Workflow Templates

📘 Description: This workflow automates a complete CRM → Sheets → AI → Email reporting pipeline for HighLevel opportunities. It fetches fresh opportunity data from HighLevel, validates and normalizes every record, syncs all structured opportunities into a Google Sheet, merges them into a single dataset, and then uses GPT-4o to generate a clean, Gmail-friendly HTML report summarizing all opportunities for the day. Finally, it emails the formatted report directly to the sales inbox—creating a fully automated, zero-touch Daily Opportunity Insight System. Invalid or incomplete CRM entries are logged automatically, ensuring data hygiene and auditability. ⚙️ What This Workflow Does (Step-by-Step) ▶️ When Clicking ‘Execute Workflow’ (Manual Trigger) Starts the daily reporting pipeline manually or on schedule. 📥 Fetch Opportunities from HighLevel CRM Retrieves the latest opportunities (limit = 5) from HighLevel along with company, contact, source, and pipeline metadata. Acts as the primary CRM input. 🔍 Validate Opportunity Data Payload (IF Node) Checks whether each record contains a valid id. ✅ Valid → proceed to extraction and normalization ❌ Invalid → sent to Google Sheets for cleanup ⚠️ Log Invalid Opportunities to Google Sheets Saves corrupt or incomplete CRM payloads into an error sheet. Supports CRM maintenance and future corrective actions. 🧾 Extract Key Fields from HighLevel Data (Code Node) Pulls only essential fields from each opportunity: id, name, company, email, phone, source, assignedTo, pipelineId, stageId, tags, monetaryValue, and timestamps. Produces a simplified, uniform data structure. 🛠 Normalize Opportunity Structure (Code Node) Cleans and standardizes each opportunity’s schema: ensures consistent field naming, fills contact info when nested, resolves pipeline/stage fields, and finalizes structure for sheet update. 📊 Update Opportunity Records in Google Sheets Upserts (append/update) each opportunity into the ghl database tab of sample_leads_50. Matching key: id Keeps HighLevel CRM and Google Sheets fully synced. 🧩 Merge All Opportunities into a Single JSON Array Combines every normalized opportunity into one array named opportunities. This consolidated payload is passed to GPT-4o for table generation. 🧠 Configure GPT-4o Model (Azure OpenAI) Initializes GPT-4o as the AI engine responsible for generating the final HTML summary. 📄 Generate Daily Opportunity Summary Report (AI Agent) GPT-4o transforms the merged opportunity dataset into a structured HTML report: Daily Opportunity Summary A short descriptive paragraph A full-width Gmail-friendly table with padded cells Header background #f5f5f5 Columns in fixed order: Name, Company, Email, Phone, Source, Pipeline ID, Stage ID, Value, Created At All nulls replaced with “–” Output is pure HTML—no markdown. 📧 Send Daily Opportunity Summary via Gmail Emails the final HTML report to the internal sales inbox with subject: “Daily Opportunity Report – Summary of New Leads” Optimized for Gmail + Outlook rendering. 🧩 Prerequisites HighLevel OAuth connection Azure OpenAI GPT-4o credentials Google Sheets OAuth (Techdome account) Gmail API connection for report delivery 💡 Key Benefits ✅ Automatic syncing of HighLevel CRM opportunities into Sheets ✅ AI-generated HTML dashboards without manual formatting ✅ Clean, readable daily insights for sales teams ✅ Built-in error logging for bad CRM records ✅ Zero manual intervention required after setup 👥 Perfect For Sales & Growth Teams using HighLevel CRM Operations teams maintaining CRM hygiene Agencies needing daily pipeline visibility Organizations wanting automated AI-generated opportunity summaries

Champion Migration Tracker Automatically detect when your champion contacts change companies and respond with intelligent, personalized AI outreach before your competitors do. THE PROBLEM When champions move to new companies, sales teams lose track and miss high-value opportunities. Manual LinkedIn monitoring doesn't scale, and by the time you notice, the relationship has gone cold. THE SOLUTION This workflow automates champion migration tracking end-to-end, combining Explorium's data intelligence with Claude AI agents to maintain relationships and prioritize opportunities. HOW IT WORKS 1. Automated Job Change Detection Uses Explorium person enrichment to detect when champions move companies Eliminates manual LinkedIn monitoring Triggers immediately when employment changes 2. Intelligent Company Enrichment Enriches new companies with Explorium data: firmographics, funding, tech stack, hiring velocity Checks if company already exists in your CRM (Customer vs Prospect) Identifies open opportunities and account owners 3. Multi-Dimensional Opportunity Scoring (0-100) ICP Fit (40%)**: Company size, funding stage, revenue, tech stack alignment Relationship Strength (40%)**: Past deals influenced, relationship warmth, CRM status Timing (20%)**: Days at new company, recent funding/acquisition signals Results in Hot/Warm/Cold priority classification 4. Smart Routing by Context Customers**: Notify account manager with congratulations message Hot Prospects (75+ score)**: Draft detailed strategic outreach for rep review 5. AI-Powered Personalization Claude AI agents generate contextually relevant emails References past relationship, deals influenced, and company intelligence Adapts tone and content based on opportunity priority and CRM status DEMO SETUP (Google Sheets) This demo uses Google Sheets for simplicity. For production use, replace with your actual CRM: Salesforce HubSpot Pipedrive Any CRM with n8n integration Important Fields to Consider: Champions: champion_id, name, email, company, title, last_checked_date relationship_strength (Hot/Warm/Cold), last_contact_date, deals_influenced relationship_notes, isChampion (TRUE/FALSE), linkedin_url, explorium_prospect_id Companies: company_ID, companyName, domain, relationship_type (Customer/Prospect/None) open_opportunity (TRUE/FALSE), opportunity_stage, account_owner, account_owner_email contractValue, notes, ExploriumBusinessID REQUIRED CREDENTIALS Anthropic API Key - Powers Claude AI agents for email generation Explorium API Key - Provides person and company enrichment data Google Sheets or Your CRM (production) - Data source and logging SETUP INSTRUCTIONS Connect Credentials in n8n Settings → Credentials Update Data Sources: Replace Google Sheets nodes with your CRM nodes (or create demo sheets with structure above) Configure Scoring: Adjust ICP scoring criteria in "Score Company" node to match your ideal customer profile Test with Sample Data: Run with 2-3 test champions to verify routing and email generation Schedule Trigger: Set to run daily or weekly based on your needs CUSTOMIZATION TIPS Scoring Weights: Adjust the 40/40/20 weighting in the scoring node to prioritize what matters most to your business Tech Stack Matching: Update the relevantTech array with tools your champions likely use Email Tone: Modify Claude prompts to match your brand voice (formal, casual, technical, etc.) Routing Logic: Add additional branches for specific scenarios (e.g., churned customers, enterprise accounts) **Agentic Experience: Consider adding an agent that sends the email for Cold prospects automatically. Integrations: Add Slack notifications, CRM updates, or calendar booking links to the output BUSINESS VALUE Prevent Revenue Leakage**: Never lose track of champion relationships Prioritize Intelligently**: Focus on opportunities with highest potential Scale Relationship Building**: Automate what used to require manual effort Act Before Competitors**: Reach out while champions are still settling into new roles Data-Driven Decisions**: Quantifiable scores replace gut feelings USE CASES Sales Teams**: Re-engage champions at new prospect companies Customer Success**: Track champions who move to existing accounts Account-Based Marketing**: Identify high-fit accounts through champion networks Revenue Operations**: Automate champion tracking at scale NOTES Production Recommendation**: Replace Google Sheets with your production CRM for real-time data Privacy**: All API keys are credential-referenced (not hardcoded) for security Explorium Credits**: Person + company enrichment uses ~2-3 credits per champion

Automated Employee Performance Review Summary (AI-Powered) Description Simplify employee performance reviews with AI-powered automation. This workflow transforms raw feedback and evaluation inputs into clear, structured, and professional performance review summaries — saving hours of manual writing while ensuring consistency and fairness. What This Workflow Does Turns scattered performance inputs into a single, AI-generated review summary: 📝 Capture Review Inputs – Collects employee details, role information, and performance feedback. 🧠 AI Review Analysis – AI analyzes strengths, areas of improvement, and overall performance trends. 📄 Generate Review Summary – Automatically creates a concise, professional performance review. 📊 Structured Output – Formats feedback into clear sections for easy understanding. 💾 Store or Share – Saves the summary or prepares it for HR systems, emails, or records. Key Features 🤖 AI-Powered Review Writing – Eliminates manual drafting of performance summaries. ⚙️ Consistent Evaluation – Ensures structured and unbiased review language. 📄 Professional Formatting – Ready-to-use summaries for HR documentation. ⏱️ Time-Saving Automation – Reduces review preparation from hours to minutes. 🔁 Reusable Template – Works for multiple employees and review cycles. Perfect For 🏢 HR Teams – Automate performance review summaries at scale. 📈 Managers & Team Leads – Quickly generate clear and actionable reviews. 🏭 Growing Organizations – Maintain consistency across evaluations. 💼 Enterprises – Standardize review documentation effortlessly. What You’ll Need Required Integrations OpenAI** – Generates clear and professional performance review summaries using AI Gmail** – Sends the performance review summary to managers or employees Google Sheets** – Stores performance inputs and AI-generated summaries for record-keeping Slack** – Notifies managers or HR teams when a review is generated HTMLCSS to Image** – Converts the performance review summary into a shareable visual format Optional Enhancements Google Drive** – Save generated review images for long-term storage PDF Conversion** – Convert review visuals into downloadable PDFs Approval Steps** – Add manager approval before sending results Quick Start 1️⃣ Import the workflow into your n8n workspace 2️⃣ Connect your OpenAI credentials 3️⃣ Configure employee and feedback inputs 4️⃣ Test with sample review data 5️⃣ Activate and start generating reviews automatically Expected Results ⚡ Faster Reviews – Generate summaries in minutes 🤖 High-Quality Feedback – Clear, balanced, and professional wording 📄 Standardized Output – Consistent structure across all reviews 🗂️ Easy Documentation – Ready for storage or sharing Workflow Structure 📝 Review Input ↓ 🧠 AI Performance Analysis ↓ 📄 Review Summary Generation ↓ 💾 Save / Share Output Upgrade your performance review process with AI. Import this template and let automation handle the writing — so managers can focus on people, not paperwork ✨

📘 Description This workflow automates the full lifecycle of a data-consent complaint: receiving the complaint, validating the payload, normalizing the data into a clean ticket format, storing it in a compliance sheet, generating a formal acknowledgement email for the user, and sending an internal Slack alert for the compliance team. Everything moves from intake → classification → communication → escalation with zero manual handling. AI-generated messages follow DPDP-compliant tone and structure. ⚙️ What This Workflow Does (Step-by-Step) ▶️ Receive Consent Complaint (Webhook) Accepts a POST request containing complaint details: name, email, issue type, description, and metadata. 🔍 Check Required Fields (IF) Validates that the complaint includes a non-empty description. Valid → processed Invalid → logged to a separate sheet. 🧹 Clean & Normalize Complaint Data (Code) Constructs a unified ticket object with: auto-generated ticketId normalized fields (action, email, issueType, description) priority scoring timestamp source metadata Sets default status to Open. 📄 Log Invalid Complaint Records (Google Sheets) Stores incomplete submissions for later review. 📥 Store Complaint Ticket in Consent Dispute Sheet (Google Sheets) Appends the cleaned ticket into the Consent Dispute sheet including all metadata for traceability. 🧠 Generate Acknowledgement Email (AI) Creates a DPDP-compliant support email: mentions user name references ticket ID summarizes issue sets response window (48–72 hours) avoids legal or internal disclosures uses formal, respectful tone ⚙️ Configure GPT-4o – Email Generator Supplies the AI model for email generation. 📝 Extract Email Subject + Body (Code) Splits the AI-generated email into structured fields: subject and message. 📧 Send Acknowledgement Email to User (Gmail) Delivers the formatted acknowledgement directly to the user who filed the complaint. ⚙️ Configure GPT-4o – Slack Summary Model Supplies the AI model for internal Slack summary generation. 🔔 Generate Slack Incident Summary (AI) Produces an internal, action-focused Slack message containing: ticket ID user details issue type description timestamp priority recommended next step No greetings, no email formatting. 📡 Slack – Notify Compliance Team Sends the incident summary to the assigned Slack user or channel for quick action by compliance. 🧩 Prerequisites Live webhook endpoint Google Sheets OAuth (Techdome) Gmail OAuth Slack API credentials Azure OpenAI GPT-4o enabled 💡 Key Benefits ✅ Zero-touch intake → acknowledgement → escalation ✅ DPDP-compliant communication with users ✅ Structured ticket normalization and prioritization ✅ Instant Slack alerts for compliance action ✅ Full audit trail in Google Sheets 👥 Perfect For Data privacy teams Compliance & grievance redressal units SaaS platforms handling consent disputes Organizations needing DPDP-aligned automated workflows

How It Works Trigger: Watches for new emails in Gmail with PDF/image attachments. OCR: Sends the attachment to OCR.space API (https://ocr.space/OCRAPI) to extract invoice text. Parsing: Extracts key fields: Vendor Invoice number Amount Currency Invoice date Due date Description Validation Logic: Checks if amount is valid Ensures vendor and invoice number are present Flags high-value invoices (e.g., over $10,000) Routing: If invalid: Sends a Slack message highlighting issues Labels email as Rejected If valid: Logs the invoice into Google Sheets Sends a Slack message to the finance team for approval After approval, creates a draft invoice in Xero Labels the email as Processed in Gmail Set up steps • Estimated setup time: 45-60 mins • You’ll need connected credentials for Gmail, Slack, Google Sheets, and Xero • Replace the default API key for OCR.space with your own (in the HTTP Request node) • Update Slack channel IDs and label IDs to match your workspace • Adjust invoice validation rules as needed (e.g. currency, red flag conditions) All detailed explanations and field mappings are provided in sticky notes within the workflow.

Automated BBB Lead Generation with BrowserAct 🚀 Overview This workflow automates business data extraction, duplicate checking, and email outreach using BrowserAct, Google Sheets, Gmail, and Google Gemini AI — all inside n8n. It’s designed for marketers, lead generation specialists, or automation developers who want to build a fully autonomous AI agent that finds businesses online, filters duplicates, and automatically sends personalized outreach emails. 🧩 Key Features 🌐 BrowserAct Integration — Scrapes business data (name, phone, email, website, rating) from any target site. 🤖 AI Data Extraction Agent — Uses Google Gemini AI to clean, structure, and validate scraped data into standardized JSON. 📊 Google Sheets Sync — Reads all existing records Checks for duplicates Appends new rows automatically ✉️ Automated Gmail Outreach — Validates email addresses Sends outreach emails to valid leads Logs each status (e.g., Successful, Duplicate, Pending - Invalid Email) ⏳ Smart Delay Control — Uses Wait node to pause execution and respect email sending limits (max 2 emails per run). 🛠️ Included Nodes | Node | Function | | -------------------------- | ------------------------------------------------- | | 🕓 Schedule Trigger | Runs the workflow automatically on schedule | | 🌍 BrowserAct | Scrapes or extracts business data | | ⚙️ If Node | Checks scraping results before processing | | 🧠 AI Agent (Gemini) | Extracts structured business info | | 💻 Code (JavaScript) | Cleans and parses AI output into usable JSON | | 📩 AI Agent 2 (Gemini) | Handles decision-making for email + sheet updates | | 📊 Google Sheets Tools | Reads, appends, and manages lead data | | 📨 Gmail Node | Sends automated outreach emails | | ⏱️ Wait Node | Adds delay to control workflow speed | 🧾 How It Works Schedule Trigger starts the automation. BrowserAct fetches business listings based on defined keywords and location. AI Agent (Gemini) extracts business details (business_name, website_url, phone_number, email_address, rating). JavaScript Code Node parses the AI’s JSON response. AI Agent 2 (Gemini) decides: If duplicate → send message on your email address Duplicate data found If invalid email → marks as “Pending - Invalid Email” If valid email → sends via Gmail + updates Google Sheet Final output returns structured statuses for each processed business. 🖼️ Workflow Diagram > * Schedule Trigger > * BrowserAct > * AI Agent (Gemini) > * JavaScript Code > * Gmail & Google Sheets tools ![Workflow Preview] ⚙️ Setup Instructions Connect your BrowserAct, Google Sheets, Gmail, and Google Gemini API credentials. Define search keywords and locations inside the BrowserAct node. Set your Google Sheet ID in the relevant nodes. Customize the Gmail message if needed. Activate the workflow and schedule it. 📤 Output Example [ { "business_name": "ABC Restaurant", "email_sent": "Successful" }, { "business_name": "XYZ Foods", "email_sent": "Duplicate - Already Exist" }, { "business_name": "Fresh Eats", "email_sent": "Pending - Invalid Email" } ] 👨‍💻 Created by Bakir Ali Automation & AI Workflow Creator — specialized in BrowserAct, Google AI (Gemini), and n8n-based automation systems.

Revolutionize university admissions with intelligent AI-driven application evaluation that analyzes student profiles, calculates eligibility scores, and automatically routes decisions - saving 2.5 hours per application and reducing decision time from weeks to hours. 🎯 What This Workflow Does Transforms your admissions process from manual application review to intelligent automation: 📝 Captures Applications - Jotform intake with student info, GPA, test scores, essay, extracurriculars 🤖 AI Holistic Evaluation - OpenAI analyzes academic strength, essay quality, extracurriculars, and fit 🎯 Intelligent Scoring - Evaluates students using 40% academics, 25% extracurriculars, 20% essay, 15% fit (0-100 scale) 🚦 Smart Routing - Automatically routes based on AI evaluation: Auto-Accept (95-100)**: Acceptance letter with scholarship details → Admin alert → Database Interview Required (70-94)**: Interview invitation with scheduling link → Admin alert → Database Reject (<70)**: Respectful rejection with improvement suggestions → Database 💰 Scholarship Automation - Calculates merit scholarships ($5k-$20k+) based on eligibility score 📊 Analytics Tracking - All applications logged to Google Sheets for admissions insights ✨ Key Features AI Holistic Evaluation: Comprehensive analysis weighing academics, extracurriculars, essays, and institutional fit Intelligent Scoring System: 0-100 eligibility score with automated categorization and scholarship determination Structured Output: Consistent JSON schema with academic strength, admission likelihood, and decision reasoning Automated Communication: Personalized acceptance, interview, and rejection letters for every applicant Fallback Scoring: Manual GPA/SAT scoring if AI fails - ensures zero downtime Admin Alerts: Instant email notifications for exceptional high-scoring applicants (95+) Comprehensive Analytics: Track acceptance rates, average scores, scholarship distribution, and applicant demographics Customizable Criteria: Easy prompt editing to match your institution's values and requirements 💼 Perfect For Universities & Colleges: Processing 500+ undergraduate applications per semester Graduate Programs: Screening master's and PhD applications with consistent evaluation Private Institutions: Scaling admissions without expanding admissions staff Community Colleges: Handling high-volume transfer and new student applications International Offices: Evaluating global applicants 24/7 across all timezones Scholarship Committees: Identifying merit scholarship candidates automatically 🔧 What You'll Need Required Integrations Jotform - Application form with student data collection (free tier works) Create your form for free on Jotform using this link Create your application form with fields: Name, Email, Phone, GPA, SAT Score, Major, Essay, Extracurriculars OpenAI API - GPT-4o-mini for cost-effective AI evaluation (~$0.01-0.05 per application) Gmail - Automated applicant communication (acceptance, interview, rejection letters) Google Sheets - Application database and admissions analytics Optional Integrations Slack - Real-time alerts for exceptional applicants Calendar APIs - Automated interview scheduling Student Information System (SIS) - Push accepted students to enrollment system Document Analysis Tools - OCR for transcript verification 🚀 Quick Start Import Template - Copy JSON and import into n8n (requires LangChain support) Create Jotform - Use provided field structure (Name, Email, GPA, SAT, Major, Essay, etc.) Add API Keys - OpenAI, Jotform, Gmail OAuth2, Google Sheets Customize AI Prompt - Edit admissions criteria with your university's specific requirements and values Set Score Thresholds - Adjust auto-accept (95+), interview (70-94), reject (<70) cutoffs if needed Personalize Emails - Update templates with your university branding, dates, and contact info Create Google Sheet - Set up columns: id, Name, Email, GPA, SAT Score, Major, Essay, Extracurriculars Test & Deploy - Submit test application with pinned data and verify all nodes execute correctly 🎨 Customization Options Adjust Evaluation Weights: Change academics (40%), extracurriculars (25%), essay (20%), fit (15%) percentages Multiple Programs: Clone workflow for different majors with unique evaluation criteria Add Document Analysis: Integrate OCR for transcript and recommendation letter verification Interview Scheduling: Connect Google Calendar or Calendly for automated booking SIS Integration: Push accepted students directly to Banner, Ellucian, or PeopleSoft Waitlist Management: Add conditional routing for borderline scores (65-69) Diversity Tracking: Include demographic fields and bias detection in AI evaluation Financial Aid Integration: Automatically calculate need-based aid eligibility alongside merit scholarships 📈 Expected Results 90% reduction in manual application review time (from 2.5 hours to 15 minutes per application) 24-48 hour decision turnaround time vs 4-6 weeks traditional process 40% higher yield rate - faster responses increase enrollment commitment 100% consistency - every applicant evaluated with identical criteria Zero missed applications - automated tracking ensures no application falls through cracks Data-driven admissions - comprehensive analytics on applicant pools and acceptance patterns Better applicant experience - professional, timely communication regardless of decision Defensible decisions - documented scoring criteria for accreditation and compliance 🏆 Use Cases Large Public Universities Screen 5,000+ applications per semester, identify top 20% for auto-admit, route borderline to committee review. Selective Private Colleges Evaluate 500+ highly competitive applications, calculate merit scholarships automatically, schedule interviews with top candidates. Graduate Programs Process master's and PhD applications with research experience weighting, flag candidates for faculty review, automate fellowship awards. Community Colleges Handle high-volume open enrollment while identifying honors program candidates and scholarship recipients instantly. International Admissions Evaluate global applicants 24/7, account for different GPA scales and testing systems, respond same-day regardless of timezone. Rolling Admissions Provide instant decisions for early applicants, fill classes strategically, optimize scholarship budget allocation. 💡 Pro Tips Calibrate Your AI: After 100+ applications, refine evaluation criteria based on enrolled student success A/B Test Thresholds: Experiment with score cutoffs (e.g., 93 vs 95 for auto-admit) to optimize yield Build Waitlist Pipeline: Keep 70-84 score candidates engaged for spring enrollment or next year Track Source Effectiveness: Add UTM parameters to measure which recruiting channels deliver best students Committee Review: Route 85-94 scores to human admissions committee for final review Bias Audits: Quarterly review of AI decisions by demographic groups to ensure fairness Parent Communication: Add parent/guardian emails for admitted students under 18 Financial Aid Coordination: Sync scholarship awards with financial aid office for packaging 🎓 Learning Resources This workflow demonstrates: AI Agents with structured output** - LangChain integration for consistent JSON responses Multi-stage conditional routing** - IF nodes for three-tier decision logic Holistic evaluation** - Weighted scoring across multiple dimensions Automated communication** - HTML email templates with dynamic content Real-time notifications** - Admin alerts for high-value applicants Analytics and data logging** - Google Sheets integration for reporting Fallback mechanisms** - Manual scoring when AI unavailable Perfect for learning advanced n8n automation patterns in educational technology! 🔐 Compliance & Ethics FERPA Compliance: Protects student data with secure credential handling Fair Admissions: Documented criteria eliminate unconscious bias Human Oversight: Committee review option for borderline cases Transparency: Applicants can request evaluation criteria Appeals Process: Structured workflow for decision reconsideration Data Retention: Configurable Google Sheets retention policies 📊 What Gets Tracked Application submission date and time Complete student profile (GPA, test scores, major, essay, activities) AI eligibility score (0-100) and decision category Academic strength rating (excellent/strong/average) Scholarship eligibility and amount ($0-$20,000+) Admission likelihood (high/medium/low) Decision outcome (accepted/interview/rejected) Email delivery status and open rates Time from application to decision Ready to transform your admissions process? Import this template and start evaluating applications intelligently in under 1 hour. Questions or customization needs? The workflow includes detailed sticky notes explaining each section and comprehensive fallback logic for reliability.

Turn a simple email workflow into a LinkedIn content machine. Generate post ideas, draft full posts, and auto-publish to LinkedIn all controlled by replying to emails. 📌 Purpose Automate your LinkedIn posting pipeline using AI + Email approvals. Generate 10 scroll-stopping post ideas tailored to your niche & audience. Approve your favorite by replying to the email with a number. Receive 3 AI-written drafts for the chosen idea. Pick your favorite draft via email reply. The selected post gets auto-published to LinkedIn ✅. All steps are logged in Google Sheets. 🔗 Apps Used Google Gemini** → generates ideas & drafts Gmail** → email-based approval workflow Google Sheets** → tracks ideas, drafts, and published posts LinkedIn API** → posts directly to your company or personal account ✨ Highlights 📬 Email-based approval → no dashboards, just reply with a number 📝 10 AI-generated content ideas + 3 full drafts per topic 🔄 End-to-end tracking in Google Sheets (ideas → drafts → published) ⚡ Auto-posting directly to LinkedIn ✅ Final confirmation email with preview 👤 Best For Startup founders Agencies managing multiple clients’ LinkedIn Solopreneurs & creators who want consistent posting 🛠️ Workflow Overview flowchart TB A["Manual Trigger"] --> B["AI Agent - Generate 10 Ideas"] B --> C["Code - Parse JSON + Correlation ID"] C --> D["Google Sheets - Append Ideas"] D --> E["Gmail - Send Ideas Email"] E --> F["Gmail Trigger - Await Reply"] F --> G["Code1 - Extract Reply Number"] G --> H["Google Sheets - Fetch Row"] H --> I{"Switch Stage"} I -- Ideas --> J["AI Agent - Generate 3 Drafts"] J --> K["Code3 - Parse Drafts"] K --> L["Google Sheets - Update Drafts"] L --> M["Gmail - Send Drafts Email"] I -- Drafts --> N["Code4 - Select Final Draft"] N --> O["LinkedIn - Publish Post"] O --> P["Google Sheets - Update Posted"] P --> Q["Gmail - Send Confirmation"] `

Fetch user-specific research papers from arXiv on a daily schedule, process and structure the data, and create or update entries in a Notion database, with support for data delivery Paper Topic**: single query keyword Update Frequency**: Daily updates, with fewer than 20 entries expected per day Tools**: Platform: n8n, for end-to-end workflow configuration AI Model: Gemini-2.5-Flash, for daily paper summarization and data processing Database: Notion, with two tables — Daily Paper Summary and Paper Details Message: Feishu (IM bot notifications), Gmail (email notifications) 1. Data Retrieval arXiv API The arXiv provides a public API that allows users to query research papers by topic or by predefined categories. arXiv API User Manual Key Notes: Response Format: The API returns data as a typical Atom Response. Timezone & Update Frequency: The arXiv submission process operates on a 24-hour cycle. Newly submitted articles become available in the API only at midnight after they have been processed. Feeds are updated daily at midnight Eastern Standard Time (EST). Therefore, a single request per day is sufficient. Request Limits: The maximum number of results per call (max_results) is 30,000, Results must be retrieved in slices of at most 2,000 at a time, using the max_results and start query parameters. Time Format: The expected format is [YYYYMMDDTTTT+TO+YYYYMMDDTTTT], TTTT is provided in 24-hour time to the minute, in GMT. Scheduled Task Execution Frequency**: Daily Execution Time**: 6:00 AM Time Parameter Handling (JS)**: According to arXiv’s update rules, the scheduled task should query the previous day’s (T-1) submittedDate data. 2. Data Extraction Data Cleaning Rules (Convert to Standard JSON) Remove Header Keep only the 【entry】【/entry】 blocks representing paper items. Single Item Each 【entry】【/entry】 represents a single item. Field Processing Rules 【id】【/id】 ➡️ id Extract content. Example: 【id】http://arxiv.org/abs/2409.06062v1【/id】 → http://arxiv.org/abs/2409.06062v1 【updated】【/updated】 ➡️ updated Convert timestamp to yyyy-mm-dd hh:mm:ss 【published】【/published】 ➡️ published Convert timestamp to yyyy-mm-dd hh:mm:ss 【title】【/title】 ➡️ title Extract text content 【summary】【/summary】 ➡️ summary Keep text, remove line breaks 【author】【/author】 ➡️ author Combine all authors into an array Example: [ "Ernest Pusateri", "Anmol Walia" ] (for Notion multi-select field) 【arxiv:comment】【/arxiv:comment】 ➡️ Ignore / discard 【link type="text/html"】 ➡️ html_url Extract URL 【link type="application/pdf"】 ➡️ pdf_url Extract URL 【arxiv:primary_category term="cs.CL"】 ➡️ primary_category Extract term value 【category】 ➡️ category Merge all 【category】 values into an array Example: [ "eess.AS", "cs.SD" ] (for Notion multi-select field) Add Empty Fields github huggingface 3. Data Processing Analyze and summarize paper data using AI, then standardize output as JSON. Single Paper Basic Information Analysis and Enhancement Daily Paper Summary and Multilingual Translation 4. Data Storage: Notion Database Create a corresponding database in Notion with the same predefined field names. In Notion, create an integration under Integrations and grant access to the database. Obtain the corresponding Secret Key. Use the Notion "Create a database page" node to configure the field mapping and store the data. Notes "Create a database page"** only adds new entries; data will not be updated. The updated and published timestamps of arXiv papers are in UTC. Notion single-select and multi-select fields only accept arrays. They do not automatically parse comma-separated strings. You need to format them as proper arrays. Notion does not accept null values, which causes a 400 error. 5. Data Delivery Set up two channels for message delivery: EMAIL and IM, and define the message format and content. Email: Gmail GMAIL OAuth 2.0 – Official Documentation Configure your OAuth consent screen Steps: Enable Gmail API Create OAuth consent screen Create OAuth client credentials Audience: Add Test users under Testing status Message format: HTML (Model: OpenAI GPT — used to design an HTML email template) IM: Feishu (LARK) Bots in groups Use bots in groups

Overview This workflow lets you capture, store, and retrieve notes from LINE chats — both text and voice messages — and automatically send them to your Gmail inbox. By leveraging Supabase Vector Database, you can not only store and recall your notes, but also repurpose them for idea generation, quiz creation, or hypothesis building. Key Features Receive text and audio messages via LINE Transcribe audio messages automatically and save them in Supabase Trigger note storage with a specific keyword (default: “Diane”) Automatically send the latest notes to your Gmail every morning at 7 AM Search and reuse your notes (e.g., generate ideas, quizzes, or insights) Requirements Supabase account (free plan supported) LINE Messaging API channel setup (obtain your access token) Gmail authentication (OAuth2) Notes Replace placeholders such as LINE_CHANNELACCESS_TOKEN, YOUR_USERID, and YOUR_EMAIL_ADDRESS with your own information. All credentials (OpenAI, Supabase, LINE, Gmail, etc.) must be configured securely in the Credentials section of n8n. You may customize the trigger keyword (“Diane”) to any word you like.

Description: Keep your API documentation accurate and reliable with this n8n automation template. The workflow automatically tests your FAQ content related to authentication and rate limits, evaluating each answer using Azure OpenAI GPT-4o-mini for completeness, edge-case coverage, and technical clarity. It logs all results to Google Sheets, scores FAQs from 0–10, and sends Slack alerts when low-quality answers are detected. Ideal for API teams, developer relations managers, and technical writers who want to maintain high-quality documentation with zero manual review effort. ✅ What This Template Does (Step-by-Step) ▶️ Manual Trigger or On-Demand Run Start the evaluation anytime you update your FAQs — perfect for regression testing before documentation releases. 📖 Fetch FAQ Q&A from Google Sheets Reads FAQ questions and answers from your designated test sheet (columns A:B). Each Q&A pair becomes a test case for AI evaluation. 🤖 AI Evaluation via GPT-4o-mini Uses Azure OpenAI GPT-4o-mini to evaluate how well each FAQ covers critical aspects of API authentication and rate limiting. The AI provides a numeric score (0–10) and a short explanation. 🔍 Parse & Format AI Results Extracts structured JSON data (Question, Score, Explanation, Timestamp) and prepares it for reporting and filtering. 💾 Save Evaluation to Google Sheets Appends all results to a Results Sheet (A:D) — creating a running history of FAQ quality audits. ⚠️ Filter for Low-Scoring FAQs Identifies any FAQ with a score below 7, flagging them as needing review or rewrite. 🔔 Send Slack Alerts for Weak Entries Posts an alert message in your chosen Slack channel, including: The question text Score received AI’s explanation Link to the full results sheet This ensures your documentation team can quickly address weak or incomplete FAQ answers. 🧠 Key Features 🤖 AI-powered FAQ quality scoring (0–10) 📊 Automated tracking of doc health over time 📥 Seamless Google Sheets integration for results storage ⚙️ Slack notifications for underperforming FAQs 🧩 Ideal for continuous documentation improvement 💼 Use Cases 📘 Validate FAQ accuracy before API documentation updates ⚡ Auto-test new FAQ sets during content refresh cycles 🧠 Ensure API rate limit and auth topics cover all edge cases 📢 Alert documentation owners about weak answers instantly 📦 Required Integrations Google Sheets API – for reading and storing FAQs and test results Azure OpenAI (GPT-4o-mini) – for evaluating FAQ coverage and clarity Slack API – for sending quality alerts and notifications 🎯 Why Use This Template? ✅ Ensures API FAQ accuracy and completeness automatically ✅ Replaces tedious manual content reviews with AI scoring ✅ Builds an ongoing record of documentation improvements ✅ Keeps technical FAQs consistent, relevant, and developer-friendly

AI-Powered CV Feedback & Fit Score This workflow uses AI to automatically analyze a candidate’s CV against any job posting. It extracts key skills, requirements, and gaps, then generates a clear fit summary, recommendations, and optimization tips. Candidates also receive a structured email report, helping them improve their CV and focus on the right roles. No more guesswork, the workflow delivers objective, AI-powered career insights in minutes. Benefits • Automated CV analysis: Instantly compare your CV with any job description. • Clear recommendations: Get a fit score (1–10) plus “Apply,” “Consider,” or “Not a fit.” • Actionable feedback: See missing skills and concrete optimization tips. • Email reports: Candidates receive a professional summary directly in their inbox. Target Audience • Job seekers • Career coaches and recruiters • HR teams evaluating candidate job alignment • Tech bootcamps and training programs Required APIs • Google Gemini API (AI analysis) • Email credentials (send candidate reports) Easy Customization • Fit score logic: Adjust thresholds for “Apply,” “Consider,” and “Not a fit.” • Email templates: Personalize branding, tone, or add follow-up resources. • Delivery channels: Add Slack, Teams, or WhatsApp nodes for real-time feedback. • Language detection: Extend to more languages by adding translation nodes.