by VΓ‘clav Δikl
Description: This sophisticated workflow automates personalized email campaigns for musicians and band managers. The system processes contact databases, analyzes previous Gmail conversation history, and uses AI to generate contextually appropriate emails tailored to different contact categories (venues, festivals, media, playlists). Key Features: Multi-category support**: Bookers, festivals, media, playlist curators Conversation context analysis**: Maintains relationship history from Gmail AI-powered personalization**: Custom prompts for each contact type Multi-language support**: Localized content and prompts Gmail integration**: Automatic draft creation with signatures Bulk processing**: Handle hundreds of contacts efficiently Use Cases: Album/single promotion campaigns Tour booking automation Festival submission management Playlist pitching campaigns Media outreach automation Venue relationship management Perfect For: Independent musicians and bands Music managers and booking agents Record labels with multiple artists PR agencies in music industry Festival organizers (for artist outreach) Required Setup: 1. Credentials & APIs: Gmail OAuth2** (read messages + create drafts permissions) Google Sheets API** (for AutomatizationHelper configuration) OpenAI API** or compatible LLM (for content generation) 2. Required Files: Contact Database** (CSV): Your venue/media/festival contacts AutomatizationHelper** (Google Sheets): Campaign configuration, prompts, links 3. Example Data: π Download Example Files The folder contains: Sample contact database (CSV) AutomatizationHelper template (CSV + Google Sheets) Detailed setup instructions (README) Data Structure: Contact Database Fields: venue_name - Organization name category - booker/festival/media/playlisting email_1 - Primary email (required) email_2 - Secondary email (optional, for CC) active - active/inactive (for filtering) language - EN/DE/etc. (for localization) AutomatizationHelper Fields: LANGUAGE - Language code CATEGORY - Contact type LATEST_SINGLE - Spotify/Apple Music link LATEST_VIDEO - YouTube/Vimeo link EPK - Electronic Press Kit URL SIGNATURE - HTML email signature PROMPT - AI prompt for this category SUBJECT - Email subject template Setup Instructions: Step 1: Prepare Your Data Download example files from the Google Drive folder Replace sample data with your real contacts and band information Customize AI prompts for your communication style Update signature with your contact details Step 2: Configure APIs Set up Gmail OAuth2 credentials in n8n Configure Google Sheets API access Add OpenAI API key for content generation Step 3: Import & Configure Workflow Import the workflow JSON Connect your credentials to respective nodes Update Google Sheets URL in AutomatizationHelper node Test with a small contact sample first Step 4: Customize & Run Adjust AI prompts in AutomatizationHelper for your style Update contact categories as needed Run workflow - drafts will be created in Gmail for review Tips: Start small**: Test with 5-10 contacts first Review drafts**: Always review AI-generated content before sending Update regularly**: Keep your AutomatizationHelper current with latest releases Monitor responses**: Track which prompts work best for different categories Language mixing**: You can have contacts in multiple languages Important Notes: Emails are created as Gmail drafts - manual review recommended Respects Gmail API rate limits automatically Conversation history analysis works best with existing email threads HTML signatures are automatically added (Gmail API limitation workaround) Handles multiple languages simultaneously Maintains conversation context across campaigns Generates unique content for each contact Template Author: Questions or need help with setup? Email: xciklv@gmail.com LinkedIn: https://www.linkedin.com/in/vaclavcikl/
by Bhuvanesh R
The competitive edge, delivered. This Customer Intelligence Engine simultaneously analyzes the web, Reddit, and X/Twitter to generate a professional, actionable executive briefing. π― Problem Statement Traditional market research for Customer Intelligence (CI) is manual, slow, and often relies on surface-level social media scraping or expensive external reports. Service companies, like HVAC providers, struggle to efficiently synthesize vast volumes of online feedback (Reddit discussions, real-time tweets, web articles) to accurately diagnose systemic service gaps (e.g., scheduling friction, poor automated systems). This inefficiency leads to delayed strategic responses and missed opportunities to invest in high-impact solutions like AI voice agents. β¨ Solution This workflow deploys a sophisticated Multisource Intelligence Pipeline that runs on a scheduled or ad-hoc basis. It uses parallel processing to ingest data from three distinct source types (SERP API, Reddit, and X/Twitter), employs a zero-cost Hybrid Categorization method to semantically identify operational bottlenecks, and uses the Anthropic LLM to synthesize the findings into a clear, executive-ready strategic brief. The data is logged for historical analysis while the brief is dispatched for immediate action. βοΈ How It Works (Multi-Step Execution) 1. Ingestion and Parallel Processing (The Data Fabric) Trigger:** The workflow is initiated either on an ad-hoc basis via an n8n Form Trigger or on a schedule (Time Trigger). Parallel Ingestion:** The workflow immediately splits into three parallel branches to fetch data simultaneously: SERP API: Captures authoritative content and industry commentary (Strategic Context). Reddit (Looping Structure): Fetches posts from multiple subreddits via an Aggregate Node workaround to get authentic user experiences (Qualitative Signal). X/Twitter (HTTP Request): Bypasses standard rate limits to capture real-time social complaints (Sentiment Signal). 2. Analysis and Fusion (The Intelligence Layer) Cleanup and Labeling (Function Nodes):** Each branch uses dedicated Function Nodes to filter noise (e.g., low-score posts) and normalize the data by adding a source tag (e.g., 'Reddit'). Merge:** A Merge Node (Append Mode) fuses all three parallel streams into a single, unified dataset. Hybrid Categorization (Function Node):** A single Function Node applies the Hybrid Categorization Logic. This cost-free step semantically assigns a pain_point category (e.g., 'Call Hold/Availability') and a sentiment_score to every item, transforming raw text into labeled metrics. 3. Dispatch and Reporting (The Executive Output) Aggregation and Split (Function Node):** The final Function Node calculates the total counts, deduplicates the final results, and generates the comprehensive summaryString. Data Logging:* The aggregated counts and metrics are appended to *Google Sheets** for historical logging. LLM Input Retrieval (Function Node):** A final Function Node retrieves the summary data using the $items() helper (the serial route workaround). AI Briefing:* The *Message a model (Anthropic) Node receives the summaryString and uses a strict HTML System Prompt to synthesize the strategic brief, identifying the top pain points and suggesting AI features. Delivery:* The *Gmail Node** sends the final, professional HTML brief to the executive team. π οΈ Setup Steps Credentials Anthropic:** Configure credentials for the Language Model (Claude) used in the Message a model node. SERP API, Reddit, and X/Twitter:** Configure API keys/credentials for the data ingestion nodes. Google Services:** Set up OAuth2 credentials for Google Sheets (for logging data) and Gmail (for email dispatch). Configuration Form Configuration:** If using the Form Trigger, ensure the Target Keywords and Target Subreddits are mapped correctly to the ingestion nodes. Data Integrity:** Due to the serial route, ensure the Function (Get LLM Summary) node is correctly retrieving the LLM_SUMMARY_HOLDER field from the preceding node's output memory. β Benefits Proactive CI & Strategy:** Shifts market research from manual, reactive browsing to proactive, scheduled data diagnostic. Cost Efficiency:** Utilizes a zero-cost Hybrid Categorization method (Function Node) for intent analysis, avoiding expensive per-item LLM token costs. Actionable Output:** Delivers a fully synthesized, HTML-formatted executive brief, ready for immediate presentation and strategic sales positioning. High Reliability:** Employs parallel ingestion, API workarounds, and serial routing to ensure the complex workflow runs consistently and without failure.
by Hyrum Hurst
AI Agent Lead Funnel for AI Agencies An End-to-End Automation That Turns Demos Into Booked Calls This n8n workflow is a full inbound β outbound hybrid funnel designed for AI agencies. It captures warm leads through instant AI value, then automatically follows up with personalized, context-aware outreach and reminders until the lead either replies or books a call. No cold scraping. No manual follow-ups. Just leverage + timing. π How the Workflow Works π PART 1 β Lead Capture & Instant Value 1 β Share High-Impact AI Image Edits You post before/after examples using the NanoBanna / Gemini image-editing model on social platforms. Each post includes a link to a lightweight form. The visual results do the selling for you. 2 β Lead Submits Image & Details The form collects: Image upload Edit instructions Name Email Company name This filters for high-intent prospects only. 3 β AI Edits the Image Instantly Once submitted, the workflow: Sends the image + instructions to the AI image editor Preserves lighting and camera angle unless specified Generates a polished result in seconds 4 β Result Delivered via Email The edited image is emailed directly to the user with: A friendly confirmation message Soft positioning for future work This establishes trust before any sales motion happens. 5 β Lead Is Logged Automatically All lead data is saved to Google Sheets: Name Company Email Timestamp This becomes your live CRM of warm inbound leads. π€ PART 2 β AI-Driven Personalized Outreach 6 β AI Analyzes the Lead An AI sales agent: Looks at the company name + context Reviews a library of proven automation ideas Either selects the best fit or creates a simple custom one 7 β AI Writes a Personalized Outreach Email The agent generates a short email that: Mentions a specific automation already built States you can help implement it quickly Invites them to book a call via your calendar No marketing fluff. No generic pitches. Every email feels hand-written. 8 β Outreach Email Is Sent Automatically The email is sent from your inbox (Outlook, Gmail, SMTP, etc.) and includes: Their name Their company A clear calendar booking link π¬ PART 3 β Smart Follow-Up System 9 β Wait 48 Hours The workflow pauses to give the lead time to respond naturally. 10 β Check for a Reply After 48 hours: If the lead replied β they are tagged as Interested If no reply β continue to follow-up (Current reply detection is placeholder logic and can be swapped for a live inbox listener.) 11 β AI Writes a Polite Follow-Up If thereβs no response, an AI agent writes: A short, non-pushy follow-up Referencing the original automation idea Under 60 words 12 β Follow-Up Email Is Sent The follow-up goes out automatically and keeps the conversation alive without manual effort. π Why This Workflow Converts So Well Instant Value First Leads experience AI results before being pitched anything. Context-Aware Outreach Every email is personalized based on the lead, not a template. Built-In Persistence The system follows up automatically β no leads fall through the cracks. Fully Automated Once live, this workflow handles: Lead capture AI delivery Outreach Follow-ups CRM updates You just keep posting content. π§ Setup Requirements To deploy this workflow, connect: Google Gemini API** (image editing + agents) Email provider** Outlook Gmail SMTP Google Sheets** Columns: Name, Company, Email, Time, Status Calendar booking link** Example: https://cal.com/your-link All credentials are modular and easily swappable. π― Summary This n8n automation turns attention into action by: Delivering immediate AI value Following up with relevant, personalized ideas Nudging leads toward a booked call β automatically Itβs not just a lead funnel. Itβs an AI sales assistant that runs 24/7.
by May Ramati Kroitero
Automated Job Hunt with Tavily β Setup & Run Guide What this template does? Automatically searches for recent job postings (example: βSoftware Engineering Internβ), 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) β 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 β 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) β 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 β set the agentβs 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 β 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 βExecute Workflowβ or manually trigger nodes). Inspect the Search in Tavily node output β confirm it returns results. Inspect the AI Agent and Message a model outputs β ensure formatted postings are produced and separated by --- END JOB POSTING ---. Run the Code node β 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 β confirm the email receives one combined message with all postings. 4. Troubleshooting tips Gmail body shows [Array: β¦]: Avoid dragging the array raw β 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 nodeβs 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.
by gotoHuman
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.
by Franz
π AI Lead Generation and Follow-Up Template π 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: π€ AI-powered lead classification (Demo-ready, Nurture, Drop) π Automatic lead enrichment with company data π§ Intelligent email responses and follow-up sequences π Automated demo scheduling for qualified leads π Complete lead logging in Google Sheets π¬ AI assistant for immediate query responses π οΈ 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) β‘ 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 π― Lead Classification Setup Define Your ICP (Ideal Customer Profile) Edit the "Define ICP and Lead Criteria" node to set your criteria: π 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+ β 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 π± Nurture Criteria: Prospects with future potential: Meet basic size requirements In target industry General interest expressed Planning future implementation Exploring options β 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 π§ 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 π§ 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 π 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 π 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 π 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
by Dean Pike
CV β Match β Screen β 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.
by Marth
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. βοΈ 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. π§ Setup Steps 1οΈβ£ Prepare Your Google Sheet Create a sheet called βEmployee_Recognition_Listβ with these columns: Name | Department | Reason | Date | Email | Status | EmailStatus Then add one test row β for example, your own name β to see it work. 2οΈβ£ 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. 3οΈβ£ (Optional) Add AI Personalization If you want the messages to sound more natural, add an OpenAI node with this prompt: > βWrite a short, friendly recognition message for {{name}} from {{dept}} who was recognized for {{reason}}. Keep it under 2 sentences.β This makes your Slack and email messages feel human and genuine. 4οΈβ£ Turn It On Once everythingβs 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 π
by Roshan Ramani
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 β Real-time personal report generation β Intelligent AI-powered analysis (Google Gemini) β Professional HTML email formatting β Automatic weekly summaries β Statistical analysis and trend identification β Zero manual processing required β Fully customizable prompts and styling β 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** β Auto-extracted demographics (age, role, location, email) Key Insights** β 3-4 AI-generated insights specific to their responses Personalized Recommendations** β 3-4 actionable suggestions based on their answers Professional Formatting** β HTML-styled email with your branding colors Mobile Responsive** β 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** β How many submissions in the period Demographic Breakdown** β Distribution of respondent profiles Response Statistics** β Percentages and frequencies for each question Answer Distribution** β Most popular choices across all responses Trend Analysis** β Patterns and correlations in the data Key Findings** β Top 5-7 insights from all responses combined Statistical Metrics** β 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 β Collect Response Data β AI Analysis & Insights Generation β Create Styled HTML Report β Send to Respondent Email AGGREGATE REPORTS: Weekly Schedule Triggers β Fetch All Submissions β Statistical Analysis & Trend Detection β Generate Insights from All Data β Create Summary HTML Report β Send to Admin Email Use Cases Customer Feedback Surveys** β Analyze responses, send personalized insights Product Research** β Track trends across respondents weekly Market Research** β Automated statistical reporting Employee Surveys** β Personalized feedback + company trends Event Feedback** β Instant attendee insights + organizer summary Customer Satisfaction (NPS)** β Personalized follow-ups + trend analysis Lead Qualification** β Auto-analyze prospect responses and route accordingly
by NODA shuichi
Description: More than an alarm. A smart morning experience that adapts to the weather. πΈβοΈβοΈ 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
by Rakin Jakaria
How it works: This project creates a personal AI knowledge assistant that operates through Telegram. The assistant can extract summaries from YouTube videos or online articles, store them in Google Sheets for later reference, and retrieve stored summaries when requested by the user. Step-by-step: Google Sheets Trigger:* The workflow starts by detecting a new YouTube or article URL added to a dedicated sheet (Sheet2*). It checks whether the link is already processed. Link Type Detection:** The system identifies if the URL is from YouTube or a standard article. Data Retrieval:** If itβs YouTube: Uses Apify to fetch the transcript. If itβs an article: Uses an HTTP Request node to fetch the webpage content. AI Summarization:* The transcript or article content is passed to *Google Gemini** for refined summarization. Google Sheets Storage:* The summary and title are appended to another sheet (Sheet1*) for long-term storage, along with a βStoredβ status update in Sheet2. Telegram Assistant:** A Telegram Trigger listens for messages from the user. The assistant searches stored summaries in Google Sheets. If a match is found, it returns the result to the user on Telegram; otherwise, it politely apologizes.
by vinci-king-01
How it works This workflow automatically extracts data from invoice documents (PDFs and images) and processes them through a comprehensive validation and approval system. Key Steps Multi-Input Triggers - Accepts invoices via email attachments or direct file uploads through webhook. AI-Powered Extraction - Uses ScrapeGraphAI to extract structured data from invoice documents. Data Cleaning & Validation - Processes and validates extracted data against business rules. Approval Workflow - Routes invoices requiring approval through a multi-stage approval process. System Integration - Automatically sends validated invoices to your accounting system. Set up steps Setup time: 10-15 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key for invoice data extraction. Set up Telegram connection - Connect your Telegram account for approval notifications. Configure email trigger - Set up IMAP connection for processing emailed invoices. Customize validation rules - Adjust business rules, amount thresholds, and vendor lists. Set up accounting system integration - Configure the HTTP request node with your accounting system's API endpoint. Test the workflow - Upload a sample invoice to verify the extraction and approval process. Features Multi-format support**: PDF, PNG, JPG, JPEG, TIFF, BMP Intelligent validation**: Business rules, duplicate detection, amount thresholds Approval automation**: Multi-stage approval workflow with role-based routing Data quality scoring**: Confidence levels and completeness analysis Audit trail**: Complete processing history and metadata tracking