Workflow Templates

Automated Job Hunt with Tavily — Setup & Run Guide What this template does? Automatically searches for recent job postings (example: “Software Engineering Intern”), extracts structured details from each posting using an AI agent + Tavily, bundles results, and emails a single weekly digest. Estimated setup time: ~30 minutes 1. Required credentials Before you import or run the workflow, create/configure these credentials in your n8n instance: OpenAI (Chat model) — used by the OpenAI Chat Model and Message a model nodes. Add an OpenAI credential (name it e.g. OpenAi account) and paste your OpenAi API key. Tavily API — used by the Search in Tavily node. Add a Tavily credential (name it e.g. Tavily account) and add your Tavily API key. Gmail (OAuth2) — used by the Send a message node to deliver the digest email. Configure Gmail OAuth2 credential and select it for the Gmail node (e.g. Gmail account. 2. Node-by-node configuration (what to check/change) Schedule Trigger Node name: Schedule Trigger Configure interval: daily or weekly (example: weekly, trigger at 08:00). Note: This is the workflow start. Adjust to your preferred cadence. AI Agent Node name: AI Agent Important: First step — set the agent’s prompt / system message. Search in Tavily (Tavily Tool node) Node name: Tavily Query: user-editable field (example default: Roles posted this week for Software Engineering) Advice: keep query under 400 chars; change to target role/location keywords. Options recommended: Search Depth: advanced (optional, better extraction) Max Results: 15 Time Range: week (limit to past week) Include Raw Content: true (fetch full page content for better extraction) Include Domains: indeed.com, glassdoor.com,linkedin.com — prioritize trusted sources Edit Fields / Set (bundle) Node name: Edit Fields (Set) Purpose: Collect the agent output into one field (e.g., $json.output or Response) for downstream processing. Message a Model (OpenAI formatting step) Node name: Message a model Uses OpenAI (the openAiApi credential). This node can be used to reformat or normalize the agent output into consistent blocks if needed. Use the same system rules you used for the agent (the prompt/system message earlier). You can also leave this minimal if the agent already outputs structured blocks. Code Node (Parsing & structuring) Node name: Code Purpose: Split the agent/LLM text into separate job postings and extract fields with regex. Aggregate Node Node name: Aggregate Mode: aggregateAllItemData (this combines all parsed postings into a single data array so the Gmail node can loop over them) Gmail node (Send a message) Node name: Send a message sendTo: set to recipient(s) (e.g., your inbox) subject: e.g. New Jobs for this week! emailType: text (or html if you build HTML content) message (body): use the expression that loops through data and formats every posting. 3. How to test (quick steps) Set credentials in n8n (OpenAI, Tavily, Gmail). Run the Schedule Trigger manually (use the “Execute Workflow” or manually trigger nodes). Inspect the Search in Tavily node output — confirm it returns results. Inspect the AI Agent and Message a model outputs — ensure formatted postings are produced and separated by --- END JOB POSTING ---. Run the Code node — confirm it returns structured items with posting_number, job_title, requirements[], etc. Check Aggregate output: you should see a single item with data array. In Gmail node, run a test send — confirm the email receives one combined message with all postings. 4. Troubleshooting tips Gmail body shows [Array: …]: Avoid dragging the array raw — use the expression that maps data to formatted strings. Code node split error: Occurs when raw is undefined. Ensure previous node returns message.content or adjust to use $input.all() and join contents safely. Missing fields after parsing: Check LLM/agent output labels match the Code node’s regex (e.g., Job Title:). If labels differ, update regex or LLM formatting. 5. Customization ideas Filter by location or remote-only roles, or add keyword filters (seniority, stack). Send results to Google Sheets or Slack instead of/in addition to Gmail. Add an LLM summarization step to create a 1-line highlight per posting.

This workflow automatically classifies every new email from your linked mailbox, drafts a personalized reply, and creates Linear tickets for bugs or feature requests. It uses a human-in-the-loop with gotoHuman and continuously improves itself by learning from approved examples. How it works The workflow triggers on every new email from your linked mailbox. Self-learning Email Classifier: an AI model categorizes the email into defined categories (e.g., Bug Report, Feature Request, Sales Opportunity, etc.). It fetches previously approved classification examples from gotoHuman to refine decisions. Self-learning Email Writer: the AI drafts a reply to the email. It learns over time by using previously approved replies from gotoHuman, with per-classification context to tailor tone and style (e.g., different style for sales vs. bug reports). Human Review in gotoHuman: review the classification and the drafted reply. Drafts can be edited or retried. Approved values are used to train the self-learning agents. Send approved Reply: the approved response is sent as a reply to the email thread. Create ticket: if the classification is Bug or Feature Request, a ticket is created by another AI agent in Linear. Human Review in gotoHuman: How to set up Most importantly, install the gotoHuman node before importing this template! (Just add the node to a blank canvas before importing) Set up credentials for gotoHuman, OpenAI, your email provider (e.g. Gmail), and Linear. In gotoHuman, select and create the pre-built review template "Support email agent" or import the ID: 6fzuCJlFYJtlu9mGYcVT. Select this template in the gotoHuman node. In the "gotoHuman: Fetch approved examples" http nodes you need to add your formId. It is the ID of the review template that you just created/imported in gotoHuman. Requirements gotoHuman (human supervision, memory for self-learning) OpenAI (classification, drafting) Gmail or your preferred email provider (for email trigger+replies) Linear (ticketing) How to customize Expand or refine the categories used by the classifier. Update the prompt to reflect your own taxonomy. Filter fetched training data from gotoHuman by reviewer so the writer adapts to their personalized tone and preferences. Add more context to the AI email writer (calendar events, FAQs, product docs) to improve reply quality.

🚀 AI Lead Generation and Follow-Up Template 📋 Overview This n8n workflow template automates your lead generation and follow-up process using AI. It captures leads through a form, enriches them with company data, classifies them into different categories, and sends appropriate follow-up sequences automatically. Key Features: 🤖 AI-powered lead classification (Demo-ready, Nurture, Drop) 📊 Automatic lead enrichment with company data 📧 Intelligent email responses and follow-up sequences 📅 Automated demo scheduling for qualified leads 📝 Complete lead logging in Google Sheets 💬 AI assistant for immediate query responses 🛠️ Prerequisites Before setting up this workflow, ensure you have: n8n Instance: Self-hosted or cloud version OpenAI API Key: For AI-powered features Google Workspace Account with: Gmail access Google Sheets Google Calendar Basic understanding of your Ideal Customer Profile (ICP) ⚡ Quick Start Guide Step 1: Import the Workflow Copy the workflow JSON Import into your n8n instance The workflow will appear with all nodes connected Step 2: Configure Credentials You'll need to set up the following credentials: OpenAI API**: For AI agents and classification Gmail OAuth2**: For sending emails Google Sheets OAuth2**: For lead logging Google Calendar OAuth2**: For demo scheduling Step 3: Create Your Lead Log Sheet Create a Google Sheet with these columns: Date Name Email Company Job Title Message Number of Employees Industry Geography Annual Revenue Technology Pain Points Lead Classification Step 4: Update Configuration Nodes Replace Sheet ID: Update all Google Sheets nodes with your sheet ID Update Email Templates: Customize all email content Set Escalation Email: Replace "your-email@company.com" with your team's email Configure ICP Criteria: Edit the "Define ICP and Lead Criteria" node 🎯 Lead Classification Setup Define Your ICP (Ideal Customer Profile) Edit the "Define ICP and Lead Criteria" node to set your criteria: 📌 ICP Criteria Example: Company Size: 50+ employees Industry: SaaS, Finance, Healthcare, Manufacturing Geography: North America, Europe Pain Points: Manual processes, compliance needs, scaling challenges Annual Revenue: $5M+ ✅ Demo-Ready Criteria: High-intent prospects who meet multiple qualifying factors: Large company size (your threshold) Clear pain points mentioned Urgent timeline Budget authority indicated Specific solution requests 🌱 Nurture Criteria: Prospects with future potential: Meet basic size requirements In target industry General interest expressed Planning future implementation Exploring options ❌ Drop Criteria: Only drop leads that clearly don't fit: Outside target geography Wrong industry (B2C if you're B2B) Too small with no growth Already with competitor Spam or test messages 📧 Email Customization Customize Follow-Up Sequences: Demo-Ready Sequence: Immediate calendar invitation Personalized demo confirmation Meeting reminder (optional) Nurture Sequence: Welcome email with resources Educational content (Day 2) Webinar/event invitation (Day 3) Demo offer (Day 4) Drop Message: Polite acknowledgment Clear explanation Keep door open for future 🔧 Advanced Configuration AI Answer Agent Setup: Update the system prompt with your company information Add common Q&A patterns Set escalation rules Configure language preferences Lead Enrichment Options: Add API keys for additional data sources Configure enrichment fields Set data quality thresholds Enable duplicate detection Calendar Integration: Set available meeting times Configure meeting duration Add buffer times Set timezone handling 📊 Monitoring and Optimization Track Key Metrics: Lead volume by classification Response rates Demo conversion rates Time to first response Enrichment success rate Optimization Tips: Regular Review: Check classification accuracy weekly A/B Testing: Test different email sequences Feedback Loop: Use outcomes to refine ICP criteria AI Training: Update prompts based on results 🎉 Best Practices Start Simple: Begin with basic criteria and refine over time Test Thoroughly: Use test leads before going live Monitor Daily: Check logs for the first week Iterate Quickly: Adjust based on results Document Changes: Keep track of criteria updates 📈 Scaling Your Workflow As your lead volume grows: Add Sub-workflows: Separate complex processes Implement Queuing: Handle high volumes Add CRM Integration: Sync with your sales tools Enable Analytics: Track detailed metrics Set Up Alerts: Monitor for issues

CV → Match → Screen → Decide, all automated This workflow automatically processes candidate CVs from email, intelligently matches them to job descriptions, performs AI-powered screening analysis, and sends actionable summaries to your team in Slack. Good to know Handles both PDF and Word document CVs automatically Two-stage JD matching: prioritizes role mentioned in candidate's email, falls back to CV analysis if needed Uses Google Gemini API for AI screening (generous free tier and rate limits, typically enough to avoid paying for API requests, but check latest pricing at Google AI Pricing) All CVs stored in Google Drive with standardized naming (candidate name + date/time) Complete audit trail logged in Google Sheets Who's it for Hiring teams and recruiters who want to automate first-round CV screening while maintaining quality. Perfect for companies receiving high volumes of applications across multiple roles, especially in tech, sales, or automation-focused positions. How it works Gmail monitors inbox for CVs with specific label and downloads attachments Detects file type (PDF or Word) and converts/standardizes format for text extraction AI agent matches candidate to best-fit job description by analyzing email context first (if candidate mentioned a role), or CV content as fallback (selects up to 3 potential JD matches) If multiple JDs matched, second AI agent selects the single best fit AI recruiter agent analyzes CV against selected JD and generates structured screening report (strengths, weaknesses, risk/reward factors, overall fit score 0-10 with justification) Extracts candidate details (name, email) from CV text Logs complete analysis to Google Sheets tracker Sends formatted summary to Slack with Proceed/Reject action buttons for instant team decisions Requirements Gmail account with API access Google Drive account (OAuth2) Google Sheets account (OAuth2) Slack workspace with bot permissions Google Gemini API key (Get free key here) Google Drive folders: one for CVs, one for Job Descriptions (as PDFs or Google Docs) How to set up Add credentials: Gmail OAuth2, Google Drive OAuth2, Google Sheets OAuth2, Slack OAuth2, Google Gemini API Create Gmail label (e.g., "CV-Screening") for incoming candidate emails In "Receive CV via Email" node: select your Gmail label for filtering Create two Google Drive folders: "Candidate CVs" and "Job Descriptions" In "Upload CV - PDF" and "Stream Doc/Docx File" nodes: update folder ID to your "Candidate CVs" folder In "Access JD Files" node: update folder ID to your "Job Descriptions" folder Create Google Sheet named "AI Candidate Screening" with columns matching the sample AI Candidate Screening sheet In "Append row in sheet" node: select your Google Sheet In "Send Candidate Screening Confirmation" node: select your Slack channel Activate workflow Customizing this workflow Change JD matching logic: Edit "JD Matching Agent" node prompt to adjust how CVs are matched to roles (e.g., weight technical skills vs. experience). Change "Company Description" in AI prompts: Insert your "Company Description" in System Message sections in "JD Matching Agent" and "Detailed JD Matching Agent" nodes Modify screening criteria: Edit "Recruiter Scoring Agent" node system message to focus on specific qualities (culture fit, leadership, technical depth, etc.) Add more storage locations: Add nodes to save CVs to other systems (Notion, Airtable, ATS platforms) Customize Slack message: Edit "Send Candidate Screening Confirmation" node to change formatting, add more context, or include additional candidate data Auto-proceed logic: Add IF node after screening to auto-proceed candidates with fit score above threshold (e.g., 8+/10) Add email responses: Connect nodes to automatically email candidates (confirmation, rejection, interview invite) Add human-in-the-loop: Sub-workflow triggered by Slack response or email response, to update Sheet with approve/reject status Add candidate email responses + interview scheduling**: For approved candidates, trigger email to candidate with Cal.com or Calendly link so they can book their interview Quick Troubleshooting No CVs being processed: Check Gmail label is correctly set in "Receive CV via Email" node and emails are being labeled Word documents failing: Verify "Stream Doc/Docx File" node has correct parent folder ID and Google Drive credentials authorized JD matching returns no results: Check "Access JD Files" node folder ID points to your Job Descriptions folder, and JD files are named clearly (e.g., "Marketing Director JD.pdf") JD matching is not relevant for my company: Update the "Company Description" in the System Messages in the "JD Matching Agent" and "Detailed JD Matching Agent" nodes "Can't find matching JD": Ensure candidate's email mentions role name OR their CV clearly indicates relevant experience for available JDs Google Sheets errors: Verify sheet name is "AI Candidate Screening" and column headers exactly match workflow expectations (Submission ID, Date, CV, First Name, etc.) Slack message not appearing: Re-authorize Slack credentials and confirm channel ID in "Send Candidate Screening Confirmation" node Missing candidate name/email: CV text must be readable - check PDF extraction quality or try converting complex CVs to simpler format 401/403 API errors: Re-authorize all OAuth2 credentials (Gmail, Google Drive, Google Sheets, Slack) AI analysis quality issues: Edit system prompts in "JD Matching Agent" and "Recruiter Scoring Agent" nodes to refine screening criteria Sample Outputs Google Sheets - AI Candidate Screening - sample Slack confirmation message Acknowledgments This workflow was inspired by Nate Herk's YouTube demonstration on building a resume analysis system. This implementation builds upon that foundation by adding dynamic job description matching (initial + detailed JD matching agents), Slack Block Kit integration with interactive buttons, updated Google Drive API methods for document handling, and enhanced candidate data capture in Google Sheets.

Build AI-powered pre-call intelligence briefs from LinkedIn profiles Automatically scrape LinkedIn profiles for upcoming sales calls, analyze the data with AI, score prospect readiness, and send structured pre-call briefs to your inbox. This workflow reads upcoming call records from Google Sheets, scrapes each prospect's LinkedIn profile with Bright Data, extracts useful talking points and rapport hooks, and uses GPT-5.5 to generate a structured pre-call intelligence brief. It then applies confidence and readiness thresholds to decide whether to send a full brief, a shorter limited-data brief, or log the result for later review. How it works A schedule trigger runs the workflow automatically. The workflow reads upcoming prospect records from a Google Sheet tab called upcoming_calls. Each LinkedIn profile URL is sent to the Bright Data dataset API for scraping. The Bright Data response is validated to catch empty results, API errors, and async snapshot responses. A code step extracts useful sales context such as: career trajectory role tenure skills and tech affinity education signals recent activity themes rapport hooks GPT-5.5 analyzes the enriched profile data and returns a structured JSON brief with: career trajectory summary recent activity summary 5 tailored talking points likely pain points readiness score recommended approach risk factors self-evaluation metadata The workflow parses the AI response and checks whether confidence is at least 0.7. If confidence is too low, the result is written to a low_confidence_briefs sheet for manual review. If confidence is high enough, the workflow checks whether the readiness score is at least 70. High-readiness prospects receive a full email brief and are logged to the call_briefs sheet. Lower-readiness prospects receive a shorter email brief and are also logged to the call_briefs sheet. Setup Create a Google Sheet with these tabs: upcoming_calls call_briefs low_confidence_briefs The upcoming_calls tab should include at least: url meeting_date our_product alert_email (optional) Then configure the workflow: Replace YOUR_SPREADSHEET_ID in the Google Sheets nodes with your sheet ID. Connect Google Sheets via OAuth2. Add your Bright Data credentials to the HTTP Header Auth used in the LinkedIn scraping node. Connect your OpenAI account in the GPT-5.5 chat model node. Connect Gmail via OAuth2 for sending the brief emails. Optionally set a fallback alert email using the ALERT_EMAIL variable. Requirements Google Sheets OAuth2 credentials Gmail OAuth2 credentials Bright Data account OpenAI API access for GPT-5.5 Notes This is a demo workflow intended for template and educational use. The Bright Data response validator is built to handle empty results, API errors, and async responses gracefully. The AI node is instructed to return raw JSON only, making the output easier to parse and route. The confidence gate helps prevent unreliable AI output from being emailed. The readiness score gate helps prioritize prospects that are more likely to lead to productive calls. You can adjust the confidence threshold (0.7) and readiness threshold (70) to match your process. The Gmail steps currently send plain-text summaries, which makes the workflow easy to customize. Good fit for sales teams founders doing outbound agencies preparing for discovery calls account executives who want better call prep RevOps teams building lightweight pre-call research systems Note: This demo uses the synchronous Bright Data request because it is easier to understand in a template. In real workflows, it is often better to use the async approach. Bright Data’s sync endpoint has a 1 minute timeout, and longer jobs return a snapshot_id instead of final data. A simple way to build the async HTTP node in n8n is to copy Bright Data’s cURL example from your account or docs and convert it into an n8n HTTP Request setup

Generate Personalized & Aggregate Survey Reports with Jotform and Gemini AI Overview Automatically transform Jotform survey responses into intelligent, professional reports. This workflow generates personalized insights for each respondent and statistical summaries for administrator, all hands-free. Who Should Use This Survey managers needing automated report generation Market researchers analyzing response data Product teams collecting customer feedback Organizations using Jotform without built-in analytics What It Does Two-Part Report System: Personal Reports (Instant) Triggers immediately when respondent submits survey AI analyzes their individual responses using Google Gemini Generates customized insights and recommendations Sends professional HTML report to respondent's email Weekly Aggregate Reports (Scheduled) Runs automatically every week Collects all survey submissions Calculates statistics, percentages, and trends Identifies patterns across all respondents Sends comprehensive analysis to admin Key Features ✓ Real-time personal report generation ✓ Intelligent AI-powered analysis (Google Gemini) ✓ Professional HTML email formatting ✓ Automatic weekly summaries ✓ Statistical analysis and trend identification ✓ Zero manual processing required ✓ Fully customizable prompts and styling ✓ Works with any Jotform survey structure Setup Requirements Jotform** account with active survey form Get Jotform from here n8n** instance (cloud or self-hosted) Google Gemini API** key Gmail** account (for sending reports) Jotform API** key What You Get in Reports Personal Reports Include: Respondent Profile** – Auto-extracted demographics (age, role, location, email) Key Insights** – 3-4 AI-generated insights specific to their responses Personalized Recommendations** – 3-4 actionable suggestions based on their answers Professional Formatting** – HTML-styled email with your branding colors Mobile Responsive** – Looks great on all devices Fully Customizable: Edit the AI prompt to generate different types of insights Change HTML styling (colors, fonts, layout) Add/remove sections (logos, footers, additional analysis) Adjust the tone (professional, casual, technical, etc.) Include custom branding and messaging Aggregate Reports Include: Total Respondents Count** – How many submissions in the period Demographic Breakdown** – Distribution of respondent profiles Response Statistics** – Percentages and frequencies for each question Answer Distribution** – Most popular choices across all responses Trend Analysis** – Patterns and correlations in the data Key Findings** – Top 5-7 insights from all responses combined Statistical Metrics** – Averages, frequencies, comparisons Fully Customizable: Choose which statistics to calculate and display Change how data is visualized and presented Customize report styling and branding Adjust analysis depth and metrics focus Add custom sections for your specific needs Modify HTML layout and design How Reports Look Personal Report Structure (Email): Header: Professional gradient background with thank you message Section 1: Respondent Details (extracted from survey) Section 2: Key Insights (AI-generated from their responses) Section 3: Recommendations (personalized suggestions) Footer: Thank you message and company info Aggregate Report Structure (Email): Header: Report title and date range Section 1: Total respondent count and demographics Section 2: Question-by-question response breakdown Section 3: Statistical analysis and trends Section 4: Key findings and patterns discovered Section 5: Actionable insights for decision-makers Footer: Next report date and company branding Quick Start Get your Jotform Form ID and API key Enable Google Gemini API and create API key Set up Gmail OAuth2 credentials in n8n Import this workflow Add your credentials to the nodes Test with a sample survey submission Complete setup instructions are included in the workflow as an expandable sticky note. Workflow Logic PERSONAL REPORTS: Survey Submission ↓ Collect Response Data ↓ AI Analysis & Insights Generation ↓ Create Styled HTML Report ↓ Send to Respondent Email AGGREGATE REPORTS: Weekly Schedule Triggers ↓ Fetch All Submissions ↓ Statistical Analysis & Trend Detection ↓ Generate Insights from All Data ↓ Create Summary HTML Report ↓ Send to Admin Email Use Cases Customer Feedback Surveys** – Analyze responses, send personalized insights Product Research** – Track trends across respondents weekly Market Research** – Automated statistical reporting Employee Surveys** – Personalized feedback + company trends Event Feedback** – Instant attendee insights + organizer summary Customer Satisfaction (NPS)** – Personalized follow-ups + trend analysis Lead Qualification** – Auto-analyze prospect responses and route accordingly

Description: More than an alarm. A smart morning experience that adapts to the weather. 🎸☔️☀️ This workflow demonstrates how to upgrade a simple automation into a smart, context-aware system. By integrating OpenMeteo (Weather API), Google Gemini (AI), and Spotify, it creates a personalized DJ experience for your morning. Why is this "Advanced"? Context Awareness: It doesn't just play music; it checks the weather (via OpenMeteo API) to understand the user's environment. AI Persona: Gemini acts as a live DJ, generating commentary that connects the specific Led Zeppelin track to the current weather conditions (e.g., "It's rainy, perfect for 'The Rain Song'"). Data Logging: It logs every wake-up session (Song, Time, Weather) to Google Sheets, creating a personal music history database. Robust Error Handling: Includes logic to detect offline speakers and send fallback alerts. How it works: Check Context: Fetches real-time weather data for your location and checks your Spotify speaker status. Select Music: Picks a random track from Led Zeppelin's top hits. Generate: Gemini generates a unique "Good Morning" script combining the song title and the weather. Action: Plays the music, logs the data to Google Sheets, and emails you the AI's greeting with album art. Setup Requirements: Spotify Premium Google Gemini API Key Google Sheets: Create a sheet named History with headers: date, time, weather, temperature, song, artist. Gmail

Who is this for? This template is perfect for agencies, consultancies, freelancers, and project-based teams who want to eliminate repetitive onboarding tasks. If you're tired of manually creating folders, Slack channels, and project pages every time a new client signs a contract, this automation will save you hours. What this workflow does When a new contract PDF is uploaded to a designated Google Drive folder, this workflow automatically: Parses the filename to extract client name, project name, and contact email Creates a project folder structure in Google Drive with organized subfolders Creates a dedicated Slack channel for project communication Sets up a Notion project page with initial kickoff tasks Logs project details to a master Google Sheet for tracking Drafts a personalized welcome email using OpenAI GPT-4o-mini Notifies your team on Slack with all relevant links when complete Setup steps Time required: ~15 minutes Configure OAuth credentials for Google Drive, Gmail, Google Sheets, Slack, and Notion Add your OpenAI API key for AI-powered email drafting Update the "Set Config Variables" node with your specific IDs: Google Drive parent folder ID Notion database ID Google Sheet ID Slack notification channel ID Set up the trigger folder in Google Drive where contracts will be uploaded Prepare your Google Sheet with columns: Client, Project Code, Notion Link, Slack Channel, Drive Folder Requirements Google Workspace account (Drive, Gmail, Sheets) Slack workspace with bot permissions to create channels Notion workspace with API integration OpenAI API key File naming convention Upload PDF files using this format: ClientName_ProjectName_email@example.com.pdf Example: AcmeCorp_WebsiteRedesign_john@acme.com.pdf How to customize Add more subfolders: Duplicate the "Create Deliverables Subfolder" node Customize the email prompt: Edit the "AI Draft Welcome Email" node Add more Notion properties: Extend the "Create Notion Project Page" node Change notification format: Modify the "Notify Team on Slack" message

Automated Employee Recognition Bot with Slack + Google Sheets + Gmail Description Turn employee recognition into an automated system. This workflow celebrates great work instantly it posts recognition messages on Slack, sends thank-you emails via Gmail, and updates your tracking sheet automatically. Your team feels appreciated. Your HR team saves hours. Everyone wins. ⚙️ How It Works You add a new recognition in Google Sheets. The bot automatically celebrates it in Slack. The employee receives a thank-you email. HR gets notified and the sheet updates itself. 🔧 Setup Steps 1️⃣ Prepare Your Google Sheet Create a sheet called “Employee_Recognition_List” with these columns: Name | Department | Reason | Date | Email | Status | EmailStatus Then add one test row — for example, your own name — to see it work. 2️⃣ Connect Your Apps Inside n8n: Google Sheets:** Connect your Google account so the bot can read the sheet. Slack:** Connect your Slack workspace to post messages in a channel (like #general). Gmail:** Connect your Gmail account so the bot can send emails automatically. 3️⃣ (Optional) Add AI Personalization If you want the messages to sound more natural, add an OpenAI node with this prompt: > “Write a short, friendly recognition message for {{name}} from {{dept}} who was recognized for {{reason}}. Keep it under 2 sentences.” This makes your Slack and email messages feel human and genuine. 4️⃣ Turn It On Once everything’s connected: Save your workflow Set it to Active Add a new row in your Google Sheet The bot will instantly post on Slack and send a thank-you email 🎉

🚀 How it works Monitors your AP inbox for incoming invoices, extracts structured data with AI, runs duplicate and vendor history checks against Supabase, then scores each invoice for fraud risk — routing suspicious ones to Slack and your AP team before any payment is processed. 📬 Gmail Trigger monitors your accounts payable inbox in real time 🤖 AI Agent extracts invoice number, vendor, amount, currency, dates and line items into structured JSON — no manual data entry 🔍 Checks Supabase for duplicate invoice numbers already in the system 🏢 Checks vendor payment history — flags unknown vendors and amount deviations above 50% from the vendor's historical average 🧠 Second AI Agent scores fraud risk: low / medium / high / critical with specific fraud indicators and a recommended action 🚨 High/critical risk — posts a detailed Slack alert to #invoice-alerts and emails your AP manager with a hold notice 🗄️ Logs every processed invoice to Supabase with risk score and status 🛠️ Set up steps Estimated setup time: ~20 minutes Gmail Trigger — connect Gmail OAuth2; point it at your AP inbox OpenAI — connect OpenAI API credential (used by both AI Agent nodes) Supabase — connect Supabase API credential; create two tables: invoices (invoice_number, vendor_name, amount, status, risk_level, created_at) and vendors (vendor_name, avg_amount, total_invoices, flagged) Slack — connect Slack OAuth2; update the channel name #invoice-alerts Gmail (Send) — connect Gmail OAuth2; replace ap-manager@example.com Follow the sticky notes inside the workflow for per-node guidance 📋 Prerequisites Gmail account receiving invoices OpenAI API key (GPT-4o) Supabase project with invoices and vendors tables Slack workspace with an alerts channel Custom Workflow Request with Personal Dashboard kumar@smartflowcraft.com https://www.smartflowcraft.com/contact More free templates https://www.smartflowcraft.com/n8n-templates

Who is this for? This workflow is ideal for Gmail users and teams who receive a high volume of emails and want to streamline inbox management. It suits professionals seeking to organize messages automatically, including sales teams, project managers, support staff, and anyone who benefits from automated email categorization. What problem is this workflow solving? / Use case Manually labeling emails is time-consuming and can lead to inconsistent organization. This automated n8n workflow uses Gmail and OpenAI to analyze incoming messages and apply the appropriate labels, such as "Quotation", "Inquiry", "Project progress", and "Notification", based on content—improving productivity and ensuring important messages are prioritized. What this workflow does The workflow retrieves new Gmail messages, analyzes their content with OpenAI, and automatically assigns pre-defined Gmail labels that match the email’s intent. This ensures emails are sorted efficiently using AI-powered content analysis and Gmail’s labeling system. Setup Ensure Gmail labels (e.g., "Quotation", "Inquiry") are created in your Gmail account. Connect your Gmail and OpenAI accounts as credentials in n8n. Import the workflow into your n8n instance and update node configurations to match your Gmail label names. How to customize this workflow to your needs Edit or add Gmail labels both in your Gmail account and within the workflow logic. Adjust the prompt or parameters sent to OpenAI to better match your categorization style. Expand or refine the list of label categories to fit your team’s or business’s requirements.

📘 Description This workflow analyzes real-time stock market sentiment and intent from public social media discussions and converts those signals into operations-ready actions. It exposes a webhook endpoint where a stock-market–related query can be submitted (for example, a stock, sector, index, or market event). The workflow then scans Twitter/X and Instagram for recent public discussions that indicate buying interest, selling pressure, fear, uncertainty, or emerging opportunities. An AI agent classifies each signal by intent type, sentiment, urgency, and strength. These insights are transformed into a prioritized Asana task for market or research teams and a concise Slack alert for leadership visibility. Built-in validation and error handling ensure reliable execution and fast debugging. This automation removes the need for manual social monitoring while keeping teams informed of emerging market risks and opportunities. ⚠️ Deployment Disclaimer This template is designed for self-hosted n8n installations only. It relies on external MCP tools and custom AI orchestration that are not supported on n8n Cloud. ⚙️ What This Workflow Does (Step-by-Step) 🌐 Receive Stock Market Query (Webhook Trigger) Accepts an external POST request containing a stock market query. 🧾 Extract Stock Market Query from Payload Normalizes and prepares the query for analysis. 🔎 Analyze Social Media for Stock Market Intent (AI) Scans public Twitter/X and Instagram posts to detect actionable market intent signals. 📡 Social Intelligence Data Fetch (MCP Tool) Retrieves relevant social data from external intelligence sources. 🧠 Transform Market Intent Signals into Ops-Ready Actions (AI) Structures insights into priorities, summaries, and recommended actions. 🧹 Parse Structured Ops Payload Validates and safely parses AI-generated JSON for downstream use. 📋 Create Asana Task for Market Signal Review Creates a prioritized task with key signals, context, and recommendations. 📣 Send Market Risk & Sentiment Alert to Slack Delivers an executive-friendly alert summarizing risks or opportunities. 🚨 Error Handler → Slack Alert Posts detailed error information if any workflow step fails. 🧩 Prerequisites • Self-hosted n8n instance • OpenAI and Azure OpenAI API credentials • MCP (Xpoz) social intelligence credentials • Asana OAuth credentials • Slack API credentials 🛠 Setup Instructions Deploy the workflow on a self-hosted n8n instance Configure the webhook endpoint and test with a sample query Connect OpenAI, Azure OpenAI, MCP, Asana, and Slack credentials Set the correct Asana workspace and project ID Select the Slack channel for alerts 🛠 Customization Tips • Adjust intent and sentiment classification rules in AI prompts • Modify task priority logic or due-date rules • Extend outputs to email reports or dashboards if required 💡 Key Benefits ✔ Real-time market sentiment detection from social media ✔ Converts unstructured signals into actionable tasks ✔ Provides leadership-ready Slack alerts ✔ Eliminates manual market monitoring ✔ Built-in validation and error visibility 👥 Perfect For Market research teams Investment and strategy teams Operations and risk teams Founders and analysts tracking market sentiment