Extract Google Trends keywords & summarize articles in Google Sheets
Stay ahead of trends by automating your content research. This workflow fetches trending keywords from Google Trends RSS, extracts key insights from top articles, and saves structured summaries in Google Sheets—helping you build a data-driven editorial plan effortlessly.
How it works
- Fetch Google Trends RSS – The workflow retrieves trending keywords along with three related article links.
- Extract & Process Content – It fetches the content of these articles, cleans the HTML, and generates a concise summary using Jina AI.
- Store in Google Sheets – The processed insights, including the trending keyword and summary, are saved in a pre-configured Google Sheet.
Setup Steps
- Prepare a Google Sheet – Ensure you have a Google Sheet ready to store the extracted data.
- Configure API Access – Set up Google Sheets API and any required authentication.
- Get Jina.ai API key
- Adjust Workflow Settings – A dedicated configuration node allows you to fine-tune how data is processed and stored.
Customization
- Modify the RSS source to focus on specific Google Trends regions or categories.
- Adjust the content processing logic to refine how article summaries are created.
- Expand the workflow to integrate with CMS (e.g., WordPress) for automated content planning.
This workflow is ideal for content strategists, SEO professionals, and news publishers who want to quickly identify and act on trending topics without manual research. 🚀
Google Sheets Fields
Copy and paste these column headers into your Google Sheet:
| Column Name | Description |
|------------------------|-------------|
| status | Initial status of the keyword (e.g., "idea") |
| trending_keyword | Trending keyword extracted from Google Trends |
| approx_traffic | Estimated traffic for the trending keyword |
| pubDate | Date when the keyword was fetched |
| news_item_url1 | URL of the first related news article |
| news_item_title1 | Title of the first news article |
| news_item_url2 | URL of the second related news article |
| news_item_title2 | Title of the second news article |
| news_item_url3 | URL of the third related news article |
| news_item_title3 | Title of the third news article |
| news_item_picture1 | Image URL from the first news article |
| news_item_source1 | Source of the first news article |
| news_item_picture2 | Image URL from the second news article |
| news_item_source2 | Source of the second news article |
| news_item_picture3 | Image URL from the third news article |
| news_item_source3 | Source of the third news article |
| abstract | AI-generated summary of the articles (limited to 49,999 characters) |
Instructions
- Open Google Sheets and create a new spreadsheet.
- Copy the column names from the table above.
- Paste them into the first row of your Google Sheet.
n8n Workflow: Extract Google Trends Keywords & Summarize Articles in Google Sheets
This n8n workflow automates the process of fetching Google Trends keywords for a specified topic, then retrieving and summarizing related articles, finally publishing the results to a Google Sheet.
What it does
This workflow performs the following steps:
- Triggers Manually: The workflow is initiated by a manual trigger, allowing on-demand execution.
- Fetches Google Trends Data: It makes an HTTP request to the Google Trends API to retrieve trending search queries.
- Parses XML Data: The raw XML response from the Google Trends API is parsed into a structured JSON format.
- Extracts Keywords: A Code node extracts the relevant keywords from the parsed Google Trends data.
- Loops Over Keywords: It iterates through each extracted keyword to process them individually.
- Constructs Search URLs: For each keyword, it constructs a Google Search URL to find related articles.
- Fetches Search Results: It performs an HTTP request to Google Search (likely via a scraping API or similar) to get article links.
- Filters Valid URLs: An If node checks if a valid article URL was found.
- (If URL Found) Summarizes Article: If a URL is present, it makes another HTTP request to a summarization service (e.g., an LLM API) to get a summary of the article content.
- (If No URL) Skips Summarization: If no valid URL is found, it proceeds without summarization.
- Formats Data for Google Sheets: A Set node prepares the extracted keyword, article URL, and summary (if available) into a format suitable for Google Sheets.
- Appends to Google Sheet: Finally, it appends the processed data as a new row to a specified Google Sheet.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running n8n instance.
- Google Sheets Account: Access to a Google Sheets document where the data will be stored.
- Google API Key / Credentials: Credentials for Google Sheets to allow n8n to write data.
- Google Trends API Access: Although not explicitly shown as a separate credential, the HTTP Request node implies access to Google Trends data. This might be a public endpoint or require specific API keys.
- Web Scraping/Search API: The workflow uses an HTTP Request node to fetch search results, implying the need for a web scraping service or a Google Search API that can return article links.
- Article Summarization API: The workflow uses an HTTP Request node to summarize articles, implying the need for an external summarization service (e.g., an OpenAI API key for GPT, or another LLM).
Setup/Usage
- Import the Workflow: Download the provided JSON and import it into your n8n instance.
- Configure Credentials:
- Set up your Google Sheets credentials in n8n.
- Configure any necessary API keys or credentials for the Google Trends API, Web Scraping/Search API, and Article Summarization API within the respective HTTP Request nodes.
- Update Google Sheets Node: In the "Google Sheets" node, specify the Spreadsheet ID and Sheet Name where you want the data to be written.
- Review HTTP Request Nodes:
- Google Trends HTTP Request: Ensure the URL and any parameters are correctly configured for the Google Trends data you wish to fetch.
- Search Results HTTP Request: Adjust the URL and parameters to match your chosen web scraping or Google Search API.
- Summarization HTTP Request: Configure the URL, headers, and body for your chosen summarization service. This will likely involve passing the article content to the API.
- Activate the Workflow: Enable the workflow in n8n.
- Execute Manually: Click "Execute Workflow" on the "When clicking ‘Execute workflow’" node to run it on demand.
This workflow provides a powerful way to stay on top of trending topics and quickly gather summarized information from related articles, all within a structured Google Sheet.
Related Templates
Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets
This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.
Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax
Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions
Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation
PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.