Workflow Templates

🧩 What Problem Does It Solve? Meta’s ad forms often generate unqualified leads from casual scrollers. This workflow uses WhatsApp and AI to automatically verify, qualify, and prioritize real leads — saving time and boosting sales efficiency. 📝 Description This workflow automates lead qualification for businesses using Meta Ads (Facebook/Instagram Lead Ads) to filter out irrelevant leads. It ensures only confirmed prospects enter your CRM by: Collecting new Facebook leads Verifying via WhatsApp confirmation Classifying responses with AI Updating CRM status based on intent When a new Facebook lead arrives: Lead details are extracted (name/phone/email) Zoho CRM is checked for existing contacts WhatsApp confirmation request is sent AI classifies the response (confirmed/declined/human/invalid) CRM status is updated automatically Sales team receives only verified leads 🎯 Key Advantages for Meta Ads ✅ Blocks 60%+ irrelevant leads based on WhatsApp non-response ✅ Reduces fake submissions by requiring active confirmation ✅ Prevents CRM bloat through duplicate checking ✅ Identifies hot leads via instant "human_requested" escalation ✅ Saves sales team hours by auto-declining "no" responses 🛠️ Features Facebook Lead Ads integration via Graph API WhatsApp messaging via Twilio AI response classification (Gemini) Zoho CRM synchronization Duplicate lead prevention Customizable confirmation flow Error-resistant JSON parsing CRM owner assignment Status-based routing 🔧 Requirements Facebook Access Token with ads_management & leads_retrieval permissions Twilio Account with WhatsApp-enabled number Zoho CRM with custom "Status" field Gemini API Key (or alternative LLM) n8n credentials configured for: Twilio (API SID/token) Zoho CRM (OAuth2) Google Gemini (or alternative LLM) ⚙️ Customization Tips 1-Adjust Classification Criteria Modify the AI prompt in Classify Response (AI) node 2-Customize CRM Status Values Update field IDs in Zoho nodes 3-Modify Messaging Edit WhatsApp templates in Send WhatsApp Confirmation 4-Set Owner Assignment Replace owner ID in Prepare Owner ID node 🧠 Use Case Examples Real Estate Agencies: Filter speculative inquiries from serious buyers Medical Clinics: Verify appointment requests before scheduling SAAS Companies: Qualify free trial sign-ups Education Providers: Confirm course interest before counselor assignment Auto Dealerships: Screen test drive requests from tire-kickers If you need help get in touch on Linkedin

🔧 AI-Powered Cold Call Machine 🎯 Purpose The AI-Powered Cold Call Machine is a fully automated workflow designed to generate qualified leads from LinkedIn, evaluate them using AI-based scoring, identify key decision-makers, and generate personalized cold call scripts. All results are saved to a Google Sheet-based CRM. ⚙️ How It Works 1. Initialization Triggered either manually or via schedule. Pulls configuration from a Google Sheet’s Settings tab (e.g., target product, keywords, company size, API key). 2. Company Search on LinkedIn Uses the Ghost Genius API to search for companies based on cleaned, relevant keywords extracted by OpenAI. Handles pagination, up to 1000 companies per batch. 3. Company Filtering Each company goes through: Data enrichment via Ghost Genius (website, size, followers, etc.). Filtering: Must have a LinkedIn page with a website. Must have 200+ followers. Deduplication: checks if the company already exists in the CRM. 4. AI-Based Company Scoring A specialized AI model scores each company from 0 to 10 based on: Industry fit. Size/location alignment. Potential pain points that match your offering. If the company is new and relevant (score ≥ 7), it is saved in the Companies sheet. 5. Decision Maker Identification Uses Sales Navigator API (via Ghost Genius) to find employees with targeted job titles. For each matching profile: Enriches contact data (title, bio, etc.). Retrieves phone number (if available). Generates a 20-second personalized cold call script using OpenAI, based on company and profile data. Saves all information in the Leads tab of the CRM. If no decision maker is found, the company status is marked accordingly. 📈 Outcome A fully enriched, qualified lead database. Custom cold call scripts** ready to be used by SDRs or founders. Zero manual work – from search to lead generation, everything is automated. 💡 Use Case Perfect for SDRs, founders, or growth marketers looking to scale cold outreach without sacrificing personalization or running into LinkedIn scraping limits.

Description: Turn raw customer feedback into actionable insights with this intelligent n8n workflow template! Automatically capture reviews from Google Sheets, run AI-driven sentiment and intent analysis, and enrich your dataset with structured insights—no manual review required. This automation connects to your feedback form responses, processes reviews with an AI model, classifies intent, evaluates sentiment, assigns a score, and generates concise summaries. The results are then parsed, merged with original customer details, and stored in a structured Google Sheet for easy tracking. Perfect for sales, product, and customer success teams looking to streamline lead qualification and feedback analysis. What This Template Does: 📊 Captures new customer feedback from Google Sheets in real time 🧠 Uses AI to classify intent (praise, complaint, suggestion, etc.) 😊 Detects sentiment (positive, neutral, negative, or mixed) 🔢 Assigns a review score (1–10) for quick lead qualification 📝 Generates short, meaningful summaries of customer reviews 📂 Saves enriched data into a structured destination sheet 🌟 100% hands-free: just let AI process and organize your feedback Built-in Logic Ensures: ✔️ Clean JSON-based AI output (intent, sentiment, score, summary) ✔️ Customer details remain tied to their feedback and insights ✔️ Final dataset is ready for reporting, CRM import, or dashboards Requirements: Google Sheets with customer feedback form responses Google Sheets account for storing enriched data Azure OpenAI (or compatible) account for AI analysis n8n instance (self-hosted or cloud) Perfect For: Sales teams qualifying leads based on review sentiment Product managers analyzing user feedback at scale Customer success teams identifying risks and opportunities Analysts turning unstructured reviews into actionable insights

Quick Overview This workflow runs every weekday at 9 AM, checks open pull requests across configured GitHub repositories, calculates how long each PR has been waiting on review activity, and posts a ranked review-lag digest to a chosen Slack channel. How it works Runs on a weekday schedule at 9:00 AM and loads the repository list, staleness threshold, draft filtering option, and target Slack channel. Queries the GitHub API for open pull requests in each configured repository. Filters out draft PRs (optional), normalizes PR fields, and fetches each PR’s review history from the GitHub API. Calculates review-lag metrics per PR (age, last review time, current review status, and whether it is stale based on the configured days threshold). Aggregates and ranks PRs by review lag, then builds a Slack-formatted digest grouped by PR author. Posts the digest to Slack only when there are PRs to report. Setup Add GitHub credentials for the HTTP Request nodes (a Personal Access Token in an HTTP Header Auth credential with access to the target repositories). Add a Slack OAuth2 connection and ensure the workflow can post to the target channel. Update the configuration values (repositories, stale_days, slack_channel, and exclude_drafts) to match your GitHub org and Slack workspace. Confirm the schedule trigger cron expression (0 9 * * 1-5) matches your intended timezone and weekdays.

📊 Log BTC/ETH Prices and USD Exchange Rates to Notion (Hourly) 📌 Overview This workflow automatically logs live crypto prices (Bitcoin & Ethereum) and fiat exchange rates (USD→EUR / USD→NGN) into a Notion database every hour. Each entry becomes a new row in your Notion dashboard, letting you visualize currency and crypto trends side by side. It’s perfect for traders, analysts, and anyone who wants a single source of truth in Notion without needing multiple apps open. With hourly updates, you’ll have a clean data history for building rollups, trend graphs, or financial dashboards. ⚙️ How it works Schedule Trigger — runs every hour (adjustable via cron). HTTP Request (ExchangeRate-API) — fetches USD-base FX rates (no API key required). HTTP Request (CoinGecko) — fetches BTC & ETH prices + 24h % change (no API key required). Merge — combines both payloads. Code (v2) — formats a Notion-ready JSON payload with the correct fields. Notion Node — creates a new page in your database with mapped properties. Example Row in Notion: Title: Crypto+FX — 2025-09-08 09:00 BTC: 112,417 | BTC_24h: +1.22% ETH: 4,334.57 | ETH_24h: +1.33% USD→EUR: 0.854 | USD→NGN: ₦1,524.54 🛠 Setup Guide 1) Create the Notion database In Notion, create a new database (Table view). Add these columns with matching property types: | Column | Property Type | |------------|---------------| | Title | Title | | BTC | Number | | BTC_24h | Number | | ETH | Number | | ETH_24h | Number | | USD_EUR | Number | | USD_NGN | Number | 2) Connect Notion in n8n In the Notion “Create Page” node, connect with your Notion OAuth2 credentials. On first use, you’ll be redirected to authorize n8n with your Notion workspace. Copy your Database ID (from the Notion URL) and paste it into the node. 3) Map the Code output The Code node outputs JSON fields: BTC, BTC_24h, ETH, ETH_24h, USD_EUR, USD_NGN. In the Notion node, map each property: BTC → {{$json.BTC}} BTC_24h → {{$json.BTC_24h}} ETH → {{$json.ETH}} ETH_24h → {{$json.ETH_24h}} USD_EUR → {{$json.USD_EUR}} USD_NGN → {{$json.USD_NGN}} 4) Test Run the workflow once. Confirm that a new page is added to your Notion database with all values filled. 🎛 Customization Cadence:** change the schedule to 10 minutes, 4 hours, or daily depending on your needs. Extra coins:** add more IDs (e.g., solana, bnb) in the CoinGecko call and update the Code node. Extra FX pairs:** expand from ExchangeRate-API (e.g., USD→GBP, USD→ZAR). Notion dashboards:** use rollups, charts, and linked databases for trend visualization. Formatting:** add emojis, colors, or sections in your Notion view for clarity. 🧩 Troubleshooting Page not created:** verify Database ID and ensure the Notion API integration has access. Empty fields:** check that property names in Notion exactly match those used in the Code node. Wrong data type:* make sure properties are set as *Number**, not Text. Rate limits:** CoinGecko and ExchangeRate-API are free but may rate-limit if called too often; keep cadence reasonable (hourly recommended).

What Is This? This workflow is an automated employee time tracking and reporting system that monitors weekly work hours via TMetric, then delivers personalized summaries directly to each team member on Slack. It compares actual logged hours against individual work schedules, accounts for approved time-off requests, and flags anyone falling behind their hourly quota. It works with any team using TMetric for time tracking and Slack for communication, making it ideal for remote-first companies, IT agencies, and consulting firms managing distributed teams. Who Is It For? Designed for team leads, operations managers, and HR departments in small to medium-sized companies who want effortless visibility into employee time tracking compliance — without micromanaging or manually pulling reports. Software development agencies, MSPs, and consulting firms billing clients by the hour will find this especially valuable, as it keeps employees accountable and ensures time entries are complete and up to date before payroll or invoicing cycles. How Does It Work? The workflow operates in two modes: a one-time Setup and a recurring Weekly Report. Setup builds a mapping table between TMetric and Slack user accounts. It checks whether the required N8N Data Table already exists, creates it if not, then fetches all TMetric and Slack users. Each Slack user receives a message asking them to identify their corresponding TMetric account via an interactive dropdown form. Responses are saved to the Data Table for use in subsequent runs. Weekly Report runs automatically every Friday at 4:00 PM. It fetches users, their work schedules, and approved time-off requests from TMetric, then processes each employee individually — retrieving their time entries, filtering out any currently running tasks, and calculating total hours worked. The workflow accounts for time-off when determining expected hours, then computes how far ahead or behind schedule each person is. Finally, it looks up each user's Slack ID from the mapping table and sends them a personalized Block Kit message summarizing their week: hours worked, schedule adherence status, and a project-by-project breakdown. How To Set It Up? Prerequisites: An active N8N account or self-hosted instance A TMetric account with API access A Slack workspace with bot permissions Step 1: Configure the Globals node tmAccountId** — found in your TMetric URL: https://app.tmetric.com/#/tracker/YOUR_ID/ Allow hours missing percentage** — tolerance threshold before flagging a user as behind (default: 10%) tmetricToSlackUserDataTableName** — name of the N8N Data Table used for user mapping (default: Tmetric to Slack user map) Step 2: Set up TMetric authentication Open any HTTP Request node, select Authentication → Generic Credential Type → Header Auth, and create a new credential: Name**: Authorization Value**: <Your TMetric API Key> You can generate an API key in your TMetric account settings. Full API documentation is available here. Step 3: Configure Slack credentials Connect your Slack workspace using OAuth2 in the Slack nodes. Ensure your bot has permissions to list users and send direct messages. Step 4: Run Setup Trigger the Setup manual trigger once. It will create the mapping table and send a form to each Slack user. Once all responses are collected, the workflow is ready for scheduled operation. Step 5: Customize the Slack message (optional) Edit the Send a message node to adjust the message layout, change delivery from DM to a channel, or add additional fields to the report. What's More? Flexible time-off handling: Approved time-off requests are subtracted from expected working hours, ensuring employees aren't penalized for legitimate absences. Running task filtering: Time entries without an end time are excluded from calculations, preventing inflated hours from active timers. Visual status indicators: Slack messages use color-coded action buttons — green for on-track employees, red for those behind schedule — giving recipients immediate visual feedback at a glance. Project breakdown: Each report includes a per-project summary showing time spent across the reporting period, giving employees and managers full transparency into where hours went. Safe re-runs: The Setup flow can be triggered again at any time to onboard new team members or correct mapping mistakes without affecting existing records. Need help? Reach out at developers@sailingbyte.com or visit sailingbyte.com. Happy hacking!

Quick overview This workflow runs monthly to audit Microsoft 365 licensing via Microsoft Graph, identifies inactive users holding premium SKUs (excluding an exemption group and recent hires), optionally downgrades them to a baseline license, and posts a results summary to a Microsoft Teams channel. How it works Runs on a monthly cron schedule at 06:00 on the 1st of each month. Loads and validates configuration values (premium SKU part numbers, baseline SKU ID, inactivity/new-hire windows, exemption group ID, Teams destination, and dry-run mode). Calls Microsoft Graph to list subscribed SKUs and builds a list of premium SKU IDs to target. Retrieves members of a Microsoft Entra ID (Azure AD) security group to exclude them from license changes. Fetches all enabled member users with assigned licenses from Microsoft Graph and filters for users who hold a premium SKU and have been inactive beyond the threshold (treating no sign-in history as inactive except for recent hires). If no users qualify, posts a “no action required” audit message to the configured Microsoft Teams channel. If users qualify, processes them in batches with a short wait between calls and either performs a dry run or uses Microsoft Graph assignLicense to remove premium licenses and add the baseline SKU per user. Posts Microsoft Teams error alerts for failed license operations and sends a final Microsoft Teams summary listing the successfully processed accounts. Setup Create a Microsoft Graph OAuth2 credential in n8n with permissions to read users and groups and to update user license assignments. Update the Config values for baselineSkuId, exemptionGroupId, teamsTeamId, teamsChannelId, and (optionally) premiumSkuPartNumbers and the inactivity/new-hire thresholds. Ensure the exemption group exists in Microsoft Entra ID and contains any users who must never be downgraded. Connect Microsoft Teams credentials in n8n and verify the workflow can post to the target team and channel. Set dryRunMode to true for testing, then disable it to allow live license changes. Requirements Microsoft 365 tenant with Azure AD Premium P1 or P2 (required for signInActivity data) App Registration in Entra ID with admin-consented application permissions: User.Read.All, AuditLog.Read.All, User.ReadWrite.All, Organization.Read.All, Directory.Read.All, GroupMember.Read.All Microsoft Graph OAuth2 credential configured in n8n and selected on the Fetch All Licensed Users node Microsoft Teams credential configured in n8n with access to the target team and channel An Entra ID Security Group populated with any accounts that should never be downgraded (break-glass, service accounts, executives) The skuId of your target baseline license plan (retrieve it by calling GET https://graph.microsoft.com/v1.0/subscribedSkus) Customization Adjust the cron expression on Monthly Schedule Trigger at 6am to match your preferred schedule and UTC offset. Update the Teams message templates in Notify No Downgrades Required, Alert License Error, and Deliver Summary to IT Manager to match your organization's tone and formatting standards.

Sync QuickBooks Chart of Accounts to Google BigQuery Keep a historical, structured copy of your QuickBooks Chart of Accounts in BigQuery. This n8n workflow runs weekly, syncing new or updated accounts for better reporting and long-term tracking. Who Is This For? Data Analysts & BI Developers** Build a robust financial model and analyze changes over time. Financial Analysts & Accountants** Track structural changes in your Chart of Accounts historically. Business Owners** Maintain a permanent archive of your financial structure for future reference. What the Workflow Does Extract** Every Monday, fetch accounts created or updated in the past 7 days from QuickBooks. Transform** Clean the API response, manage currencies, create stable IDs, and format the data. Format** Convert cleaned data into an SQL insert-ready structure. Load** Insert or update account records into BigQuery. Setup Steps 1. Prepare BigQuery Create a table (e.g., quickbooks.accounts) with columns matching the final SQL insert step. 2. Add Credentials Connect QuickBooks Online and BigQuery credentials in n8n. 3. Configure the HTTP Node Open 1. Get Updated Accounts from QuickBooks. Replace the Company ID {COMPANY_ID} with your real Company ID. Press Ctrl + Alt + ? in QuickBooks to find it. 4. Configure the BigQuery Node Open 4. Load Accounts to BigQuery. Select the correct project. Make sure your dataset and table name are correctly referenced in the SQL. 5. Activate Save and activate the workflow. It will now run every week. Requirements QuickBooks Online account QuickBooks Company ID Google Cloud project with BigQuery and a matching table Customization Options Change Sync Frequency** Adjust the schedule node to run daily, weekly, etc. Initial Backfill** Temporarily update the API query to select * from Account for a full pull. Add Fields** Modify 2. Structure Account Data to include or transform fields as needed.

How It Works This workflow automates real estate lead qualification and routing by enriching leads from multiple sources with AI-powered analysis and directing them to appropriate sales agents based on priority. Designed for real estate brokers, sales managers, and lead generation teams, it solves the critical challenge of quickly identifying high-value prospects from high-volume lead streams while ensuring timely agent follow-up. The system triggers on schedule, fetches leads simultaneously from MLS portals and CRM/email sources, aggregates all leads into unified dataset, splits leads for parallel processing, then deploys AI agents using Anthropic's Claude to analyze lead quality, buying intent, budget capacity, and urgency. Based on enrichment scores, leads are routed to best-fit agents by priority tier, with engagement tracking logged to Google Sheets for performance monitoring and optimization. Setup Steps Configure Schedule Trigger with desired lead processing frequency Set up MLS/portal integration API credentials in Fetch Leads from MLS/Portals node Configure CRM/email system API access in Fetch Leads from CRM/Email node Connect Anthropic API credentials for AI Lead Enrichment Agent Customize Structured Output Parser with your lead scoring criteria Update Check Lead Priority node with priority threshold rules Configure agent routing logic in Route to Best-Fit Agent nodes by priority tier Prerequisites Active Anthropic API account, MLS/real estate portal API access, CRM system with API integration Use Cases Inbound lead qualification from property portals, open house inquiry processing, email campaign lead scoring Customization Modify AI enrichment prompts for market-specific criteria, adjust priority scoring algorithms for business goals Benefits Reduces lead response time by 75%, ensures high-value prospects receive immediate attention

How it works Runs every 6 hours to pull contacts needing a data refresh from HubSpot All contacts are bulk-enriched with Lusha in a single API call Each contact is scored against your ICP criteria (seniority, company size, industry) CRM records are updated with fresh data; high-scoring fast-track leads trigger a Slack alert Set up steps Install the Lusha community node Add your Lusha API, HubSpot, and Slack credentials Customize the ICP scoring weights in the Code node Set the Slack channel for fast-track alerts and activate

Automatic Clean Up Expired AWS ACM Certificates with Human Approval > Automate the cleanup of expired AWS ACM certificates with Slack-based approval. This workflow helps maintain a secure and tidy AWS environment by detecting expired SSL certs, sending detailed Slack notifications to admins, and deleting them upon approval, ensuring full visibility and control over certificate lifecycle management. 🧑‍💼 Who’s it for This workflow is designed for: AWS administrators** who want to keep their environment clean and secure DevOps teams** managing SSL lifecycle in AWS ACM IT Admins** needing visibility and control over expired cert removal Teams that use Slack for collaboration and approvals ⚙️ How it works / What it does This automated workflow performs the following tasks on a daily schedule: Fetch all ACM certificates in your AWS account. Filter out the expired ones by comparing expiration date and status. Send a Slack approval message with certificate details to the admin team. Wait for approval response directly in Slack (✅ to approve deletion). If approved, it deletes the expired certificate using AWS ACM. Finally, it notifies the IT admin about the action taken. 🔧 How to set up Create the Workflow Add the nodes as shown: Schedule Trigger AWS - ACM: listCertificates AWS - ACM: describeCertificate (loop per cert) IF Node to filter expired certs Slack - Send & Wait for Reaction AWS - ACM: deleteCertificate Slack - Post Message to notify Configure Slack Create a Slack Bot Token with: chat:write reactions:read channels:read Connect it in your Slack nodes. Configure AWS Credentials Use IAM User or Role with: acm:ListCertificates acm:DescribeCertificate acm:DeleteCertificate Set schedule Daily, Weekly, or custom cron expression. 📋 Requirements | Component | Description | |------------------|--------------------------------------| | AWS ACM Access | IAM permissions for ACM actions | | Slack Bot Token | With chat:write & reactions:read | | n8n Environment | Self-hosted or n8n Cloud | | Slack Channel | Where approval messages will be sent | 🛠️ How to customize the workflow 🕒 Change waiting time Adjust the wait time before checking Slack reactions in the sendAndWait node (default 1 hour). 👥 Change Slack target Change the Slack channel or tag specific people (<@U123456>). 📓 Add logging Add Google Sheets, Notion, or DynamoDB to log certificate details and approval decisions. 🧪 Add dry-run/test mode Use an IF node before deletion to simulate removal when ENV === dry-run.

Description Keep your CRM and task system perfectly in sync — automatically. This workflow monitors a Google Sheet for lead reply updates, instantly updates the corresponding contact in GoHighLevel (GHL), and creates a ClickUp follow-up task when a response is detected. It ensures your sales team never misses a warm lead and every reply is logged, tracked, and acted upon. ⚡ What This Template Does Watches a Google Sheet for reply status changes (“Yes” in the Replied column). 📄 Filters only rows where a lead has replied. 🔍 Updates the corresponding contact record in GoHighLevel (GHL). 🔗 Automatically creates a follow-up task in ClickUp with lead details. 🗂️ Merges updates from both GHL and ClickUp into a single result. 🔄 Logs sync status and timestamps in a tracking sheet (Sheet2) for auditing. 📋 Key Benefits Instant sync: Update GHL and ClickUp the moment a lead replies. ⚙️ Zero manual work: Automated updates and follow-up task creation. 🚫 Full traceability: Audit trail stored in a second Google Sheet. ✅ Increased sales responsiveness: Teams act immediately on new replies. 💬 Multi-app harmony: Connects Google Sheets, GHL, and ClickUp seamlessly. 🌐 Features Google Sheets trigger runs every minute to check for changes. ⏱️ Conditional logic (If node) processes only “Replied = Yes” leads. ✅ GHL contact update node for CRM synchronization. 💼 ClickUp task creation node with customizable priority and naming. 📝 Merge node to unify both paths before final sheet update. 🔀 Timestamp and action tracking for audit clarity. 📊 Visual sticky notes explaining each stage. 🧩 Requirements n8n instance (cloud or self-hosted). 🧰 Google Sheet with headers: Name, GHL_ID, Replied, Email. 📑 Connected credentials for: Google Sheets API (Trigger + Update access) GoHighLevel OAuth2 API ClickUp API Separate “log” sheet (Sheet2) for sync tracking. 📄 Target Audience Sales teams using GoHighLevel for CRM and ClickUp for task management. 🎯 Agencies managing multiple client pipelines. 🤝 Business development reps who track lead replies manually. 📞 Founders automating lead follow-up and CRM hygiene. 🚀 Step-by-Step Setup Instructions (Concise) Prepare a Google Sheet with columns: Name, GHL_ID, Email, Replied. 📋 Add a second sheet (“Sheet2”) for tracking updates. 📊 Connect credentials for Google Sheets, GoHighLevel, and ClickUp in n8n. 🔐 Import and open this workflow. Update GHL contact field mappings if your CRM structure differs. ⚙️ Adjust ClickUp team, space, and list IDs to match your setup. 📂 Execute manually once for testing, then enable it to run every minute. ▶️ Security Best Practices Share your Google Sheet only with the n8n Google account (Editor). 🔒 Keep API credentials securely stored in n8n’s credential manager. 🛡️ Log timestamps for traceability and rollback. ⏳ Periodically archive older sync logs to keep the sheet lightweight. 🧹