Back to Audit and rewrite Shopify product data with Claude, Google Sheets, and Slack
Card snapshot
n8n-workflow-21507.n8n.io
·
2026-10-25 14:37:37 UTC
·
eaaf7c3c18a8bee02873cd0dd0fce14259a61b113f95417c1dd39444c4e35715
This is a frozen copy of the agent's agent-card.json as we observed it at the timestamp above. We capture a new snapshot every time the card's content hash changes. Useful for: forensic drift analysis, verifying downstream callers see the right version, reproducing routing decisions made historically.
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]
}
}
},
"group": "[\"input\"]",
"defaults": {
"name": "Sticky Note",
"color": "#FFD233"
},
"iconData": {
"icon": "sticky-note",
"type": "icon"
},
"displayName": "Sticky Note",
"typeVersion": 1,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 834,
"icon": "file:code.svg",
"name": "n8n-nodes-base.code",
"codex": {
"data": {
"alias": [
"cpde",
"Javascript",
"JS",
"Python",
"Script",
"Custom Code",
"Function"
],
"details": "The Code node allows you to execute JavaScript in your workflow.",
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/"
}
]
},
"categories": [
"Development",
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Helpers",
"Data Transformation"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Code"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iNTEyIiBoZWlnaHQ9IjUxMiIgdmlld0JveD0iMCAwIDUxMiA1MTIiIGZpbGw9Im5vbmUiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+CjxnIGNsaXAtcGF0aD0idXJsKCNjbGlwMF8xMTcxXzQ0MSkiPgo8cGF0aCBkPSJNMTcwLjI4MyA0OEgxOTYuNUMyMDMuMTI3IDQ4IDIwOC41IDQyLjYyNzQgMjA4LjUgMzZWMTJDMjA4LjUgNS4zNzI1OCAyMDMuMTI3IDAgMTk2LjUgMEgxNzAuMjgzQzEyNi4xIDAgOTAuMjgzIDM1LjgxNzIgOTAuMjgzIDgwVjE3NkM5MC4yODMgMjA2LjkyOCA2NS4yMTA5IDIzMiAzNC4yODMgMjMySDIzQzE2LjM3MjYgMjMyIDExIDIzNy4zNzIgMTEgMjQ0VjI2OEMxMSAyNzQuNjI3IDE2LjM3MjQgMjgwIDIyLjk5OTYgMjgwTDM0LjI4MyAyODBDNjUuMjEwOSAyODAgOTAuMjgzIDMwNS4wNzIgOTAuMjgzIDMzNlY0NDBDOTAuMjgzIDQ3OS43NjQgMTIyLjUxOCA1MTIgMTYyLjI4MyA1MTJIMTk2LjVDMjAzLjEyNyA1MTIgMjA4LjUgNTA2LjYyNyAyMDguNSA1MDBWNDc2QzIwOC41IDQ2OS4zNzMgMjAzLjEyNyA0NjQgMTk2LjUgNDY0SDE2Mi4yODNDMTQ5LjAyOCA0NjQgMTM4LjI4MyA0NTMuMjU1IDEzOC4yODMgNDQwVjMzNkMxMzguMjgzIDMwOS4wMjIgMTI4LjAxMSAyODQuNDQzIDExMS4xNjQgMjY1Ljk2MUMxMDYuMTA5IDI2MC40MTYgMTA2LjEwOSAyNTEuNTg0IDExMS4xNjQgMjQ2LjAzOUMxMjguMDExIDIyNy41NTcgMTM4LjI4MyAyMDIuOTc4IDEzOC4yODMgMTc2VjgwQzEzOC4yODMgNjIuMzI2OSAxNTIuNjEgNDggMTcwLjI4MyA0OFoiIGZpbGw9IiNGRjk5MjIiLz4KPHBhdGggZD0iTTMwNSAzNkMzMDUgNDIuNjI3NCAzMTAuMzczIDQ4IDMxNyA0OEgzNDIuOTc5QzM2MC42NTIgNDggMzc0Ljk3OCA2Mi4zMjY5IDM3NC45NzggODBWMTc2QzM3NC45NzggMjAyLjk3OCAzODUuMjUxIDIyNy41NTcgNDAyLjA5OCAyNDYuMDM5QzQwNy4xNTMgMjUxLjU4NCA0MDcuMTUzIDI2MC40MTYgNDAyLjA5OCAyNjUuOTYxQzM4NS4yNTEgMjg0LjQ0MyAzNzQuOTc4IDMwOS4wMjIgMzc0Ljk3OCAzMzZWNDMyQzM3NC45NzggNDQ5LjY3MyAzNjAuNjUyIDQ2NCAzNDIuOTc5IDQ2NEgzMTdDMzEwLjM3MyA0NjQgMzA1IDQ2OS4zNzMgMzA1IDQ3NlY1MDBDMzA1IDUwNi42MjcgMzEwLjM3MyA1MTIgMzE3IDUxMkgzNDIuOTc5QzM4Ny4xNjEgNTEyIDQyMi45NzggNDc2LjE4MyA0MjIuOTc4IDQzMlYzMzZDNDIyLjk3OCAzMDUuMDcyIDQ0OC4wNTEgMjgwIDQ3OC45NzkgMjgwSDQ5MEM0OTYuNjI3IDI4MCA1MDIgMjc0LjYyOCA1MDIgMjY4VjI0NEM1MDIgMjM3LjM3MyA0OTYuNjI4IDIzMiA0OTAgMjMyTDQ3OC45NzkgMjMyQzQ0OC4wNTEgMjMyIDQyMi45NzggMjA2LjkyOCA0MjIuOTc4IDE3NlY4MEM0MjIuOTc4IDM1LjgxNzIgMzg3LjE2MSAwIDM0Mi45NzkgMEgzMTdDMzEwLjM3MyAwIDMwNSA1LjM3MjU4IDMwNSAxMlYzNloiIGZpbGw9IiNGRjk5MjIiLz4KPC9nPgo8ZGVmcz4KPGNsaXBQYXRoIGlkPSJjbGlwMF8xMTcxXzQ0MSI+CjxyZWN0IHdpZHRoPSI1MTIiIGhlaWdodD0iNTEyIiBmaWxsPSJ3aGl0ZSIvPgo8L2NsaXBQYXRoPgo8L2RlZnM+Cjwvc3ZnPgo="
},
"displayName": "Code",
"typeVersion": 2,
"nodeCategories": [
{
"id": 5,
"name": "Development"
},
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 839,
"icon": "fa:clock",
"name": "n8n-nodes-base.scheduleTrigger",
"codex": {
"data": {
"alias": [
"Time",
"Scheduler",
"Polling",
"Cron",
"Interval"
],
"resources": {
"generic": [],
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.scheduletrigger/"
}
]
},
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0"
}
},
"group": "[\"trigger\",\"schedule\"]",
"defaults": {
"name": "Schedule Trigger",
"color": "#31C49F"
},
"iconData": {
"icon": "clock",
"type": "icon"
},
"displayName": "Schedule Trigger",
"typeVersion": 1,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 1119,
"icon": "fa:robot",
"name": "@n8n/n8n-nodes-langchain.agent",
"codex": {
"data": {
"alias": [
"LangChain",
"Chat",
"Conversational",
"Plan and Execute",
"ReAct",
"Tools"
],
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Agents",
"Root Nodes"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "AI Agent",
"color": "#404040"
},
"iconData": {
"icon": "robot",
"type": "icon"
},
"displayName": "AI Agent",
"typeVersion": 3,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1145,
"icon": "file:anthropic.svg",
"name": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"codex": {
"data": {
"alias": [
"claude",
"sonnet",
"opus"
],
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatanthropic/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Language Models",
"Root Nodes"
],
"Language Models": [
"Chat Models (Recommended)"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Anthropic Chat Model"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI0NiIgaGVpZ2h0PSIzMiIgZmlsbD0ibm9uZSI+PHBhdGggZmlsbD0iIzdEN0Q4NyIgZD0iTTMyLjczIDBoLTYuOTQ1TDM4LjQ1IDMyaDYuOTQ1ek0xMi42NjUgMCAwIDMyaDcuMDgybDIuNTktNi43MmgxMy4yNWwyLjU5IDYuNzJoNy4wODJMMTkuOTI5IDB6bS0uNzAyIDE5LjMzNyA0LjMzNC0xMS4yNDYgNC4zMzQgMTEuMjQ2eiIvPjwvc3ZnPg=="
},
"displayName": "Anthropic Chat Model",
"typeVersion": 2,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1179,
"icon": "fa:code",
"name": "@n8n/n8n-nodes-langchain.outputParserStructured",
"codex": {
"data": {
"alias": [
"json",
"zod"
],
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.outputparserstructured/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Output Parsers"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Structured Output Parser"
},
"iconData": {
"icon": "code",
"type": "icon"
},
"displayName": "Structured Output Parser",
"typeVersion": 1,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
}
]
},
"detail": {
"description": "Quick overview This workflow runs every Monday to audit published Shopify products, compare each live product page\u2019s JSON-LD data with Shopify, log per-product scores to Google Sheets, post a summary to Slack, and use Anthropic Claude to propose description rewrites that only update Shopify after Slack approval. How it works 1. Runs every Monday morning and validates the required configuration values (store URL, Google Sheet, and Slack channel). 2. Fetches published products from Shopify (up to the configured limit) and checks core catalog data like description length, barcodes/GTINs, product type, image alt text, and title. 3. Loads each public product page via HTTP, parses JSON-LD structured data, and flags issues like missing Product/Offer data or mismatched price and availability compared to Shopify. 4. Calculates a score out of 100 per product, then appends a detailed row for each product to a Google Sheets tab. 5. Builds and posts a Slack summary with the average score, most common problems, lowest-scoring products, and a link to the Google Sheet report. 6. Selects a small set of products with short descriptions, asks Anthropic Claude to rewrite each description, and programmatically rejects rewrites that add unsupported numbers, facts, claims, or disallowed HTML. 7. Sends passing rewrites to Slack with Approve/Decline buttons and updates the product description in Shopify only when someone approves within the configured wait period. Setup 1. Create or connect Shopify OAuth2 credentials with readproducts and writeproducts scopes, and select them for the Shopify nodes that read products and update descriptions. 2. Add Google Sheets OAuth credentials, create a spreadsheet, and set its URL and the target tab name (for example, \u201cAI readiness\u201d) in the Config values. 3. Add Slack OAuth credentials, invite the Slack app to your target channel, and set the channel ID in the Config values. 4. Add Anthropic credentials (or n8n AI credits) and confirm the Claude model selection used for generating rewritten descriptions. 5. Replace the placeholder values in the Config node (storeUrl, reportSheetUrl, slackChannelId, and any limits/thresholds) before enabling the schedule. Requirements - A Shopify store with public product pages (live checks are skipped on password-protected stores) - Google Sheets and Slack - An Anthropic API key or n8n AI credits Customization - In Config, change how many products are checked and rewritten, the minimum description length and the approval wait. - Change the points per problem in Check Product Data and Compare Live Page With Shopify. - Add words to the claims list in Check Rewrite Against Product. Additional info Who is it for: Shopify store owners and e-commerce agencies who want products described correctly when shoppers ask ChatGPT, Gemini or Google AI Mode what to buy. These assistants rely on product data and on the structured data in each product page."
}
}