Data processing and analytics
# Track n8n Node Definitions from GitHub and Export to Google Sheets ## Overview This workflow automatically retrieves and processes metadata from the official n8n GitHub repository, filters all available `.node.json` files, parses their structure, and appends structured information into a Google Sheet. Perfect for developers, community managers, and technical writers who need to maintain up-to-date information about n8n's evolving node ecosystem. ## Setup Instructions ### Prerequisites Before setting up this workflow, ensure you have: - A GitHub account with API access - A Google account with Google Sheets access - An active n8n instance (cloud or self-hosted) ### Step 1: GitHub API Configuration 1. Navigate to GitHub Settings → Developer Settings → Personal Access Tokens 2. Generate a new token with `public_repo` permissions 3. Copy the generated token and store it securely 4. In n8n, create a new GitHub API credential 5. Paste your token in the credential configuration and save ### Step 2: Google Sheets Setup 1. Create a new Google Sheets document 2. Set up the following column headers in the first row: - `node` (Column A) - Node identifier/name - `nodeVersion` (Column B) - Version of the node - `codexVersion` (Column C) - Codex version number - `categories` (Column D) - Node categories - `credentialDocumentation` (Column E) - Credential documentation URL - `primaryDocumentation` (Column F) - Primary documentation URL 3. Note down the Google Sheets document ID from the URL 4. Configure Google Sheets OAuth2 credentials in n8n ### Step 3: Workflow Configuration 1. Import the workflow into your n8n instance 2. Update the following placeholder values: - Replace `YOUR_GOOGLE_SHEETS_DOCUMENT_ID` with your actual document ID - Replace `YOUR_WEBHOOK_ID` if using webhook functionality 3. Configure the GitHub API credentials in the HTTP Request nodes 4. Set up Google Sheets credentials in the Google Sheets nodes 5. Share your Google Sheets document with the email address associated with your Google OAuth2 credentials 6. Grant Editor permissions to allow the workflow to write data ## Google Sheets Template Details The workflow creates a structured dataset with these columns: - **node**: Node identifier (e.g., `n8n-nodes-base.slack`) - **nodeVersion**: Version of the node (e.g., `1.0.0`) - **codexVersion**: Codex version number (e.g., `1.0.0`) - **categories**: Node categories (e.g., `Communication, Productivity`) - **credentialDocumentation**: URL to credential documentation - **primaryDocumentation**: URL to primary node documentation ## Customization Options ### Modifying Data Extraction You can customize the Format Data node to extract additional fields: - Add new assignments in the Set node - Modify the column mapping in the Google Sheets node - Update your spreadsheet headers accordingly ### Changing Update Frequency To run this workflow on a schedule: 1. Replace the Manual Trigger with a Cron node 2. Set your desired schedule (e.g., daily, weekly) 3. Configure appropriate timing to avoid API rate limits ### Adding Filters Customize the Filter Node Files code node to: - Filter specific node types - Include/exclude certain categories - Process only recently updated nodes ## Features - Fetches all node definitions from the `n8n-io/n8n` repository - Filters for `.node.json` files only - Downloads and parses metadata automatically - Extracts key fields like node names, versions, categories, and documentation URLs - Appends structured data to Google Sheets with batch processing - Includes error handling and retry mechanisms - Clears existing data before appending new information for fresh results ## Use Cases This workflow is ideal for: - Tracking changes in official n8n node definitions over time - Auditing node categories and documentation links for completeness - Building custom dashboards from node metadata - Community management and documentation maintenance - Integration planning and compatibility analysis
The template allows making Dropcontact batch requests up to 250 requests every 10 minutes (1500/hour). Valuable if high volume email enrichment is expected. Dropcontact will look for email & basic email qualification if first_name, last_name, company_name is provided. --- **Step 1: Node Profiles Query** Connect your own source (Airtable, Google Sheets, Supabase,...); the template is using Postgres by default. **Note I:** Be careful your source is only returning a maximum of 250 items. **Note II:** The next node uses the following variables, make sure you can map these from your source file: - first_name - last_name - website (company_name would work too) - full_name (see note) **Note III:** This template is using the Dropcontact Batch API, which works in a POST & GET setup. Not a GET request only to retrieve data, as Dropcontact needs to process the batch data load properly. --- **Step 2: Node Data Transformation** Will transform the input variables into the proper JSON format. This JSON format is expected from the Dropcontact API to make a batch request. full_name is being used as a custom identifier to update the returned email to the proper contact in your source database. To make things easy, use a unique identifier in the full_name variable. --- **Step 3: Node: Bulk Dropcontact Requests.** Enter your Dropcontact credentials in the node: Bulk Dropcontact Requests. --- **Step 4: Connect your output source by mapping the data you like to use.** --- **Step 5: Node: Slack (OPTIONAL)** Connect your Slack account, if an error occurs, you will be notified. **TIP:** Try to run the workflow with a batch of 10 (not 250) as it might need to run initially before you will be able to map the data to your final destination. Once the data fields are properly mapped, adjust back to 250.
This n8n workflow starts with a manual trigger, then uses a function node to parse a provided URL and extract any email address found within it. It’s a practical tool for sales teams, recruiters, or researchers who need to quickly pull contact details from web pages without manual copying.
This workflow automates the process of downloading an XML file, parsing its contents, and storing the structured data in a Google Sheets spreadsheet.
Automatically monitor the freshness of data in Postgres tables and receive email alerts if data becomes stale beyond a configurable threshold.
### Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client.  ### Who this is for? The Extract, Transform LinkedIn Data with Bright Data MCP Server & Google Gemini workflow is an automated solution that scrapes LinkedIn content via Bright Data MCP Server then transforms the response using a Gemini LLM. The final output is sent via webhook notification and also persisted on disk. This workflow is tailored for: 1. **Data Analysts**: Who require structured LinkedIn datasets for analytics and reporting. 2. **Marketing and Sales Teams**: Looking to enrich lead databases, track company updates, and identify market trends. 3. **Recruiters and Talent Acquisition Specialists**: Who want to automate candidate sourcing and company research. 4. **AI Developers**: Integrating real-time professional data into intelligent applications. 5. **Business Intelligence Teams**: Needing current and comprehensive LinkedIn data to drive strategic decisions. ### What problem is this workflow solving? Gathering structured and meaningful information from the web is traditionally slow, manual, and error-prone. This workflow solves: 1. Reliable web scraping using Bright Data MCP Server LinkedIn tools. 2. LinkedIn person and company web scraping with AI Agents setup with the Bright Data MCP Server tools. 3. Data extraction and transformation with Google Gemini LLM. 4. Persists the LinkedIn person and company info to disk. 5. Performs a Webhook notification with the LinkedIn person and company info. ### What this workflow does? This n8n workflow performs the following steps: 1. **Trigger**: Start manually. 2. **Input URL(s)**: Specify the LinkedIn person and company URL. 3. **Web Scraping (Bright Data)**: Use Bright Data's MCP Server, LinkedIn tools for the person and company data extract. 4. **Data Transformation & Aggregation**: Uses the Google LLM for handling the data transformation. 5. **Store / Output**: Save results into disk and also performs a Webhook notification. ### Pre-conditions 1. Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - [model-context-protocol](https://www.anthropic.com/news/model-context-protocol) 2. You need to have the [Bright Data](https://brightdata.com/) account and do the necessary setup as mentioned in the **Setup** section below. 3. You need to have the Google Gemini API Key. Visit [Google AI Studio](https://aistudio.google.com/) 4. You need to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp) 5. You need to install the [n8n-nodes-mcp](https://github.com/nerding-io/n8n-nodes-mcp) ### Setup 1. Please make sure to set up n8n locally with MCP Servers by navigating to [n8n-nodes-mcp](https://github.com/nerding-io/n8n-nodes-mcp) 2. Please make sure to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp) on your local machine. 3. Sign up at [Bright Data](https://brightdata.com/). 4. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. 5. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. 6. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). 7. In n8n, configure the credentials to connect with MCP Client (SDIO) account with the Bright Data MCP Server as shown below.  Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>. 8. Update the LinkedIn URL person and company workflow. 9. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. 10. Update the file name and path to persist on disk. ### How to customize this workflow to your needs 1. **Different Inputs**: Instead of static URLs, accept URLs dynamically via webhook or form submissions. 2. **Data Extraction**: Modify the **LinkedIn Data Extractor** node with the suitable prompt to format the data as you wish. 3. **Outputs**: Update the Webhook endpoints to send the response to Slack channels, Airtable, Notion, CRM systems, etc.
Effortlessly sync member and activity data from Google Sheets to Orbit, ensuring your community metrics are always up-to-date.
Efficiently extract and structure comments from X posts using Airtop and n8n, saving time and enhancing data analysis capabilities.
This workflow looks for a Close Date value using REGEX in the IF node. If it finds the correct value, it will pass that value on. If it does not find the correct value, it will generate a value based on the present time plus three weeks. The final result will show up in the NoOps node. You can test this execution by enabling and disabling the Set node when you run the execution.
Extract data from a webpage (Ycombinator news page) and create a nice list using the itemList node. It seems that the current version in n8n (0.141.1) requires extracting each variable one by one. Hopefully, in the future, it will be possible to create the table using just one itemList node. Another nice feature of the workflow is an automatically generated file name with the resulting table. Check out the fileName option of the Spreadsheet File node: ``` Ycombinator_news_{{new Date().toISOString().split('T', 1)[0]}}.{{$parameter['fileFormat']}} ``` The resulting table is saved as an .xls file and delivered via email. 
This workflow is designed to validate and fetch information about a card using the BIN code. It utilizes apilayers BIN Check API and provides details like the card brand, type, issuing bank, and country. Prerequisites: - An apilayer account - API Key for the BIN Check API --- Steps in n8n: **Step 1: Manual Trigger** - Node Type: Manual Trigger - Purpose: Starts the workflow manually **Step 2: Set BIN Code and API Key** - Node Type: Set - Fields to set: - bin_code: A sample BIN like JH4KA7560RC003647 - apikey: Your apilayer API key **Step 3: HTTP Request** - Node Type: HTTP Request - Method: GET - URL: https://api.apilayer.com/bincheck/{{ $json.bin_code }} - Headers: - Name: apiKey - Value: {{ $json.apikey }} **(Optional) Step 4: Handle the Output** - Add nodes to store, parse, or visualize the API response. --- Expected Output: The response from apilayer contains detailed information about the provided BIN: - Card scheme (e.g., VISA, MasterCard) - Type (credit, debit, prepaid) - Issuing bank - Country of issuance --- Example Use Case: Use this to build a fraud prevention microservice, pre-validate card data before sending to payment gateways, or enrich card-related logs.
This workflow retrieves the first and last names of a user from the Facebook Graph API, providing a simple way to access user information.
This n8n workflow implements an exponential backoff strategy to manage retries for Google API requests, helping to handle transient errors effectively.
This workflow demonstrates the use of the Split In Batches node and the Wait node to avoid API rate limits. **Customer Datastore node:** The workflow fetches data from the Customer Datastore node. Based on your use case, replace it with a relevant node. **Split In Batches node:** This node splits the items into a single item. Based on the API limit, you can configure the Batch Size. **HTTP Request node:** This node makes API calls to a placeholder URL. If the Split In Batches node returns 5 items, the HTTP Request node will make 5 different API calls. **Wait node:** This node will pause the workflow for the time you specify. On resume, the Split In Batches node gets executed, and the next batch is processed. **Replace Me (NoOp node):** This node is optional. If you want to continue your workflow and process the items, replace this node with the corresponding node(s).
This tutorial demonstrates the creation of the HTML report via Markdown node. The main idea is to prepare a very long text variable via the Function Node and then convert it to the HTML file. The resulting report can be downloaded from the workflow canvas directly or sent via email as an attachment. 
Automatically fetch and analyze workflows in your n8n instance to identify AI models used, and document the findings in Google Sheets.
This workflow ensures seamless synchronization of contacts between Pipedrive and HubSpot, updating each platform with unique entries from the other.
This n8n workflow efficiently fetches Airtable records and their related child records up to three levels deep, organizing them into a structured JSON format. It optimizes API calls and allows for flexible configuration of data retrieval.
This workflow automates the process of downloading a CSV file from the web, parsing its data, and inserting it into a Snowflake database. It ensures that the data is correctly mapped to the database columns before insertion.
Automatically sync GitHub issues with your Notion database. This workflow updates the Notion database whenever an issue is opened, edited, closed, or deleted in GitHub, ensuring your project management is always up-to-date.
This workflow automates the process of sending the International Space Station's location data to Google BigQuery every minute, ensuring real-time tracking and data analysis.
This workflow compares two SQL datasets from different time periods, generating a detailed report on similarities and differences.
Effortlessly manage and analyze your YouTube channel content by syncing video data with Airtable, enabling comprehensive analytics and bulk updates.
Set up an MCP server to expose YouTube Reporting API operations to AI agents, enabling seamless data retrieval and management.
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