# N8nTemplate ProductInfo Scraper (`getdataforme/n8ntemplate-productinfo-scraper`) Actor The N8nTemplate ProductInfo Scraper efficiently extracts detailed workflow data from n8n.io, offering insights for analysis, research, or integration. Features include quick data extraction, comprehensive output, reliable performance, and customizable exports in JSON, CSV, or Excel formats. - **URL**: https://apify.com/getdataforme/n8ntemplate-productinfo-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## N8nTemplate ProductInfo Scraper ### Introduction The N8nTemplate ProductInfo Scraper is designed to efficiently extract detailed workflow information from n8n.io, providing users with comprehensive insights into various automation processes. This Actor is ideal for users seeking to gather data for analysis, research, or integration into their own systems. ### Features - **Efficient Data Extraction**: Quickly scrape detailed workflow information from n8n.io. - **Comprehensive Output**: Provides detailed descriptions, categories, ratings, and creator information. - **Reliable Performance**: Built on Apify's robust infrastructure for consistent data retrieval. - **User-Friendly Setup**: Simple configuration with start URLs for targeted scraping. - **Versatile Use Cases**: Suitable for market research, competitive analysis, and business automation. - **Customizable Output**: Easily export data in JSON, CSV, or Excel formats. - **Secure and Verified**: Ensures data integrity and security with verified sources. ### Input Parameters | Parameter | Type | Required | Description | Example | |------------|--------|----------|------------------------|-------------------------------------------------------------------------| | startUrls | array | Yes | URLs to start with. | `[{"url": "https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/"}]` | ### Example Usage #### Example Input JSON ```json { "startUrls": [ { "url": "https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/" } ] } ```` #### Example Output JSON ```json [ { "workflow": { "id": 5691, "name": "Generate personalized sales emails with LinkedIn data & Claude 3.7 via OpenRouter", "description": "How it worksThe automation loads rows from a Google Sheet of leads that you want to contact.It makes a Google search via Apify for LinkedIn links based on...", "full_description": "How it works The automation loads rows from a Google Sheet of leads that you want to contact. It makes a Google search via Apify for LinkedIn links based on the First name / Last name / Company. Another Apify actor fetches the right LinkedIn profile based on the first profile which is retuned The same process is done for the company that the lead works for, giving extra context. If the lead has a current company listed on their LinkedIn, we use that URL to do the lookup, rather than doing a separate Google search. A call is made to OpenRouter to get an LLM to generate an email based on a prompt designed to do personalized outreach. An email is sent via a Gmail node. Set up steps Connect your Google Sheets + Gmail accounts to use these APIs. Make an account with Apify and enter your credentials. Set your details in the \"Set My Data\" node to customize the workflow to revolve around your company + value proposition. I would recommend changing the prompt in the \"Generate Personalized Email\" node to match the tone of voice that you want your agent to have. You can change the guidelines to e.g. change whether the agent introduces itself, and give more examples in the style you want to make the output better.", "total_views": 0, "last_update": "Last update 2 months ago", "last_update_date": "2026-02-23T08:04:07.588Z", "created_at": null, "categories": [ "Lead Nurturing", "Multimodal AI" ], "rating": { "score": 4.6, "count": 27, "best": 5 }, "url": "https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/" }, "creator": { "name": "Adam Janes", "username": "", "profile_url": "", "avatar_url": "https://gravatar.com/avatar/1ffac81a3c36347b065a27a6a4d489c7d2c6fe52ac17d3fe9e6ff89db9d9ce6a?r=pg&d=retro&size=200", "is_verified": true, "bio": null }, "nodes": { "displayed_nodes": [ { "name": "google-sheets", "display_name": "Google Sheets", "url": "https://n8n.io/integrations/google-sheets/" }, { "name": "http-request", "display_name": "HTTP Request", "url": "https://n8n.io/integrations/http-request/" }, { "name": "if", "display_name": "If", "url": "https://n8n.io/integrations/if/" } ], "total_displayed_count": 3, "additional_nodes_count": 10, "total_nodes_count": 13, "node_types": [], "node_details": [], "connections_count": 0, "has_credentials": false }, "sharing": { "twitter_url": "https://x.com/intent/post?url=https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/", "linkedin_url": "https://www.linkedin.com/sharing/share-offsite/?url=https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/" }, "scraped_at": "2026-04-23T08:04:07.596Z", "source_url": "https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather insights on workflow trends and automation processes. - **Competitive Intelligence**: Analyze competitor workflows and strategies. - **Price Monitoring**: Track changes in workflow offerings and pricing. - **Content Aggregation**: Compile workflow data for content creation and distribution. - **Academic Research**: Study automation techniques and their applications. - **Business Automation**: Integrate workflow data into business processes for enhanced automation. ### Installation and Usage ``` 1. Search for "N8nTemplate ProductInfo Scraper" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 4. Click "Start" to begin extraction 5. Monitor progress in the log 6. Export results in your preferred format (JSON, CSV, Excel) ``` ### Output Format The output data includes detailed workflow information such as workflow ID, name, description, categories, ratings, creator details, nodes used, and sharing links. This structured data allows for easy integration and analysis. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! # Actor input Schema ## `startUrls` (type: `array`): URLs to start with. ## Actor input object example ```json { "startUrls": [ { "url": "https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/" } ] } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ { "url": "https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/n8ntemplate-productinfo-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrls": [{ "url": "https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/" }] } # Run the Actor and wait for it to finish run = client.actor("getdataforme/n8ntemplate-productinfo-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://n8n.io/workflows/5691-generate-personalized-sales-emails-with-linkedin-data-and-claude-37-via-openrouter/" } ] }' | apify call getdataforme/n8ntemplate-productinfo-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/n8ntemplate-productinfo-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "N8nTemplate ProductInfo Scraper", "description": "The N8nTemplate ProductInfo Scraper efficiently extracts detailed workflow data from n8n.io, offering insights for analysis, research, or integration. Features include quick data extraction, comprehensive output, reliable performance, and customizable exports in JSON, CSV, or Excel formats.", "version": "0.0", "x-build-id": "oTdc8282tXoiGC3Jx" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~n8ntemplate-productinfo-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-n8ntemplate-productinfo-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~n8ntemplate-productinfo-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-n8ntemplate-productinfo-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~n8ntemplate-productinfo-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-n8ntemplate-productinfo-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "URLs to start with.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```