Sync Amazon Luna Prime games to Google Sheets with automatic updates
Amazon Luna Prime Games Catalog Tracker (Auto-Sync to Google Sheets)**
Automatically fetch, organize, and maintain an updated catalog of Amazon Luna – Included with Prime games.This workflow regularly queries Amazon’s official Luna endpoint, extracts complete metadata, and syncs everything into Google Sheets without duplicates.
Ideal for:
-
tracking monthly Prime Luna rotations
-
keeping a personal archive of games
-
monitoring new games appearing on Amazon Games / Prime Gaming, so you can instantly play titles you’re interested in
-
building dashboards or gaming databases
-
powering notification systems (Discord, Telegram, email, etc.)
Overview
Amazon Luna’s “Included with Prime” lineup changes frequently, with new games added and old ones removed.Instead of checking manually, this n8n template fully automates the process:
-
Fetches the latest list from Amazon’s backend
-
Extracts detailed metadata from the response
-
Syncs the data into Google Sheets
-
Avoids duplicates by updating existing rows
-
Supports all major Amazon regions
Once configured, it runs automatically—keeping your game catalog correct, clean, and always up to date.
🛠️ How the workflow works
1. Scheduled Trigger
Starts the workflow on a set schedule (default: every 5 days at 3:00 PM).You can change both frequency and time freely.
2. HTTP Request to Amazon Luna
Calls Amazon Luna’s regional endpoint and retrieves the full “Included with Prime” catalog.
3. JavaScript Code Node – Data Extraction
Parses the JSON response and extracts structured fields:
-
Title
-
Genres
-
Release Year
-
ASIN
-
Image URLs
-
Additional metadata
The result is a clean, ready-to-use dataset.
4. Google Sheets – Insert or Update Rows
Each game is written into the selected Google Sheet:
-
Existing games get updated
-
New games are appended
The Title acts as the unique identifier to prevent duplicates.
## ⚙️ Configuration Parameters
| Parameter | Description | Recommended values |
| --- | --- | --- |
| x-amz-locale | Language + region | it_IT 🇮🇹 · en_US 🇺🇸 · de_DE 🇩🇪 · fr_FR 🇫🇷 · es_ES 🇪🇸 · en_GB 🇬🇧 · ja_JP 🇯🇵 · en_CA 🇨🇦 |
| x-amz-marketplace-id | Marketplace backend ID | APJ6JRA9NG5V4 🇮🇹 · ATVPDKIKX0DER 🇺🇸 · A1PA6795UKMFR9 🇩🇪 · A13V1IB3VIYZZH 🇫🇷 · A1RKKUPIHCS9HS 🇪🇸 · A1F83G8C2ARO7P 🇬🇧 · A1VC38T7YXB528 🇯🇵 · A2EUQ1WTGCTBG2 🇨🇦 |
| Accept-Language | Response language | Example: it-IT,it;q=0.9,en;q=0.8 |
| User-Agent | Browser-like request | Default or updated UA |
| Trigger interval | Refresh frequency | Every 5 days at 3:00 PM (modifiable) |
| Google Sheet | Storage output | Select your file + sheet |
You can adapt these headers to fetch data from any supported country.
💡 Tips & Customization
🌍 Regional catalogs
Duplicate the HTTP Request + Code + Sheet block to track multiple countries (US, DE, JP, UK…).
🧹 No duplicates
The workflow updates rows intelligently, ensuring a clean catalog even after many runs.
🗂️ Move data anywhere
Send the output to:
-
Airtable
-
Databases (MySQL, Postgres, MongoDB…)
-
Notion
-
CSV
-
REST APIs
-
BI dashboards
🔔 Add notifications (Discord, Telegram, Email, etc.)
You can pair this template with a notification workflow.When used with Discord, the notification message can include:
-
game title
-
description or metadata
-
the game’s image, automatically downloaded and attached
This makes notifications visually informative and perfect for tracking new Prime titles.
🔒 Important Notes
-
All retrieved data belongs to Amazon.
-
The workflow is intended for personal, testing, or educational use only.
-
Do not republish or redistribute collected data without permission.
Sync Amazon Luna Prime Games to Google Sheets with Automatic Updates
This n8n workflow automates the process of fetching Amazon Luna Prime Gaming titles and synchronizing them to a Google Sheet, with built-in mechanisms to detect and notify about new or removed games.
What it does
This workflow simplifies keeping track of Amazon Luna Prime Gaming titles by:
- Scheduled Execution: Runs on a predefined schedule (e.g., daily) to check for updates.
- Fetches Current Games: Makes an HTTP request to a specified API endpoint to retrieve the latest list of Amazon Luna Prime Gaming titles.
- Processes Game Data: Transforms the raw API response into a structured format, extracting relevant game details.
- Retrieves Existing Sheet Data: Reads all existing game entries from a designated Google Sheet.
- Compares and Identifies Changes: Compares the newly fetched games with the existing entries in the Google Sheet to identify:
- New Games: Titles that are present in the latest fetch but not in the Google Sheet.
- Removed Games: Titles that were in the Google Sheet but are no longer present in the latest fetch.
- Updates Google Sheet:
- Adds all identified new games to the Google Sheet.
- Removes all identified removed games from the Google Sheet.
- Notifies via Discord (Optional): If new or removed games are detected, the workflow sends a notification to a Discord channel, detailing the changes.
- Waits (Optional): Includes a configurable wait step, potentially to rate-limit API calls or allow for processing time before subsequent actions (though not connected in the provided JSON, it's present).
Prerequisites/Requirements
- n8n Instance: A running instance of n8n.
- Google Sheets Account: A Google account with access to Google Sheets. You will need to create a new spreadsheet or designate an existing one for storing game data.
- Google Sheets Credentials: An n8n credential configured for Google Sheets (OAuth 2.0 or Service Account).
- Discord Account (Optional): A Discord server and channel where notifications should be sent.
- Discord Webhook (Optional): An n8n credential configured for Discord (Webhook URL).
- Amazon Luna Prime Gaming API Endpoint: The URL for the API that provides the list of Amazon Luna Prime Gaming titles. (This is configured within the HTTP Request node).
Setup/Usage
- Import the Workflow:
- Copy the provided JSON code.
- In your n8n instance, go to "Workflows" and click "New".
- Click the "Import from JSON" button and paste the copied JSON.
- Configure Credentials:
- Google Sheets: Locate the "Google Sheets" node. Click on the "Credential" field and select an existing Google Sheets credential or create a new one (OAuth 2.0 is recommended for user accounts).
- Discord (Optional): Locate the "Discord" node. Click on the "Credential" field and select an existing Discord credential or create a new one (using a Webhook URL is typically easiest for notifications).
- Configure HTTP Request:
- Locate the "HTTP Request" node.
- Ensure the "URL" field is set to the correct API endpoint for Amazon Luna Prime Gaming titles.
- Adjust any other HTTP Request settings (e.g., headers, authentication) if required by the API.
- Configure Google Sheet Details:
- In the "Google Sheets" node, specify the "Spreadsheet ID" and "Sheet Name" where the game data will be stored.
- Configure Schedule Trigger:
- Locate the "Schedule Trigger" node.
- Set the desired interval for the workflow to run (e.g., daily, hourly).
- Review and Activate:
- Examine the "Edit Fields (Set)" and "Code" nodes to understand how data is being processed and transformed. Adjust if necessary based on the exact structure of your API response.
- Once all configurations are complete, activate the workflow by toggling the "Active" switch in the top right corner of the n8n editor.
The workflow will now automatically fetch, compare, and update your Google Sheet with Amazon Luna Prime Gaming titles according to your schedule, and notify you of changes on Discord if configured.
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.