Runs a store-side "Agentic Commerce Readiness" scan — the same questions the public agentiq.report audit asks, but answered from inside the Shopify Admin with full catalog data. It scores whether AI shopping agents (ChatGPT, Gemini, Perplexity, agentic checkout) can FIND, READ, and RECOMMEND the store's products, then prints a prioritized gap list where every gap names the sibling agentic skill that fixes it. Read-only — it changes nothing. Use it first (and on a schedule) to decide which remediation skills to run.
Required API scopes: read_products, read_files, read_content (themes), read_online_store_pages
Parameters
All skills accept these universal parameters:
Parameter
Type
Required
Default
Description
store
string
yes
—
Store domain (e.g., mystore.myshopify.com)
format
string
no
human
Output format: human (default) or json
dry_run
bool
no
false
No-op here — this skill never mutates
Skill-specific parameters:
Parameter
Type
Required
Default
Description
sample_size
int
no
250
How many products to sample for the catalog-data checks
min_description_chars
int
no
120
Threshold below which a description counts as "thin"
Workflow Steps
OPERATION:shop — query
Inputs: none
Expected output: Shop name, primary domain, social sameAs links, and policy presence — feeds the identity + policy checks.
OPERATION: — query
, then
Whether the published theme allows AI crawlers (robots), ships an Organization JSON-LD block, and serves an llms.txt — feeds discovery + identity checks.
OPERATION:metafieldDefinitions — query
Inputs:ownerType: PRODUCTExpected output: Which structured attributes are defined (material, specs, features) — feeds the metafield-coverage check.
OPERATION:files — query
Inputs:first: 50, query: "media_type:IMAGE" (sample) — corroborate alt-text coverage at the file level.
Expected output: Alt-text fill rate across product media.
COMPUTE (no API): roll the findings into a 0–100 readiness score across five pillars — Discoverable (robots/llms.txt), Trusted (Organization schema, sameAs, policies), Readable (descriptions, alt text, JSON-LD fields), Structured (metafields, category, barcodes), Matchable (title/tag/metafield richness for intent) — and map each failing pillar to its fix skill.
GraphQL Operations
# shop:query — validated against api_version 2025-01query AgenticReadinessShop {
shop {
name
myshopifyDomain
primaryDomain { url }
contactEmail
shopPolicies {type body url }}}
# themes:query — validated against api_version 2025-01query AgenticReadinessTheme {
themes(first:1, roles:[MAIN]){
nodes {
id
name
files(filenames:["templates/robots.txt.liquid",
"layout/theme.liquid",
"assets/llms.txt",
"templates/llms.txt.liquid"]){
nodes {
filename
body {...on OnlineStoreThemeFileBodyText { content }}}}}}}
# metafieldDefinitions:query — validated against api_version 2025-01query AgenticReadinessMetafieldDefs {
metafieldDefinitions(first:100, ownerType: PRODUCT){
edges { node { namespace key name type{ name }}}}}
# products:query — validated against api_version 2025-01query AgenticReadinessProducts($first: Int!, $after: String){
products(first:$first, after:$after) {
edges {
node {
id
title
descriptionHtml
category { id fullName }
tags
media(first:10){
edges { node {...on MediaImage { id image { altText url }}}}}
metafields(first:20){ edges { node { namespace key value }}}
variants(first:100){
edges { node { id sku barcode price }}}}}
pageInfo { hasNextPage endCursor }}}
# files:query — validated against api_version 2025-01query AgenticReadinessFiles($first: Int!, $after: String){
files(first:$first, after:$after, query:"media_type:IMAGE"){
edges { node {...on MediaImage { id alt }}}
pageInfo { hasNextPage endCursor }}}
Session Tracking
Claude MUST emit the following output at each stage. This is mandatory.
A readiness scorecard. human: an overall 0–100 score + per-pillar bars (Discoverable / Trusted / Readable / Structured / Matchable) + a prioritized gap table where each row is gap → impact → the agentic skill to run. json: { score, grade, pillars{...}, gaps:[{ pillar, audit_signal, finding, fix_skill }], sampled_products }. Every fix_skill value is a sibling skill name (e.g. shopify-admin-agentic-image-alt-text) so the operator can chain straight into remediation.
Error Handling
Error
Cause
Recovery
THROTTLED
API rate limit
Wait 2s, retry up to 3 times
ACCESS_DENIED reading themes
Missing read_content scope
Skip the theme pillar, mark Discoverable/Trusted "unknown", continue
Empty catalog
New/empty store
Report "no products to assess"; still check theme + policies
Best Practices
Run this FIRST and re-run it after each remediation skill — it's the scoreboard that tells you what's left and what moved.
Sample, don't crawl: 250 products is enough to estimate fill rates; only audit the full catalog when the sample shows borderline pillars.
Treat category-unassigned and barcode-missing as the highest-leverage gaps — they unblock both AI retrieval (Matchable) and Product JSON-LD (Readable) at once.
This skill is read-only; it never needs dry_run. The skills it routes you to DO mutate — run each of those with dry_run: true first.
