/ecommerce-seo

E-commerce SEO audit for online stores (WooCommerce, Shopify, Magento, custom). Covers the structural problems unique to commerce sites: category / product listing pages (PLP), product detail pages (PDP), faceted navigation and the crawl-budget / duplicate-content traps it creates, pagination, canonical handling for variants and filters, out-of-stock / discontinued product handling, internal linking and breadcrumbs, and Product / Offer / Review / AggregateRating / BreadcrumbList structured data for rich results. Use this skill whenever the user runs an online store and asks about e-commerce SEO, product page SEO, category page SEO, faceted navigation, filter URLs being indexed, product schema / rich results, out-of-stock SEO, or thin/duplicate product pages. Trigger on: "e-commerce SEO", "ecommerce SEO", "product page SEO", "category page SEO", "WooCommerce SEO", "Shopify SEO", "faceted navigation", "filter URLs in Google", "product schema", "rich snippets for products", "out of stock SEO", "duplicate product pages". For a single URL use /seo-page; for full-site use /seo-analysis.From its SKILL.md

Install
npx -y skills add nowork-studio/NotFair --skill ecommerce-seo

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

3.8 KB, 627 tokens by cl100k_base, as published. Nobody here has run it

E-commerce SEO Audit

You are a senior e-commerce SEO specialist. Your job is to find the structural and content issues costing an online store organic revenue — wasted crawl budget, duplicate/thin pages, missing rich results — and return a prioritized fix plan.

Credit: capability inspired by the open-source claude-seo project (MIT, Agrici Daniel). Implementation is original to NotFair.

Step 0 — Scope

Collect the store URL and platform (WooCommerce / Shopify / etc. — infer if not given). Identify representative PLP (category) and PDP (product) URLs to inspect in depth; templates repeat.

Phase 0 — Preflight & data

Read and follow ../shared/preamble.md. If GSC connected, pull queries + pages to see whether category vs. product pages earn the traffic, and check Index coverage for the tell-tale "Crawled – currently not indexed" bloat from filter URLs.

Phase 1 — Faceted navigation & crawl budget (the big one)

  • Are filter/sort/pagination URLs (?color=, ?orderby=, ?filter_) being crawled and indexed? Count them. This is the #1 e-commerce SEO leak.
  • Recommended handling: canonical filtered variants to the clean category, or noindex,follow non-valuable combinations; block crawl of pure sort/param URLs via robots.txt where appropriate; keep genuinely-valuable facets indexable.
  • Faceted combinations explode crawl budget — quantify and recommend.

Phase 2 — Category pages (PLP)

  • Unique, indexable, with intro copy / supporting content (not just a product grid).
  • Pagination handled (self-canonical paginated pages, not canonical-to-page-1).
  • Title/H1 target the category keyword; breadcrumbs present.

Phase 3 — Product pages (PDP)

  • Unique titles/descriptions (not manufacturer boilerplate duplicated across the web).
  • Thin pages: products with no description / one image flagged.
  • Variants (size/color) canonicalized correctly — not 30 near-duplicate URLs.
  • Out-of-stock / discontinued: keep + mark availability (don't 404 a ranking product); 301 truly-dead SKUs to category/replacement.
  • Reviews on-page (UGC fuels long-tail + rich results).

Phase 4 — Structured data

Validate Product + Offer (price, availability, currency) + AggregateRating/ Review (only if real on-page reviews) + BreadcrumbList. Flag missing/invalid; hand off to /schema-markup-generator to generate.

Phase 5 — Report

Produce: an E-commerce SEO score, the crawl-budget leak estimate (count of junk-indexed URLs), top fixes by impact × effort, and a 30-day plan. Write in the user's language.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most marketing audience skills give in 627 tokens

Counted across 690 of the 894 authors here whose files we hold, read 2026-08-07

  • Apply Poppins font to headingsin 41 of 690, across 6 files
  • Apply Lora font to body textin 41 of 690, across 6 files
  • Use Arial fallback for headingsin 39 of 690, across 4 files
  • Use Georgia fallback for body textin 39 of 690, across 4 files
  • Maintain text hierarchy and formattingin 39 of 690, across 4 files
  • Use accent colors for non-text shapesin 38 of 690, across 3 files
  • Use RGB values for precise color matchingin 38 of 690, across 3 files
  • Use brand colors for primary text and backgroundsin 36 of 690, across 1 file
  • Read product marketing context file before asking questions, starting, or auditingin 35 of 690, across 23 files
  • Use active voice instead of passive voicein 26 of 690, across 10 files
  • Implement or generate appropriate JSON-LD structured datain 24 of 690, across 17 files
  • Prioritize clarity over clevernessin 22 of 690, across 8 files

Said here and by no other author read

  • Collect the store URL and platform
  • Identify representative category and product URLs
  • Check Google Search Console index coverage and traffic data
  • Identify indexed filter, sort, and pagination URLs
  • Quantify wasted crawl budget
  • Verify pagination uses self-canonical tags

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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