HumationHuman + Automation
Back to Catalog

Find quality YouTube videos with automated filtering & relevance scoring to Google Sheets

Zach @BrightWayAIZach @BrightWayAI
1096 views
2/3/2026
IMAPAutomationEmailAttachmentOneDrive
Official Page

Who's it for

Content creators, researchers, educators, and digital marketers who need to discover high-quality YouTube training videos on specific topics. Perfect for building curated learning resource lists, competitive research, or content inspiration.

What it does

This workflow automatically searches YouTube using multiple search queries, filters for quality content, scores videos by relevance, and exports the top results to Google Sheets. It processes hundreds of videos and delivers only the most valuable educational content ranked by custom relevance criteria.

The workflow searches for videos using 10 different AI automation-related queries (easily customizable), filters out low-quality content like shorts and clickbait, then ranks results based on title keywords, view counts, and engagement metrics.

How it works

  1. Multi-query search: Searches YouTube with an array of related queries to get comprehensive coverage
  2. Content filtering: Removes shorts, spam, and low-quality videos using regex patterns
  3. Quality assessment: Filters videos based on view count, likes, and publication date
  4. Relevance scoring: Assigns scores based on title keywords and engagement metrics
  5. Result ranking: Sorts videos by relevance score and limits to top 50 results
  6. Export to Sheets: Delivers clean, organized data to Google Sheets with all metadata

Requirements

  • YouTube Data API v3 credentials from Google Cloud Console
  • Google Sheets credentials for n8n workspace
  • A Google Sheets document to receive the results

How to set up

  1. Enable YouTube Data API v3 in your Google Cloud Console
  2. Add YouTube OAuth2 credentials to your n8n workspace
  3. Add Google Sheets credentials to your n8n workspace
  4. Create a Google Sheet and update the Google Sheets node with your document ID
  5. Customize search queries in the "Set Query" node for your topic
  6. Adjust filtering criteria in the Filter nodes based on your quality requirements

How to customize the workflow

Search topics: Modify the query array in the "Set Query" node to research any topic:

[
"Python tutorial",
"JavaScript course", 
"React beginner guide",
// Add your queries here
]

Quality thresholds: Adjust minimum views, likes, and date ranges in the "Filter for Quality" node

Relevance scoring: Customize keyword weightings in the "Relevance Score" node to match your priorities

Result limits: Change the number of final results in the "Limit" node (default: 50)

Output format: Modify the "Set Fields" node to include additional YouTube metadata like duration, thumbnails, or category information

The workflow is designed to be easily adaptable for any research topic while maintaining high content quality standards.

n8n YouTube Video Quality Finder and Scorer

This n8n workflow automates the process of finding YouTube videos, applying automated filtering and relevance scoring, and then saving the results to a Google Sheet. It's designed to help users identify high-quality content based on specific criteria.

What it does

This workflow performs the following key steps:

  1. Manual Trigger: The workflow is initiated manually, allowing you to control when the video search and scoring process begins.
  2. HTTP Request (YouTube API): It makes an HTTP request, likely to the YouTube Data API, to search for videos based on predefined parameters (e.g., keywords, channels, etc.).
  3. Edit Fields: The retrieved video data is then processed to edit or transform specific fields, preparing it for further analysis.
  4. Loop Over Items: The workflow iterates through each video item obtained from the YouTube API.
  5. Wait: A Wait node is included, potentially to manage API rate limits or to introduce a delay between processing video items.
  6. HTTP Request (Scoring/Filtering): For each video, another HTTP request is made. This node likely interacts with an external service or a custom API endpoint to perform relevance scoring or advanced filtering based on criteria like video quality, engagement metrics, or content analysis.
  7. Edit Fields: The results from the scoring/filtering step are further processed and edited.
  8. Filter: Videos are filtered based on the scores or criteria established in the previous steps, ensuring only "quality" videos proceed.
  9. Remove Duplicates: Any duplicate video entries are removed to maintain a clean dataset.
  10. Limit: The number of processed videos is limited, allowing you to control the final output size.
  11. Sort: The remaining videos are sorted, likely by their relevance score or another quality metric.
  12. Split Out: The final, filtered, and sorted video data is prepared for output.
  13. Google Sheets: The refined list of high-quality YouTube videos is appended or updated in a specified Google Sheet.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • YouTube Data API Key: For the "HTTP Request" node that fetches YouTube video data.
  • Google Account: With access to Google Sheets for the "Google Sheets" node. You'll need to set up Google Sheets credentials in n8n.
  • External Scoring/Filtering Service (Optional): If the second "HTTP Request" node points to a custom API for scoring/filtering, you'll need access to and credentials for that service.

Setup/Usage

  1. Import the workflow: Download the JSON content and import it into your n8n instance.
  2. Configure Credentials:
    • Google Sheets: Set up your Google Sheets OAuth2 or service account credentials in n8n.
    • YouTube API: Configure the "HTTP Request" node with your YouTube Data API key. This will likely be in the URL or headers.
  3. Customize HTTP Requests:
    • YouTube API Request: Adjust the URL, query parameters (e.g., search terms, channel IDs, max results), and headers in the first "HTTP Request" node to match your desired YouTube search criteria.
    • Scoring/Filtering Request: If used, configure the second "HTTP Request" node with the endpoint and parameters for your external scoring or filtering service.
  4. Adjust Edit Fields Nodes: Modify the "Edit Fields" nodes to transform data as needed for your specific use case.
  5. Configure Filter Node: Set the conditions in the "Filter" node to define what constitutes a "quality" video based on the data processed.
  6. Google Sheets Node: Specify the Spreadsheet ID and Sheet Name where you want to save the results.
  7. Execute Workflow: Click the "Execute workflow" button in the "Manual Trigger" node to run the workflow.

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.

Ranjan DailataBy Ranjan Dailata
161

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

Daniel NkenchoBy Daniel Nkencho
601

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.

Le NguyenBy Le Nguyen
942