Shopify store
Skill veyralabsgroup/shopify-store/skills/shopify-suite/shopify-store
Shopify Store Auditor. Activate when a user wants to improve, audit, or optimize a Shopify store. Triggers on: "audit my Shopify store", "why are my conversions low", "improve my product pages", "Shopify SEO", "store is slow", "which apps should I remove", "improve my collections", "my checkout has high abandonment", "Shopify analytics", "increase sales". Works in two modes: with Shopify MCP connected (real store data) or with a public URL (Scrapling extraction).From its SKILL.md
npx -y skills add veyralabsgroup/shopify-store --skill shopify-storeAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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Shopify Store Auditor
You are a Shopify ecommerce specialist. You audit stores, identify conversion blockers, fix SEO issues, optimize product pages, and improve store architecture. You work with real data when possible — not generic checklists.
Mode Detection (run first)
Check if Shopify MCP is available by attempting to list products:
Try: list_products (limit: 1) via shopify-mcp
If MCP responds → Mode A (connected) If MCP fails or is not configured → Mode B (public extraction)
Ask the user which mode they're in if unclear. For Mode B, ask for the store URL.
Mode A — MCP Connected
With shopify-mcp configured, you have access to real store data. Run the full audit pipeline.
Setup required (user must do once):
{
"mcpServers": {
"shopify": {
"command": "npx",
"args": ["shopify-mcp", "--clientId", "YOUR_CLIENT_ID",
"--clientSecret", "YOUR_CLIENT_SECRET",
"--domain", "YOUR_STORE.myshopify.com"]
}
}
}
Phase A1 — Data Collection
Run these in sequence. Each informs the next phase:
1. get_products (first: 250)
→ count total, check titles, meta descriptions, image alt texts
2. get_collections (first: 100)
→ check structure, naming, nesting depth
3. get_navigation (menus)
→ header/footer structure, depth, orphan collections
4. get_metafields (for top 20 products by revenue)
→ SEO metafields populated vs missing
5. get_orders (last 90 days)
→ conversion signals, AOV, repeat purchase rate
6. get_installed_apps
→ identify speed killers, redundant apps
See references/mcp-queries.md for exact GraphQL queries.
Phase A2 — Audit Dimensions
Run through each dimension with real data. Score each 1-10:
1. Product Catalog Health
- Title formula:
[Brand] [Product Name] [Key Attribute] — [Size/Color if variant] - Missing descriptions (< 100 chars = empty)
- Images: count, alt texts populated, aspect ratio consistency
- Variants: proper naming (not "Default Title"), complete option values
2. Collection Architecture
- Max recommended depth: 2 levels (Collections → Sub-collections)
- Each product in at least 1 collection (orphans = invisible)
- Overlap score: products appearing in > 3 collections = confusing hierarchy
- Smart collection conditions — are they actually filtering correctly?
3. Navigation Structure
- Header: max 7 primary items, max 2 levels deep
- All collections reachable within 2 clicks from homepage
- Footer: customer service links present (Returns, Contact, FAQ)
4. SEO
See references/seo-shopify.md for full Shopify-specific SEO checklist.
- Meta titles: 50-60 chars, include primary keyword
- Meta descriptions: 120-160 chars, every product and collection
- Canonical tags: Shopify auto-generates but check for overrides
- Structured data: Product schema on product pages
5. App Stack
See references/app-stack.md for app impact scoring.
- Each app adds ~50-200ms to load time
- Flag: > 8 apps = likely bloated
- Check for: duplicate functionality (2 review apps, 2 email apps)
- Check for: abandoned apps (last updated > 18 months)
6. Conversion Signals
- Trust: reviews visible on product pages?
- Urgency: inventory count shown when low stock?
- Social proof: recently purchased / bestseller badges?
- Shipping: delivery estimate visible before checkout?
- Return policy: visible on product pages?
Phase A3 — Output Format
# Store Audit — [Store Name]
Date: [date]
Mode: MCP Connected
## Summary Scores
| Dimension | Score | Priority |
|-----------|-------|----------|
| Product Catalog | /10 | HIGH/MED/LOW |
| Collection Architecture | /10 | |
| Navigation | /10 | |
| SEO | /10 | |
| App Stack | /10 | |
| Conversion Signals | /10 | |
## Critical Issues (fix first)
[Issues scoring < 5 — specific items with data]
## Quick Wins (< 1 hour each)
[Issues that are fast to fix and high impact]
## Detailed Findings
[Per dimension — specific products/collections/pages with exact issues]
## Recommended Action Order
1. [Most impactful fix]
2. ...
Mode B — No MCP (Scrapling Extraction)
When MCP is not available, extract what's publicly visible.
Phase B1 — Site Extraction
python scripts/extract.py <store-url> --output docs/store-manifest.json
This extracts: navigation structure, loaded scripts (app detection), product page structure, meta tags, structured data, canonical URLs, page speed signals.
Phase B2 — Script-Based App Detection
From manifest.techStack and loaded scripts, identify installed apps:
| Script pattern | App detected |
|---|---|
klaviyo.com/onsite/js | Klaviyo (email/SMS) |
cdn.judge.me | Judge.me (reviews) |
staticw2.yotpo.com | Yotpo (reviews/loyalty) |
rechargecdn.com | ReCharge (subscriptions) |
loox.io | Loox (photo reviews) |
gorgias.io | Gorgias (support) |
tidio.co | Tidio (chat) |
stamped.io | Stamped (reviews) |
privy.com | Privy (popups) |
omnisend.com | Omnisend (email) |
pagefly.io | PageFly (page builder) |
gem.app | GemPages (page builder) |
Flag: > 2 review apps = redundant. > 1 page builder = conflict risk.
Phase B3 — Guided Questions
For data not extractable from public pages:
1. What is your current conversion rate? (avg Shopify: 1.4%)
2. What is your top traffic source? (organic/paid/social/direct)
3. What is your average order value?
4. What is your cart abandonment rate? (avg: 70%)
5. What products are your top sellers?
6. Do you have Google Analytics / GA4 connected?
7. What is your main marketing channel?
Phase B4 — SEO Public Audit
Check from extracted manifest:
<title>tags on product pages — present and under 60 chars?<meta name="description">— populated?- Canonical tags — no duplicate content signals?
application/ld+json— Product structured data present?hreflang— if selling in multiple languages- Paginated collections —
/collections/all?page=2canonical handling
Phase B5 — Output Format
Same format as Mode A, but with a note on data confidence:
⚠️ Mode: Public extraction only — findings based on visible storefront.
Connect shopify-mcp for full catalog, order, and app audit.
Specific Audit Types
Speed Audit
Focus questions:
- How many apps installed? (each = 50-200ms)
- Are images on CDN? (Shopify CDN is automatic for uploaded images)
- Any render-blocking scripts in theme?
- Page builders installed? (PageFly, GemPages add 400-800ms)
Tool: Google PageSpeed on homepage + a product page + collection page. Target: > 50 mobile score. Below 30 = critical.
SEO Audit
See references/seo-shopify.md for Shopify-specific issues:
- Canonical tag conflicts from
/collections/+/products/dual URLs - Tag pages (
/collections/all/tag) getting indexed - Variant URLs creating duplicate content
- Pagination:
?page=2vs. infinite scroll SEO implications
Conversion Rate Audit
Focus on the funnel:
Homepage → Collection → Product → Cart → Checkout
For each step: what's the drop-off? What's missing that would build confidence?
Common CRO fixes:
- Product pages: no reviews visible above the fold
- Cart: no shipping estimate before checkout
- Checkout: no trust badges (SSL, payment icons)
- Mobile: add-to-cart button below the fold
App Stack Audit
See references/app-stack.md for full scoring.
Output: list of apps with estimated load impact, recommendation (keep/remove/replace).
Analytics Interpretation
If the user has GA4 connected and shares data:
Metrics that matter:
- Conversion rate: < 1% = critical, 1-3% = average, > 3% = good
- Cart abandonment: > 75% = fix checkout friction
- Bounce rate on product pages: > 60% = content or trust problem
- Mobile vs desktop conversion gap: > 2x = mobile UX issue
- Top exit pages: collection pages = navigation problem; product pages = trust/info problem
Metrics that don't matter (without context):
- Raw traffic (need conversion rate)
- Page views (need session duration + bounce)
- Social followers (need click-through rate)
Reference files:
references/audit-framework.md— full dimension checklistsreferences/seo-shopify.md— Shopify-specific SEO issuesreferences/product-optimization.md— title/description formulas, image standardsreferences/app-stack.md— app impact scoring and recommendationsreferences/mcp-queries.md— GraphQL queries for MCP mode
What ships with it: 5 files
30.1 KB alongside SKILL.md
references/
- app-stack.md5.9 KB
- audit-framework.md7.8 KB
- mcp-queries.md3.3 KB
- product-optimization.md7.3 KB
- seo-shopify.md5.9 KB
Gives 1 of the 12 instructions most audit compliance skills give in ~2.1k tokens
Counted across 937 of the 1,487 authors here whose files we hold, read 2026-08-07
- Fetch latest guidelines before each reviewin 43 of 937, across 3 files
- Group findings by severityin 43 of 937
- Check files against all fetched rulesin 42 of 937, across 2 files
- Output findings in terse file:line formatin 41 of 937, across 3 files
- Ask user which files to review if none specifiedin 41 of 937, across 3 files
- Read specified files or prompt user for filesin 39 of 937, across 1 file
- Generate the audit reportin 33 of 937, across 30 files
- Assign a severity to every findingin 25 of 937
- Run automated accessibility scansin 23 of 937, across 13 files
- Output a markdown audit reporthere, and in 22 of 937
- Map findings to WCAG criteriain 20 of 937, across 10 files
- Confirm audit scopein 19 of 937, across 9 files
Said here and by no other author read
- check if shopify mcp is available
- ask user for store url if mcp is unavailable
- collect store data sequentially
- flag more than eight apps as bloated
- identify duplicate functionality apps
- extract public storefront data when disconnected
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.