Workflow Templates

What it does Pulls up to 700 Amazon reviews per product (recent and top-rated) and writes them straight into a Google Sheet tab you choose. Perfect for • Brand and product managers tracking sentiment • Marketplace sellers analysing competitor feedback • Agencies building product-review dashboards Apps used RapidAPI Real-Time Amazon Data, Google Sheets, n8n Form Trigger How it works Form Trigger collects brand, product and sheet info. Code node extracts the ASIN and builds 70 API requests (10 pages × star ratings). Split-in-batches loops through the request list, throttled by two Wait nodes. HTTP Request fetches reviews from RapidAPI. IF node drops empty or error responses. Split Out breaks arrays into single reviews. Google Sheets appends every review to the target tab. Loop continues until all pages finish. Setup Fill in Brand name, Product / Model Name, Amazon Product URL, Tab URL to insert reviews in the form. Grab your X-RapidAPI-Key from RapidAPI → Add as httpHeaderAuth credential. Connect Google Sheets OAuth2 and make the spreadsheet Anyone with the link can edit. Open Workflow Settings → set timezone if you plan to schedule runs. Hit Execute workflow or share the form link. Credentials • Real-Time Amazon Data (RapidAPI HTTP Header Auth) • Google Sheets OAuth2 Limits and notes • \~100 RapidAPI calls for the free plan. Plan quota accordingly. • Assumes Amazon returns 10 pages per star rating; fewer pages skip silently. • Large sheets may hit Google API write quotas. If you have any questions in running the workflow, feel free to reach out to me at my youtube channel: https://www.youtube.com/@lifeofhunyao

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. **Alternatively, you can delete the community node and use the HTTP node instead. ** Most email agent templates are fundamentally broken. They're stateless—they have no long-term memory. An agent that can't remember past conversations is just a glorified auto-responder, not an intelligent system. This workflow is Part 1 of building a truly agentic system: creating the brain. Before you can have an agent that replies intelligently, you need a knowledge base for it to draw from. This system uses a sophisticated parser to automatically read, analyze, and structure every incoming email. It then logs that intelligence into a persistent, long-term memory powered by mem0. The Problem This Solves Your inbox is a goldmine of client data, but it's unstructured, and manually monitoring it is a full-time job. This constant, reactive work prevents you from scaling. This workflow solves that "system problem" by creating an "always-on" engine that automatically processes, analyzes, and structures every incoming email, turning raw communication into a single source of truth for growth. How It Works This is an autonomous, multi-stage intelligence engine. It runs in the background, turning every new email into a valuable data asset. Real-Time Ingest & Prep: The system is kicked off by the Gmail Trigger, which constantly watches your inbox. The moment a new email arrives, the workflow fires. That email is immediately passed to the Set Target Email node, which strips it down to the essentials: the sender's address, the subject, and the core text of the message (I prefer using the plain text or HTML-as-text for reliability). While this step is optional, it's a good practice for keeping the data clean and orderly for the AI. AI Analysis (The Brain): The prepared text is fed to the core of the system: the AI Agent. This agent, powered by the LLM of your choice (e.g., GPT-4), reads and understands the email's content. It's not just reading; it's performing analysis to: Extract the core message. Determine the sentiment (Positive, Negative, Neutral). Identify potential red flags. Pull out key topics and keywords. The agent uses Window Buffer Memory to recall the last 10 messages within the same conversation thread, giving it the context to provide a much smarter analysis. Quality Control (The Parser): We don't trust the AI's first draft blindly. The analysis is sent to an Auto-fixing Output Parser. If the initial output isn't in a perfect JSON format, a second Parsing LLM (e.g., Mistral) automatically corrects it. This is our "twist" that guarantees your data is always perfectly structured and reliable. Create a Permanent Client Record: This is the most critical step. The clean, structured data is sent to mem0. The analysis is now logged against the sender's email address. This moves beyond just tracking conversations; it builds a complete, historical intelligence file on every person you communicate with, creating an invaluable, long-term asset. Optional Use: For back-filling historical data, you can disable the Gmail Trigger and temporarily connect a Gmail "Get Many" node to the Set Target Email node to process your backlog in batches. Setup Requirements To deploy this system, you'll need the following: An active n8n instance. Gmail** API credentials. An API key for your primary LLM (e.g., OpenAI). An API key for your parsing LLM (e.g., Mistral AI). An account with mem0.ai for the memory layer.

Auto-publish NASA APOD to LinkedIn with AI translation and hashtags Transform NASA's daily astronomical wonders into engaging LinkedIn content automatically. This workflow fetches NASA's Astronomy Picture of the Day, translates it to Brazilian Portuguese using AI, generates strategic hashtags, and publishes everything to your LinkedIn profile with the stunning space image attached. Who's it for Content creators, astronomy enthusiasts, science communicators, and anyone wanting to share high-quality educational content consistently on LinkedIn. Perfect for Portuguese-speaking professionals who want to engage their network with fascinating space discoveries while building their personal brand as a science advocate. How it works The workflow runs on a daily schedule and handles the complete content pipeline automatically. It fetches the latest NASA APOD through the official API, including both the image and detailed explanation. The English description gets professionally translated to selected language using Google Gemini 2.5 Flash, while maintaining scientific accuracy and terminology. Smart hashtag generation combines fixed branding tags with content-specific ones, mixing Portuguese and English for maximum reach. The final post includes the NASA image, translated description, and strategic hashtags, then gets published to your LinkedIn profile automatically. How to set up You'll need accounts for Google AI Studio (free), LinkedIn Developer (free), and a Telegram bot for notifications. The setup takes about 15 minutes and uses only free services and APIs. First, create your Google AI Studio account and get an API key for the AI translation services. Then set up a LinkedIn OAuth2 application to enable posting permissions. Create a Telegram bot through BotFather and get your chat ID for notifications. Configure the Settings node with your Telegram chat ID and preferred language. The workflow comes with all prompts and configurations ready to use. Test each component individually before activating the daily automation. Requirements LinkedIn account with posting permissions Google AI Studio API key (free tier available) Telegram bot token and your chat ID Basic understanding of OAuth2 setup for LinkedIn NASA API key (optional - demo key included) All services used have generous free tiers, making this workflow completely free to operate indefinitely. How to customize the workflow The centralized Settings node makes customization simple. Change the target language from Brazilian Portuguese to any other language by updating the translate_to_language variable. Modify the posting schedule in the CRON trigger to match your preferred timing. Customize the post template in the "Create Final Post Text" node to match your personal brand voice. Adjust the hashtag strategy by editing the AI prompt in the "Generate Hashtags" node. Add additional social platforms by duplicating the LinkedIn publisher with different credentials. The AI prompts can be fine-tuned for different writing styles or specific astronomical topics. You can also extend the workflow to include additional content processing, image enhancements, or cross-posting to multiple platforms while maintaining the core NASA APOD automation.

MCP Tool — Replicate (Flux) Image Generator → WordPress/Twitter Generates images via Replicate Flux models and uploads to WordPress (and optionally Twitter/X). Built to act as an MCP module that other agents/workflows call for on-demand image creation. Models configured in this workflow:\ black-forest-labs/flux-schnell, black-forest-labs/flux-dev, black-forest-labs/flux-1.1-pro Switch rationale: lower cost 💰, broader model choice 🎯, full control of parameters ⚙️ Leonardo API credits cannot be used in the web UI 🙅‍♂️; separate spend for API vs UI Links: 📜 Prior Leonardo-based workflow: https://n8n.io/workflows/6363-generate-and-upload-images-with-leonardo-ai-wordpress-and-twitter/ 📰 Blog automation consuming these images: https://n8n.io/workflows/6734-ai-blog-automation-publish-hourly-seo-articles-to-wordpress-and-twitter-v3/ 📥 Inputs | Field | Type | Description | | ------ | ------ | --------------------------------- | | prompt | string | Text description for the image | | slug | string | Filename slug for WP media | | model | string | One of the configured Flux models | Example: { "prompt":"Joker watching a Batman movie on his laptop", "slug":"joker-watching-batman", "model":"black-forest-labs/flux-dev" } 📤 Output { "public_image_url": "https://your-wp.com/wp-content/uploads/2025/08/img-joker-watching-batman.webp", "wordpress": {...}, "twitter": {...} } 🔄 Flow Trigger with prompt, slug, model Build model payload (quality/steps/ratio/output format) Call Replicate: POST /v1/models/{model}/predictions (Prefer: wait) Download the generated image URL Upload to WordPress (returns public URL) Optional: upload to Twitter/X Return URL + metadata 🤖 MCP Use at Scale (emp0.com) Operational pattern: I currently use this setup for my blog where i generate 300 posts/month, each with 4 images (banner + 2 to 3 inline images) → 1,000 images/month produced by this MCP. 💡 Hybrid Cost-Optimized Setup: High-priority images* (banners, main visuals): Generated using *Flux Dev** on Leonardo for slightly better prompt adherence. Low-priority images* (inline blog visuals): Generated using *Flux Schnell** on Replicate for maximum cost efficiency. 💰 Pricing Comparison (per image) Leonardo per-image cost uses API Basic math: $9 / 3,500 credits = $0.0025714 per credit. Flux Schnell (Leonardo)** = 7 credits Flux Dev (Leonardo)** = 7 credits Flux 1.1 Pro equivalent in Leonardo* = *Leonardo Phoenix** based on my experience = 10 credits | Flux Model | Replicate | Leonardo API* | | ------------------------ | ------------------------- | ------------------------------- | | flux-schnell | $0.0030 (=$3/1,000) | $0.0180 (7 × $0.0025714) | | flux-dev | $0.0250 | $0.0180 (7 × $0.0025714) | | flux-1.1-pro / Phoenix | $0.0400 | $0.0257 (10 × $0.0025714) | Replicate pricing: https://replicate.com/pricing\ Leonardo pricing: https://leonardo.ai/pricing/\ Leonardo API usage: https://docs.leonardo.ai/docs/commonly-used-api-values 📊 Monthly Cost Example (1,000 images/month) Mix: 300 ×flux-dev on Leonardo, 700 ×flux-schnell on Replicate. | Platform/Model | Images | Price per Image | Total | | ------------------------ | ------ | --------------- | ---------- | | Leonardo flux-dev | 300 | $0.0180 | $5.40 | | Replicate flux-schnell | 700 | $0.0030 | $2.10 | | Total Monthly Spend | 1000 | — | $7.50 | 💵 If using Leonardo for both: 300 × $0.0180 = $5.40 700 × $0.0180 = $12.60 Total = $18.00** Savings: $10.50/month (≈58% lower) with the hybrid setup. 📌 Notes More Replicate models can be added in Code1 node. Parameters tuned for aspect ratio, inference steps, quality, guidance. Leonardo credit model is API-only; credits are not spendable in Leonardo's web UI.

What this workflow does Automatically triages risky AWS misconfigurations and alerts your team. Pipeline: Security Hub or AWS Config -> EventBridge rules -> SNS (HTTP) -> n8n Webhook -> Normalize -> AI Prioritizer -> Airtable (log) -> Gmail (email) Normalizes incoming findings (S3 / Security Groups / IAM / RDS) into a consistent JSON. Uses an LLM to assign a priority (P0–P3) with rationale and remediation steps. Upserts the finding into Airtable (avoids duplicates). Emails a compact incident summary to your inbox. This can be swapped for Microsoft Teams or Slack, etc. Category: Security / Cloud / Alerting Time to set up: ~10–15 minutes Difficulty: Beginner–Intermediate Cost: Mostly free (n8n CE + AWS SNS/EventBridge; OpenAI + Airtable/Gmail as used) What you’ll need An n8n instance reachable over HTTP. AWS account (one region) with permissions to create SNS topics and EventBridge rules. Security Hub** enabled (or AWS Config rules that emit compliance events). n8n credentials: OpenAI, Airtable, Gmail. Nodes used Webhook** (POST /aws-misconfig) Code:** SNS Handler (token check, confirm/unwrap) IF:** route mode === "confirm" vs notification HTTP Request:** SNS SubscriptionConfirmation (GET) Code:** Normalize Finding Message a model:** AI Prioritizer (JSON out) Airtable:** Create/Upsert Gmail:** Send message Edit Fields:** final JSON response Setup steps Import and activate the workflow in n8n. Webhook Respond: When Last Node Finishes -> First Entry JSON. Append a shared secret to the URL, e.g. ?token=MY_SUPER_TOKEN, and keep the check in the SNS Handler code node. Create an SNS topic (e.g., misconfig-events) in the same region as your EventBridge rules. Create EventBridge rules targeting the SNS topic: Rule A (Security Hub): source = aws.securityhub, detail-type = Security Hub Findings - Imported Rule B (AWS Config): source = aws.config, detail-type = Config Rules Compliance Change Create an SNS subscription with Protocol = HTTP and Endpoint = your production webhook URL: http://YOUR_HOST:5678/webhook/aws-misconfig?token=MY_SUPER_TOKEN (The workflow auto-confirms the subscription on first POST.) Configure Airtable (Upsert on Finding ID) and Gmail recipients.

This n8n workflow recommends Taiwan indie music based on a user's city, mood, birthday, today's weather, and star sign. Here's a concise overview: Trigger: Starts manually with the "When clicking ‘Test workflow’" node. Input Setup: The "infomation" node sets user inputs (e.g., city: Taipei, mood: Happy, birthday: 1996/11/21). Song Recommendation: The "get song recommendation" node uses OpenAI's GPT-4o-mini to: Fetch today's weather for the specified city. Determine the user's zodiac sign from their birthday. Check the zodiac sign's daily fortune. Recommend a Taiwan indie song considering weather and fortune. Explain the song choice and highlight its features. Return results in JSON format. Data Extraction: The "Information Extractor" node parses the JSON output, extracting fields like date, city, weather, zodiac sign, fortune, song, artist, and additional info. Spotify Search: The "Spotify" node searches for the recommended song using the artist and song name, retrieving a Spotify URL. Final Output: The "Final Output" node compiles all data, including the Spotify link, into a structured format. Additional Note: A "Sticky Note" provides context about the workflow's purpose and credits the creator, n8nguide. This workflow integrates AI, weather data, astrology, and Spotify to deliver personalized Taiwan indie music recommendations.

Introduction This workflow offers a streamlined solution for uploading multiple files to a GitHub repository simultaneously using GitHub's REST API. It addresses a significant limitation of n8n's native GitHub node, which only supports single-file uploads at a time. By leveraging GitHub's Git Data API, this workflow creates a new Git tree containing multiple files, commits this tree, and updates the target branch—all in a single automated process. The workflow is particularly valuable for automation scenarios that require batch file operations, such as deploying website updates, publishing documentation, or maintaining configuration files across repositories. It eliminates the need for multiple separate API calls when working with multiple files, making your automation more efficient and less prone to partial update issues. By abstracting the complexities of GitHub's Git Data API into a reusable workflow, it provides a practical solution for developers, content managers, and DevOps professionals who need to programmatically manage repository content at scale. Who is this for? This workflow is designed for: Developers and DevOps engineers who need to automate file updates in GitHub repositories Content managers who regularly publish multiple files to GitHub-hosted websites or documentation Automation specialists looking to integrate GitHub operations into larger workflows Teams using n8n for CI/CD processes who need to push code or configuration changes Users should have basic familiarity with GitHub concepts (repositories, branches, commits) and should be comfortable obtaining and using GitHub Personal Access Tokens. While the workflow handles the API complexity, users should understand the fundamentals of version control to effectively utilize and customize it. What problem is this workflow solving? This workflow addresses several key challenges: Limited batch operations: n8n's native GitHub node only supports uploading one file at a time, making multi-file operations cumbersome and inefficient. API complexity: GitHub's Git Data API requires multiple sequential calls with interdependent data to create commits with multiple files, which is complex to implement manually. Automation bottlenecks: Without this workflow, automating multi-file updates would require either multiple separate API calls (risking partial updates) or custom scripting outside of n8n. Consistency issues: When files need to be updated together (e.g., code and corresponding documentation), this workflow ensures they're committed in a single atomic operation. By solving these issues, the workflow enables reliable, atomic updates of multiple files, maintaining repository consistency and simplifying automation processes. What this workflow does Overview This workflow uses GitHub's REST API to push multiple files to a repository in a single operation. It follows Git's internal model by: Retrieving the current state of the repository Creating a new tree with the files to be added or updated Creating a new commit with this tree Updating the branch reference to point to the new commit Process Initialization: The workflow starts with a manual trigger and sets up GitHub credentials and repository information. File Content Definition: Two "Set" nodes define the content for the files to be uploaded. Repository State Retrieval: The workflow fetches the latest commit SHA for the specified branch It then retrieves the base tree SHA from this commit Tree Creation: A new Git tree is created that includes both files (file1.txt and file2.txt), specifying their paths and content. Commit Creation: A new commit is created with the specified commit message, referencing the new tree and the parent commit. Branch Update: Finally, the branch reference is updated to point to the new commit, making the changes visible in the repository. Setup To use this workflow: Import the workflow: Download the workflow JSON and import it into your n8n instance. Create a GitHub Personal Access Token: Go to GitHub Settings → Developer Settings → Personal Access Tokens → Fine-grained tokens Create a new token with "Contents" permission (Read and write) for your target repository Configure the workflow: Update the "Set Github Info" node with: Your GitHub Personal Access Token Your GitHub username Your repository name The target branch (default is "main") A commit message Define file content: Modify the "File 1" and "File 2" nodes with the content you want to upload Adjust file paths if needed: In the "Create new tree" node, update the file paths if you want to change where the files are stored in the repository Save and run the workflow: Click "Test workflow" to execute the process. How to customize this workflow to your needs This workflow can be adapted in several ways: Add more files: Create additional "Set" nodes for more file content In the "Create new tree" node, add more tree entries following the same pattern (path, mode, type, content) Change file locations: Modify the "path" parameters in the "Create new tree" node to place files in different directories Dynamic file content: Replace the static content in the "File" nodes with data from other sources Use previous nodes or HTTP requests to generate file content dynamically Conditional file updates: Add IF nodes to determine which files should be updated based on certain conditions Create separate branches in your workflow for different update scenarios Scheduled updates: Replace the manual trigger with a Schedule node to run the workflow at specific intervals Combine with other triggers like Webhook or database events to push files when certain events occur Error handling: Add Error Trigger nodes to handle potential API failures Implement notification nodes to alert you of successful pushes or failures

Gmail AI Email Manager - Setup Guide 🎯 Workflow Overview This workflow will create an intelligent Gmail email manager that can: Monitor incoming emails via webhook Analyze email content using AI Categorize emails automatically Generate smart responses Take actions based on email content Send notifications for important emails 📋 Pre-Setup Checklist Before we build the workflow, let me gather the necessary information and validate our approach. Phase 1: Discovery & Planning [ ] Search for Gmail nodes [ ] Find AI analysis nodes [ ] Identify webhook trigger options [ ] Check notification nodes Phase 2: Configuration Requirements [ ] Gmail API credentials [ ] AI service (OpenAI/Claude) API key [ ] Webhook URL setup [ ] Email classification rules 🔧 Setup Instructions Step 1: Gmail API Setup Go to Google Cloud Console Create new project or select existing Enable Gmail API Create OAuth 2.0 credentials Add authorized redirect URI: https://your-n8n-instance.com/rest/oauth2-credential/callback Step 2: AI Service Setup Choose one of the following: OpenAI**: Get API key from platform.openai.com Claude**: Get API key from console.anthropic.com Local AI**: Set up Ollama or similar Step 3: n8n Credentials Gmail OAuth2: Add client ID, secret, and scopes AI Service: Add API key Webhook: Configure webhook URL Gmail AI Email Manager - Setup Guide 🔧 Quick Setup Checklist 1. Google Cloud Console [ ] Enable Gmail API [ ] Create OAuth2 credentials [ ] Add redirect URI: https://your-n8n.com/rest/oauth2-credential/callback [ ] Set up Gmail push notifications with Pub/Sub 2. API Keys [ ] Get OpenAI API key from platform.openai.com [ ] Create Google Sheets for logging (optional) 3. n8n Credentials [ ] Gmail OAuth2: Client ID, Secret, Scopes: gmail.readonly,gmail.modify,gmail.compose [ ] OpenAI API: Your API key 4. Gmail Labels (Create these) [ ] URGENT (red) [ ] IMPORTANT (orange) [ ] PROMOTIONAL (purple) [ ] PERSONAL (green) [ ] WORK (blue) [ ] SPAM (gray) 5. Update Workflow Values [ ] High Priority Alert: Change notification email [ ] Spreadsheet Log: Update sheet ID (if using) [ ] Webhook: Copy URL after saving workflow 6. Test [ ] Save & activate workflow [ ] Send test email to Gmail [ ] Check execution log [ ] Verify auto-categorization works That's it! Your AI email manager is ready! 🚀

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Description This workflow automatically collects daily trending topics from Twitter and YouTube, filters them for relevance, and uses an AI model (such as Mistral Cloud or another OpenAI-compatible API) to generate engaging social media hashtags. The final results, including source platform and date, are saved into a connected Google Sheet for easy access, tracking, or team collaboration. Ideal for content creators, marketers, and social media managers, this automation eliminates the manual effort of trend research and hashtag writing by combining real-time scraping with LLM-powered generation. The result is a scalable, daily strategy tool to stay aligned with what’s trending across major platforms. How It Works Daily Trigger Starts the workflow automatically on a daily schedule. Trend Scraping Scrapes current trending content from Twitter and YouTube using the Crawl and Scrape community node. Filtering & Slicing Removes irrelevant or duplicate entries and limits each platform’s list to top-performing trends. Merge Trends Combines Twitter and YouTube trends into a single dataset. AI Hashtag Generation Sends each trend topic to an AI model to generate relevant hashtags. Output to Google Sheets Loops through AI results and writes them to a Google Sheet, including trend, platform, hashtags, and timestamp. Setup Instructions Estimated time: 10–15 minutes Prerequisites A self-hosted instance of n8n (required for community nodes) API key for Mistral Cloud or any OpenAI-compatible LLM Google Sheets account connected via OAuth2 credentials Twitter and YouTube trend URLs (or scraping logic for target regions) Template Image: Example: Crawl and Scrape Node for Twitter Trends You can use the following configuration in the Crawl and Scrape node to extract Twitter trends from Trends24) { "parameters": { "url": "https://trends24.in/", "selectors": [ { "label": "Twitter Trends", "selector": ".trend-card__list li a", "type": "text" } ] }, "name": "Scrape Twitter Trends", "type": "n8n-nodes-crawl-and-scrape.crawlAndScrape", "typeVersion": 1, "position": [300, 200] } Google Sheet Column Format Column A: Generated Hashtags

🔒 SSL Certificate Monitoring & Expiry Alert with Spreadsheet [FREE APIs] ✅ What This Workflow Does This n8n template automatically monitors SSL certificates of websites listed in a Google Sheet and sends email alerts if any are expiring within 14 days. It helps ensure you avoid downtime, security issues, and trust warnings due to expired certificates. 🧩 Key Features 📅 Weekly Automation: Runs every Monday at 7:00 AM (configurable). 📄 Google Sheets Integration: Fetches and updates data in a spreadsheet. 🔍 SSL Check via API: Uses ssl-checker.io to get certificate details. ⚠️ SSL Expiry Filter: Identifies certificates expiring within 14 days. 📧 Email Alerts: Sends notifications for certificates close to expiration. 📂 Input Spreadsheet Format Your Google Sheet should have the following columns: | No | Name | Link | SSL Issued On | SSL Expired On | SSL Status | |----|-----------------|-----------------------|-------------------|-------------------|------------| | 1 | Example Site | https://example.com | 2024-07-01 | 2025-07-01 | Valid | | 2 | My Blog | https://myblog.org | 2024-07-05 | 2024-07-20 | Expiring | Each row should include a valid website URL in the Link column. 🛠️ How It Works Scheduled Trigger Executes weekly (Monday 7:00 AM). Fetch Website List Reads all website entries from the Google Sheet. Check SSL Certificates Uses ssl-checker.io API to retrieve certificate details for each website. Update Spreadsheet Writes "Issued On" and "Expired On" fields back to the spreadsheet. Evaluate SSL Expiry Filters for certificates expiring within 14 days. Check Condition Determines whether to send alerts based on filtered results. Send Email Alert Notifies via email if any certificates are expiring soon. 📬 Example Email Output Subject: ⚠️ ALERT!! SSL EXPIRED SSL certificates expiring soon: example.com (expires in 5 days) anotherdomain.net (expires in 3 days) 🧰 Setup Requirements A Google Sheet with the correct columns and website links. SMTP credentials to send alert emails.

Automating Company Data Enrichment and HubSpot Integration Use Case This automation enriches company data based on email domain and LinkedIn profile, calculates an ICP (Ideal Customer Profile) score, and updates the corresponding company record in HubSpot. It’s ideal for onboarding, qualification, and CRM enrichment. What This Automation Does Input Parameters Contact email**: Used to derive the company domain. Company domain**: Primary web domain of the company. Company LinkedIn* *(optional): LinkedIn URL for enrichment accuracy. Airtop Profile (connected to LinkedIn)**: An authenticated Airtop Profile. What It Outputs Full company profile (name, tagline, website, headquarters) Employee count ICP score based on AI/tech profile, scale, agency type, and location Updates/creates record in HubSpot with all enriched attributes How It Works Input Validation: Filters out non-corporate domains like Gmail, Yahoo, or .edu. Enrichment Trigger: Launches Airtop workflows to extract and analyze data from LinkedIn and calculate the ICP score. Data Mapping: Compiles structured fields including: Overview, location (city, state, country) Company website and domain LinkedIn URL, employee count ICP score HubSpot Sync: Sends all the enriched fields to the designated HubSpot object for upsertion. Setup Requirements Airtop API Key Airtop Profile with active LinkedIn authentication HubSpot integration enabled for object updates Next Steps Use in Webforms**: Trigger this on signup to auto-populate CRM records. Enrich Manually Entered Contacts**: Use with list-based workflows for batch enrichment. Sync to Other CRMs**: Replace HubSpot step with Salesforce, Pipedrive, etc. for flexible integration. Read more about comapny data enrichment

🚀 LinkedIn Lead-Gen Flywheel – Apify → GPT-4o → Google Sheets → Phantombuster What this workflow does Collect audience specs – simple web-form asks for your ideal company profile. Generate a laser-targeted Apollo search URL with GPT-4o (no manual filtering). Scrape the matching leads via an Apify actor (returns clean JSON). Craft hyper-personalized icebreakers for each lead using GPT-4o (ultra-short, human-sounding). Log everything to Google Sheets – name, LinkedIn URL, company site, summary, and the icebreaker. (Optional) Auto-launch Phantombuster to fire off those connection requests at scale. Why it matters Zero grunt work:** audience research, scraping, copy-writing, and outreach all run hands-free. Punchy personalization:** micro-icebreakers outperform canned intros, boosting accept rates. Scales with you:** flip a switch to go from 10 to 1 000+ connections/day. Node rundown | Step | Node | Key Inputs | Key Outputs | |------|------|-----------|-------------| | 1 | Form Trigger | Audience description | description_of_company | | 2 | OpenAI (GPT-4o) | Audience text | SearchUrl | | 3 | HTTP Request – Apify | SearchUrl, APIFY_TOKEN | Lead JSON | | 4 | OpenAI (GPT-4o) | Lead JSON | Icebreaker | | 5 | Google Sheets | Lead + Icebreaker | Row append/update | | 6 | Aggregate | Sheet rows | Batched output | | 7 | HTTP Request – Phantombuster | PHANTOM_KEY, AGENT_ID | Launch status | Prerequisites OpenAI API key** (GPT-4o access recommended) Apify API token** with access to actor id Google Service Account creds** shared with your target sheet Phantombuster API key** and Agent ID for your LinkedIn connector Active Apollo account to open the generated search URL (only required for debugging) Setup (5-minute sprint) Import the workflow into n8n. Add the required credentials in Credentials → OpenAI, Apify, Google Sheets, Phantombuster. Paste your Phantombuster Agent ID into the HTTP Request node URL. Publish the Form Trigger URL—this is where you (or your SDRs) describe the target audience. Hit Execute Workflow once to verify data flows end-to-end. Customization tips Titles & keywords:** tweak the prompt in the first GPT-4o node to lock in different roles or industries. Icebreaker style:** adjust the second GPT-4o prompt to match your brand voice. Data columns:** map extra fields from Apify into Google Sheets as needed. Skip outreach:** disable the Phantombuster node if you only want the leads + icebreakers.