Jira Software and Google Sheets integration

Description Automatically triage Product UAT feedback using AI, route it to the right tools and teams, and close the feedback loop with testers, all in one workflow. This workflow analyzes raw UAT feedback, classifies it (critical bug, feature request, UX improvement, or noise), validates AI confidence, escalates when human review is needed, and synchronizes everything across Jira, Slack, Notion, Google Sheets, and email. Context Product teams often receive unstructured UAT feedback from multiple sources (forms, Slack, internal tools), making triage slow, inconsistent, and error-prone. This workflow ensures: Faster bug detection Consistent categorization Zero feedback lost Clear accountability between Product, Engineering, and Design It combines AI automation with human-in-the-loop control, making it safe for real production environments. Who is this for? Product Managers running UAT or beta programs Project Managers coordinating QA and release validation Product Ops / PMO teams Engineering teams who want faster, cleaner bug escalation Any team managing high-volume UAT feedback Perfect for teams that want speed without sacrificing control. Requirements Webhook trigger (form, internal tool, Slack integration, etc.) OpenAI account (for AI triage) Jira (bug tracking) Slack (team notifications) Notion (product roadmap / UX backlog) Google Sheets (UAT feedback log) Gmail (tester & manual review notifications) How it works Trigger The workflow starts when UAT feedback is submitted via a webhook (form, Slack, or internal tool). Normalize & Clean Incoming data is normalized into a consistent structure (tester, build, page, message) and cleaned to be AI-ready. AI Triage An AI model analyzes the feedback and returns: Type (Critical Bug, Feature Request, UX Improvement, Noise) Severity & sentiment Summary and suggested title Confidence score Quality Control If the AI output is unreliable (low confidence or parsing error), the feedback is automatically routed to manual review via email and Slack. Routing & Actions If confidence is sufficient: Critical Bugs → Jira issue + Engineering Slack alert Feature Requests → Notion roadmap UX Improvements → Design / UX tracking Noise → Archived but traceable Closed Loop The tester is notified via Slack or email, and the workflow responds to the original webhook with a structured status payload. What you get One unified UAT triage system Faster bug escalation Clean product and UX backlogs Full traceability of every feedback Automatic tester communication Safe AI usage with human fallback About me : I’m Yassin a Product Manager Scaling tech products with a data-driven mindset. 📬 Feel free to connect with me on Linkedin

ETL Monitoring & Alert Automation: Jira & Slack Integration This workflow automatically processes ETL errors, extracts important details, generates a preview, creates a log URL, classifies the issue using AI and saves the processed data into Google Sheets. If the issue is important or needs attention, it also creates a Jira ticket automatically. The workflow reduces manual debugging effort, improves visibility and ensures high-severity issues are escalated instantly without human intervention. Quick Start – Implementation Steps Connect your webhook or ETL platform to trigger the workflow. Add your OpenAI, Google Sheets and Jira credentials. Enable the workflow. Send a sample error to verify Sheets logging and Jira ticket creation. Deploy and let the workflow monitor ETL pipelines automatically. What It Does This workflow handles ETL errors end-to-end by: Extracting key information from ETL error logs. Creating a short preview for quick understanding. Generating a URL to open the full context log. Asking AI to identify root cause and severity. Parsing the AI output into clean fields. Saving the processed error to Google Sheets. Creating a Jira ticket for medium/high-severity issues. This creates a complete automated system for error tracking, analysis and escalation. Who’s It For DevOps & engineering teams monitoring data pipelines. ETL developers who want automated error reporting. QA teams verifying daily pipeline jobs. Companies using Jira for issue tracking. Teams needing visibility into ETL failures without manual log inspection. Requirements to Use This Workflow n8n account or self-hosted instance. ETL platform capable of sending error payloads (via webhook). OpenAI API Key. Google Sheets credentials. Jira Cloud API credentials. Optional: log storage URL (S3, Supabase, server logs). How It Works & Setup Steps Get ETL Error (Webhook Trigger) Receives ETL error payload and starts the workflow. Prepare ETL Logs (Code Node) Extracts important fields and makes a clean version of the error.Generates a direct link to open the full ETL log. AI Severity Classification (OpenAI / AI Agent) AI analyzes the issue, identifies cause and assigns severity. Parse AI Output (Code Node) Formats AI results into clean fields: severity, cause, summary, recommended action. Prepare Data for Logging (Set / Edit Fields) Combines all extracted info into one final structured record. Save ETL Logs (Google Sheets Node) Logs each processed ETL error in a spreadsheet for tracking. Create Jira Ticket (Jira Node) Automatically creates a Jira issue when severity is Medium, High or Critical. ETL Failure Alert (Slack Node) Sends a Slack message to notify the team about the issue. ETL Failure Notify (Gmail Node) Sends an email with full error details to the team. How to Customize Nodes ETL Log Extractor Add/remove fields based on your ETL log structure. AI Classification Modify the OpenAI prompt for custom severity levels or deep-dive analysis. Google Sheets Logging Adjust columns for environment, job name or log ID. Jira Fields Customize issue type, labels, priority and assignees. Add-Ons (Extend the Workflow) Send Slack or Teams alerts for high severity issues Store full logs in cloud storage (S3, Supabase, GCS) Add daily/weekly error summary reports Connect monitoring tools like Datadog or Grafana Trigger automated remediation workflows Use Case Examples Logging all ETL failures to Google Sheets Auto-creating Jira tickets with AI-driven severity Summarizing large logs with AI for quick analysis Centralized monitoring of multiple ETL pipelines Reducing manual debugging effort across teams Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|----------| | Sheets not updating | Wrong Sheet ID or missing permission | Reconnect and reselect the sheet | | Jira ticket fails | Missing required fields or invalid project key | Update Jira mapping | | AI output empty | Invalid OpenAI key or exceeded usage | Check API key or usage limits | | Severity always “low” | Prompt too broad | Adjust AI prompt with stronger rules | | Log preview empty | Incorrect error field mapping | Verify the structure of the ETL error JSON | Need Help? For assistance setting up this workflow, customizing nodes or adding additional features, feel free to contact our n8n developers at WeblineIndia. We can help configure, scale or build similar automation workflows tailored to your ETL and business requirements.

Automated Social Media Lead Processing with AI Summaries, Slack Alerts & Jira Ticketing This workflow automatically collects new lead messages from social media platforms, LinkedIn or web forms, filters relevant marketing inquiries using keywords, classifies and summarizes the lead with AI, logs it to Google Sheets, creates a Jira task and sends Slack notifications. Additionally, it generates weekly lead reports for team insights. It reduces manual triage, ensures no valid inquiry is missed and keeps your team updated with both immediate notifications and summary reports. Quick Start – Implementation Steps Connect your webhook to your social media inbox, LinkedIn, Twitter or web form. Add your OpenAI, Google Sheets, Jira and Slack credentials. Enable the workflow. Send a test message to confirm Google Sheets logging, Slack notification and Jira task creation. Activate the scheduler for weekly reports to track lead performance. What It Does This workflow performs the following key tasks: Filters incoming messages for marketing-related keywords like ad request, promo request, collaboration, partnership or social media inquiry. Uses OpenAI GPT to classify the lead into categories such as Sales, Support, Partnership, Influencer Inquiry or General Lead. Generates a short AI summary of the message. Logs structured lead data to Google Sheets, including username, source, category, summary and timestamp. Creates a Jira task automatically with summary, description, category and received time. Sends a Slack notification to alert the team instantly. Runs a scheduled workflow that aggregates weekly leads and sends a weekly report to Slack. This ensures a structured, automated pipeline for capturing, summarizing and assigning leads efficiently. Who’s It For Marketing and sales teams managing leads from social media and web forms. Agencies handling client campaigns and inquiries. Businesses that want automated notifications and ticketing. Teams using Slack and Jira for daily operations. Requirements to Use This Workflow n8n account or self-hosted instance. Webhook-enabled social media inbox or lead form. OpenAI API Key. Slack Bot Token with channel posting permission. Jira Software Cloud API credentials. Google Sheets credentials. Predefined keyword list for filtering messages. How It Works & Setup Steps Get DM (Webhook Trigger) Receives new messages from social media or web forms and starts the workflow. Lead Keyword Filter (Code Node) Filters incoming messages for predefined marketing keywords and removes irrelevant or spam messages. AI Lead Classifier (OpenAI Node) Classifies the lead into categories (Sales, Support, Partnership, Influencer Inquiry, General Lead) and generates a one-line summary using GPT-4.1. AI Output Parser (Code Node) Parses AI JSON output and merges it with original message data, adding timestamp and structured fields. Store Lead (Google Sheets Node) Logs structured lead data to Google Sheets including username, source, category, summary and timestamp. Create Task (Jira Node) Automatically creates a Jira story or task in your selected project with the AI summary, category and timestamp. Send a Summary (Slack Node) Sends a formatted message to your selected Slack channel, alerting your team of the new lead. Weekly Reporting Schedule Trigger** – triggers the weekly reporting workflow. Extract Lead Data** – fetches all logged leads from Google Sheets. Weekly Lead Filter** – filters data to include leads from the last week. Report Data Formatter** – calculates total leads, category counts, source counts and example leads. Weekly Report Slack** – sends a formatted weekly lead summary to Slack. How to Customize Nodes Keyword Filter Add or remove keywords in the JavaScript code to match your specific lead types or campaigns. AI Classification Update the OpenAI prompt for different summary lengths, tones, or lead categories. Google Sheets Logging Map additional columns like email, phone or campaign source as needed. Jira Fields Customize summary, description, labels, priority or assignees based on your project requirements. Slack Message Format Modify emojis, line breaks and formatting to suit your team’s Slack notifications. Add-Ons (Extend the Workflow) Send email alerts for high-priority leads. Trigger WhatsApp replies using an API provider. Integrate with CRMs like HubSpot, Zoho or Salesforce. Add sentiment analysis to detect frustrated or VIP users. Automate daily or weekly analytics reports to Slack. Use Case Examples Collecting Instagram, LinkedIn and Twitter DMs and logging them to Google Sheets. Creating automated Jira tickets for marketing inquiries. Sending instant Slack notifications for new leads. Filtering out irrelevant messages and only processing valid marketing leads. Generating weekly lead summary reports for team review. Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|----------| | No leads appearing | Webhook not receiving messages | Check webhook URL and ensure messages are sent correctly | | AI summary empty | OpenAI API key invalid or model limit reached | Regenerate API key / check usage | | Jira task not created | Missing required Jira fields or incorrect project ID | Add required fields or update Jira project settings | | Slack message not sent | Wrong channel ID or missing permissions | Reconnect Slack credentials | | Filter passes 0 items | Keywords do not match | Update or expand keyword list in filter node | Need Help? If you need assistance setting up this workflow, customizing nodes, building add-ons or automating more processes, our n8n workflow development team at WeblineIndia is happy to help. We can guide you through integrations, scaling or building end-to-end automation systems tailored to your business.

Description This workflow turns scattered user feedback into a structured product backlog pipeline. It collects feedback from three channels (Telegram bot, Google Form/Sheets, and Gmail), normalizes it, and sends it to an AI model that: Classifies the feedback (bug, feature request, question, etc.) Extracts sentiment and pain level Estimates business impact and implementation effort Generates a short summary Then a custom RICE-style priority score is computed, a Jira ticket is created automatically, a Notion page is generated for documentation, and a monthly product report is sent by email to stakeholders. It helps product & support teams move from “random feedback in multiple tools” to a repeatable, data-driven product intake process with zero manual triage. Context In most teams, feedback is: spread across emails, forms, and chat messages manually copy–pasted into Jira (when someone remembers) hard to prioritize objectively nearly impossible to review at the end of the month This workflow solves that by: Centralizing feedback from Telegram, Google Forms/Sheets, and Gmail Automatically normalizing all inputs into the same JSON structure Using AI to categorize, tag, summarize, and score each request Calculating a RICE-based priority adapted to your tiers (free / pro / enterprise) Creating a Jira issue with all the context and acceptance criteria Generating a Notion page for each feedback+ticket pair Sending a monthly “Product Intelligence Report” by email with insights & recommendations The result: less manual work, better prioritization, and a clear story of what users are asking for. Target Users This template is designed for: Product Managers and Product Owners SaaS teams with multiple feedback channels Support / CS teams that need a structured escalation path Project Managers who want objective, data-driven prioritization Any team that wants “feedback → backlog” automation without building a custom platform Technical Requirements You’ll need: Google Sheets credential Gmail credential Telegram Bot + Chat ID Google Form connected to a Google Sheet Jira credential (Jira Cloud) Notion credential OpenAI/ Anthropic credential for the AI analysis node An existing Jira project where tickets will be created A Notion database or parent page where feedback pages will be stored Workflow Steps The workflow is organized into four main sections: 1) Triggers (Multi-channel Intake) Telegram Trigger – Listens for new messages sent to your bot Google Form / Sheet Trigger – Listens for new form responses / rows Gmail Trigger – Listens for new emails matching your filter (e.g. [Feedback] in subject) All three paths send their payloads into a “Data Normalizer” node that outputs a unified structure: 2) Request Treated and Enriched (AI Analysis) Instant Reply (Telegram only) – Sends a quick “Thanks, we’re analysing your feedback” message User Enrichment – Enriches user tier based on mapping Message a Model (AI) classifies the feedback extracts tags scores sentiment, pain, business impact, effort generates a short summary & acceptance criteria JSON Parse / Merge – Merges AI output back into the original feedback object 3) Priority Calculation & Jira Ticket Creation Priority Calculator applies a RICE-style formula using: pain level business impact implementation effort user tier weight assigns internal priority: P0 / P1 / P2 / P3 maps to Jira priority: Highest / High / Medium / Low Create Jira Issue – Creates a ticket with: summary from AI description including raw feedback, AI analysis, and RICE breakdown labels based on tags priority based on the calculator Post-processing – Prepares a clean payload for notifications & logging IF (Source = Telegram) – Sends a rich Telegram message back to the user with: Jira key + URL category, priority, RICE score, tags, and estimated handling time Append to Google Sheet (Analytics Log) – Logs each feedback with: source, user, category, sentiment, RICE score, priority, Jira key, Jira URL Create Notion Page – Creates a documentation page linking: the feedback the Jira ticket AI analysis acceptance criteria 4) Monthly Reporting (Product Intelligence Report) Monthly Trigger – Runs once a month Query Google Sheet – Fetches all feedback logs for the previous month Aggregate Monthly Stats – Computes: feedback volume breakdown by category / sentiment / source / tier / priority average RICE, pain, and impact top P0/P1 issues and top feature requests Message a Model (AI) – Generates a written “Product Intelligence Report” with: executive summary key insights & trends top pain points strategic recommendations Parse Response: Extracts structured insights + short summary Create Notion Report Page with: metrics, charts-ready tables, insights, and recommendations Append Monthly Log to Google Sheet – Stores high-level stats for historical tracking Send Email with a formatted HTML report to stakeholders with: key metrics top issues recommendations link to the full Notion report Key Features Multi-channel intake: Telegram + Google Forms/Sheets + Gmail AI-powered triage: automatic category, sentiment, tags, and summary RICE-style priority scoring with tier weighting Automatic Jira ticket creation with full context Notion documentation for each feedback and for monthly reports Google Sheets analytics log for exploration and dashboards Monthly “Product Intelligence Report” sent automatically by email Designed to be adaptable: you can plug in your own labels, tiers, and scoring rules Expected Output When the workflow is running, you can expect: A Jira issue created automatically for each relevant feedback A confirmation email A Telegram confirmation message when the feedback comes from Telegram A Google Sheet filled with normalized feedback and scoring data A Notion page per feedback/ticket with AI analysis and acceptance criteria Every month: a Notion “Monthly Product Intelligence Report” page a summary email with key metrics and insights for your stakeholders How it works Trigger – Listens to Telegram / Google Forms / Gmail Normalize – Converts all inputs to a unified feedback format Enrich with AI – Category, sentiment, pain, impact, effort, tags, summary Score – Computes RICE-style priority and maps to Jira priority Create Ticket – Opens a Jira issue + Notion page + logs to Google Sheets Notify – Sends Telegram confirmation (if source is Telegram) Report – Once a month, aggregates everything and sends a Product Intelligence Report Tutorial Video Tutorial video: Watch the Youtube Tutorial video About me I’m Yassin a Project & Product Manager Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin

📘 Description This workflow automates dependency update risk analysis and reporting using Jira, GPT-4o, Slack, and Google Sheets. It continuously monitors Jira for new package or dependency update tickets, uses AI to assess their risk levels (Low, Medium, High), posts structured comments back into Jira, and alerts the DevOps team in Slack — all while logging historical data into Google Sheets for visibility and trend analysis. This ensures fast, data-driven decisions for dependency upgrades, improved code stability, and reduced security risks — with zero manual triage. ⚙️ What This Workflow Does (Step-by-Step) 🟢 When Clicking “Execute Workflow” Manually triggers the dependency risk analysis sequence for immediate review or scheduled monitoring. 📋 Fetch All Active Jira Issues Retrieves all active Jira issues to identify tickets related to dependency or package updates. Provides the complete dataset — including summary, status, and assignee information — for AI-based risk evaluation. ✅ Validate Jira Query Response Verifies that Jira returned valid issue data before proceeding. If data exists → continues filtering dependency updates. If no data or API error → logs the failure to Google Sheets. Prevents workflow from continuing with empty or broken datasets. 🔍 Identify Dependency Update Issues Filters Jira issues to find only dependency-related tickets (keywords like “update,” “bump,” “package,” or “library”). This ensures only relevant version update tasks are analyzed — filtering out unrelated feature or bug tickets. 🏷️ Extract Relevant Issue Metadata Extracts essential fields such as key, summary, priority, assignee, status, and created date for downstream AI processing. Simplifies the data payload and ensures accurate, structured analysis. 📢 Alert DevOps Team in Slack Immediately notifies the assigned DevOps engineer via Slack DM about any new dependency update issue. Includes formatted details like summary, key, status, priority, and direct Jira link for quick access. Ensures rapid visibility and faster response to potential risk tickets. 🤖 AI-Powered Risk Assessment Analyzer Uses GPT-4o (Azure OpenAI) to intelligently evaluate each dependency update’s risk level and impact summary. Considers factors such as: Dependency criticality Version change type (major/minor/patch) Security or EOL indicators Potential breaking changes Outputs a clean JSON with fields: {"risk_level": "Low | Medium | High","impact_summary": "Short human-readable explanation"} Helps DevOps teams prioritize updates with context. 🧠 GPT-4o Language Model Configuration Configures the AI reasoning engine for precise, context-aware DevOps assessments. Optimized for consistent technical tone and cost-efficient batch evaluation. 📊 Parse AI Response to Structured Data Safely parses the AI’s JSON output, removing markdown artifacts and ensuring structure. Adds parsed fields — risk_level and impact_summary — back to the Jira context. Includes fail-safes to prevent crashes on malformed AI output (fallbacks to “Unknown” and “Failed to parse”). 💬 Post AI Risk Assessment to Jira Ticket Automatically posts the AI’s analysis as a comment on the Jira issue: Displays 🤖 AI Risk Assessment Report header Shows Risk Level and Impact Summary Includes a checklist of next steps for developers Creates a permanent audit trail for each dependency decision inside Jira. 📈 Log Dependency Updates to Tracking Dashboard Appends all analyzed updates into Google Sheets, recording: Date Jira Key & Summary Risk Level & Impact Summary Assignee & Status This builds a historical dependency risk database that supports: Trend monitoring Security compliance reviews Dependency upgrade metrics DevOps productivity tracking 📊 Log Jira Query Failures to Error Sheet If the Jira query fails, the workflow automatically logs the error (API/auth/network) into a centralized error sheet for troubleshooting and visibility. 🧩 Prerequisites Jira Software Cloud API credentials Azure OpenAI (GPT-4o) access Slack API connection Google Sheets OAuth2 credentials 💡 Key Benefits ✅ Automated dependency risk assessment ✅ Instant Slack alerts for update visibility ✅ Historical tracking in Google Sheets ✅ Reduced manual triage and faster decision-making ✅ Continuous improvement in release reliability and security 👥 Perfect For DevOps and SRE teams managing large dependency graphs Engineering managers monitoring package updates and risks Security/compliance teams tracking vulnerability fix adoption Product teams aiming for stable CI/CD pipelines

Description Streamline IT and operations change management by automating approval routing, Jira issue creation, audit logging, and real-time Slack alerts. This workflow ensures faster reviews, traceable approvals, and transparent communication across systems. 🚀 What This Template Does Step 1: Triggers automatically every weekday at 3:00 AM to fetch new or updated change requests from Monday.com. ⏰ Step 2: Extracts key fields (request name, component, risk level, approvers, and description) for structured processing. 🧩 Step 3: Routes each request based on its current status — Pending, Approved, or Rejected. 🔀 Step 4: Sends Slack alerts for pending approvals with detailed context for quick action. 💬 Step 5: Creates Jira tickets for approved requests, ensuring smooth implementation tracking. 🎫 Step 6: Logs all approved requests to Google Sheets for compliance and audit purposes. 📊 Step 7: Sends confirmation emails to requesters with Jira ticket details via Gmail. 📧 Step 8: Automatically creates resubmission items in Monday.com for rejected requests. 🔁 Key Benefits ✅ Eliminates manual approval routing between tools ✅ Centralizes audit trails and implementation data ✅ Accelerates change management turnaround ✅ Provides real-time alerts to approvers and teams ✅ Ensures compliance with automated record-keeping Features Automated daily trigger (Mon–Fri, 3 AM) Monday.com integration for request intake Conditional branching by status (Pending, Approved, Rejected) Jira issue creation for approved requests Slack notifications for pending and approved updates Google Sheets logging for audit tracking Gmail email confirmations for requesters Automatic resubmission handling for rejected requests Requirements Monday.com API credentials with board access Jira API credentials with project permissions Google Sheets OAuth2 credentials Slack Bot token with chat:write permissions Gmail OAuth2 credentials for email automation Target Audience IT & DevOps teams managing structured change approvals Project management teams tracking implementation requests Organizations seeking automated risk-based change routing Managers needing centralized logs and instant Slack alerts Step-by-Step Setup Instructions Connect your Monday.com account and replace YOUR_BOARD_ID and groupId. Configure Jira credentials and set the target project for new tickets. Link your Google Sheets document and replace YOUR_SHEET_ID and YOUR_SHEET_GID. Add Slack credentials and update YOUR_CHANNEL_ID for notifications. Set up Gmail OAuth2 for sending confirmation emails. Adjust the cron expression (0 3 * * 1-5) if needed to match your timezone. Run the workflow manually once to test end-to-end connectivity. Enable for scheduled automation and enjoy a fully managed approval process.

Description Automate customer feedback analysis and action planning by integrating Monday.com, Azure OpenAI, Jira, Google Sheets, and Outlook. This workflow classifies customer feedback with AI, calculates business impact, creates Jira tasks for high-priority issues, and sends weekly performance summaries — turning raw feedback into actionable insights. 💬📈🤖 What This Template Does Step 1: Triggers automatically every Monday at 9:00 AM to fetch new customer feedback from Monday.com. ⏰ Step 2: Normalizes and structures data into consistent fields (title, account, ARR, NPS, etc.). 🧩 Step 3: Uses Azure OpenAI GPT-4 to classify sentiment and identify feedback themes (e.g., “UI Design,” “App Crash”). 🧠 Step 4: Calculates a business impact score based on ARR, NPS delta, and sentiment weightings. ⚙️ Step 5: Creates Jira tasks for high-impact feedback items for product or engineering follow-up. 🎫 Step 6: Logs all feedback and impact scores into Google Sheets for analytics dashboards. 📊 Step 7: Generates a professional HTML report summarizing metrics, wins, and risks, then emails it via Outlook. 📧 Step 8: Sends automated error-alert emails if any node fails during execution. 🚨 Key Benefits ✅ Converts qualitative feedback into measurable business intelligence ✅ Identifies critical customer issues automatically using AI ✅ Reduces manual effort in triaging and prioritizing feedback ✅ Creates real-time visibility for product and CX teams ✅ Provides weekly executive summaries and performance insights Features Weekly scheduled trigger (every Monday 9 AM) Monday.com data fetching and field normalization Azure OpenAI GPT-4-based sentiment and theme detection Impact scoring combining ARR + NPS + sentiment weighting Jira issue creation with context-rich descriptions Google Sheets logging for dashboards and historical records Outlook HTML email reports for leadership visibility Automated Gmail error-notification system Requirements Monday.com API credentials with board access Azure OpenAI GPT-4 API credentials Jira Software Cloud API credentials Google Sheets OAuth2 credentials with edit permissions Microsoft Outlook OAuth2 credentials for email delivery Gmail OAuth2 credentials for error alerting Target Audience Product and CX teams analyzing customer sentiment SaaS businesses tracking post-implementation feedback Customer-success and support operations teams Product managers prioritizing improvements based on impact Leadership teams monitoring customer health and satisfaction Step-by-Step Setup Instructions 1️⃣ Connect Monday.com API and update your boardId and groupId. 2️⃣ Configure Azure OpenAI GPT-4 credentials for the AI classifier. 3️⃣ Set Jira project ID and issue type for ticket creation. 4️⃣ Link Google Sheets and replace YOUR_SHEET_ID. 5️⃣ Connect Outlook OAuth2 and add recipient email for reports. 6️⃣ Configure Gmail OAuth2 for error alerts. 7️⃣ Adjust the cron expression (0 9 * * 1) to fit your timezone. 8️⃣ Test the workflow end-to-end with sample data. 9️⃣ Enable automation for seamless weekly feedback intelligence. ✅

Description: Transform your Jira project management workflow with this intelligent n8n automation template that continuously tracks, scores, and reports the health of Jira Epics. The automation runs every 6 hours, fetches all active Epics, analyzes linked issues for performance, quality, and stability metrics, and automatically flags at-risk Epics. It updates Jira fields, sends alerts to Slack, logs trends in Google Sheets, and syncs visibility with Monday.com—ensuring teams stay proactive, not reactive. Ideal for agile teams, project managers, and product owners looking to monitor delivery health, detect risks early, and maintain transparent reporting across tools. ✅ What This Template Does (Step-by-Step) ⏱ Trigger Every 6 Hours: Automatically executes every six hours to keep health data updated in near real-time. 📥 Fetch All Epics from Jira: Retrieves all Epics, their keys, and fields via the Jira API to establish a full analysis scope. 🔀 Split Epics for Processing: Converts the batch of Epics into individual items, enabling sequential metric analysis. 🔗 Fetch Linked Issues: Collects all issues linked to each Epic, capturing their types, statuses, cycle times, and labels for deeper health analysis. 📈 Calculate Health Score: Computes a weighted score (0–1 scale) based on: • 40% Average Cycle Time • 30% Bug Ratio • 20% Churn (Reopened issues) • 10% Blocker Ratio Scores above 0.6 indicate at-risk Epics. ⚖️ Decision Gate: At-Risk or Healthy: If the health score exceeds 0.6, the workflow automatically initiates corrective actions. 🔧 Update Jira Epic: Updates Jira with the computed health score and adds an “At Risk” label for visibility in dashboards and filters. 🚨 Send Slack Alerts: Notifies the #project-alerts channel with Epic details, health score, and direct Jira links for immediate attention. 📋 Update Monday.com Pulse: Syncs health metrics and risk status back to your Monday board, maintaining cross-platform transparency. 📊 Log to Google Sheets: Appends health score logs with timestamps and Epic keys for trend analysis, audits, and dashboard creation. 🧠 Key Features ✔️ Automated Jira Epic health scoring (cycle time, churn, bugs, blockers) ✔️ Real-time risk flagging with Slack alerts ✔️ Integrated cross-tool visibility (Jira + Monday + Sheets) ✔️ Continuous trend tracking for performance improvement ✔️ Secure API-based automation 💼 Use Cases 💡 Track project delivery health and spot risks early 📈 Build executive dashboards showing team velocity and quality 🤝 Align product and engineering with shared visibility 🧾 Maintain a compliance audit trail of Epic health trends 📦 Required Integrations • Jira Software Cloud API – for Epic and issue data • Slack API – for real-time team alerts • Monday.com API – for visual board updates • Google Sheets API – for historical tracking and analytics 🎯 Why Use This Template? ✅ Prevents project delays by flagging risks early ✅ Provides automated, data-driven Epic health insights ✅ Connects your reporting ecosystem across platforms ✅ Perfect for Agile and DevOps teams driving continuous improvement

Description Automate Jira backlog management with intelligent cleanup, prioritization, and AI-powered reporting. This workflow scans daily to identify stale issues, missing priorities, and overdue tasks — auto-updates Jira with corrective labels, logs everything into Google Sheets for tracking, and notifies teams via Slack. Every Friday, it sends an AI-generated backlog summary email to project leads for visibility and planning. 🚀📅 What This Template Does Step 1: Triggers automatically every weekday at 7:00 AM to fetch backlog issues from Jira. ⏰ Step 2: Filters issues missing estimates, assignees, or priority values for cleanup. 🧹 Step 3: Applies corrective labels (e.g., “Needs Estimation,” “Unassigned,” “Overdue”). 🏷️ Step 4: Logs all flagged issues into Google Sheets with timestamps for audit tracking. 📊 Step 5: Sends real-time Slack alerts summarizing key backlog insights. 💬 Step 6: Every Friday, uses GPT-4 to generate a summarized backlog health report. 🤖 Step 7: Delivers weekly summary emails to leads and project managers via Gmail. 📧 Key Benefits ✅ Eliminates manual backlog reviews and prioritization. ✅ Ensures consistent Jira hygiene and task visibility. ✅ Provides centralized backlog tracking via Google Sheets. ✅ Sends real-time alerts for overdue and unassigned tasks. ✅ Offers AI-driven insights for better sprint planning. Features Automated daily trigger (Mon–Fri, 7 AM) Jira issue fetching and filtering by priority and assignment Smart labeling for hygiene tracking Slack alerts for backlog anomalies Weekly GPT-4 generated summary reporting Google Sheets integration for historical logging Gmail integration for summary email delivery Requirements Jira API credentials with read/write issue permissions Google Sheets OAuth2 credentials for data logging Slack Bot token with chat:write permissions Gmail OAuth2 credentials for email delivery OpenAI or Azure OpenAI API key for GPT-4 summarization Target Audience Agile and Scrum teams maintaining large backlogs 🧩 Product managers ensuring backlog quality and consistency 📋 Engineering leads seeking proactive backlog hygiene 🛠️ Organizations needing visibility across project tasks 🏢 Remote teams using Slack for daily syncs 🌐 Step-by-Step Setup Instructions Connect Jira credentials and specify your project key(s). 🔑 Link your Google Sheet and replace YOUR_SHEET_ID for backlog tracking. 📊 Configure Slack and replace YOUR_CHANNEL_ID for alert delivery. 💬 Add Gmail credentials and define recipient emails for weekly reports. 📧 Add your GPT-4 API key (OpenAI or Azure) for AI summarization. 🤖 Adjust cron expression (0 7 * * 1-5) to match your local timezone. ⏰ Run manually once to validate all connections, then enable automation. ✅

📊 Description Streamline IT and operations change management by automating approval routing, Jira issue creation, audit logging, and real-time Slack alerts. This workflow ensures faster reviews, traceable approvals, and transparent communication across systems. 🚀💼 What This Template Does Step 1: Triggers automatically every weekday at 3:00 AM to fetch new or updated change requests from Monday.com. ⏰ Step 2: Extracts key fields (request name, component, risk level, approvers, and description) for structured processing. 🧩 Step 3: Routes each request based on its current status — Pending, Approved, or Rejected. 🔀 Step 4: Sends Slack alerts for pending approvals with detailed context for quick action. 💬 Step 5: Creates Jira tickets for approved requests, ensuring smooth implementation tracking. 🎫 Step 6: Logs all approved requests to Google Sheets for compliance and audit purposes. 📊 Step 7: Sends confirmation emails to requesters with Jira ticket details via Gmail. 📧 Step 8: Automatically creates resubmission items in Monday.com for rejected requests. 🔁 Key Benefits ✅ Eliminates manual approval routing between tools ✅ Centralizes audit trails and implementation data ✅ Accelerates change management turnaround ✅ Provides real-time alerts to approvers and teams ✅ Ensures compliance with automated record-keeping Features Automated daily trigger (Mon–Fri, 3 AM) Monday.com integration for request intake Conditional branching by status (Pending, Approved, Rejected) Jira issue creation for approved requests Slack notifications for pending and approved updates Google Sheets logging for audit tracking Gmail email confirmations for requesters Automatic resubmission handling for rejected requests Requirements Monday.com API credentials with board access Jira API credentials with project permissions Google Sheets OAuth2 credentials Slack Bot token with chat:write permissions Gmail OAuth2 credentials for email automation Target Audience IT & DevOps teams managing structured change approvals Project management teams tracking implementation requests Organizations seeking automated risk-based change routing Managers needing centralized logs and instant Slack alerts Step-by-Step Setup Instructions Connect your Monday.com account and replace YOUR_BOARD_ID and groupId. Configure Jira credentials and set the target project for new tickets. Link your Google Sheets document and replace YOUR_SHEET_ID and YOUR_SHEET_GID. Add Slack credentials and update YOUR_CHANNEL_ID for notifications. Set up Gmail OAuth2 for sending confirmation emails. Adjust the cron expression (0 3 * * 1-5) if needed to match your timezone. Run the workflow manually once to test end-to-end connectivity. Enable for scheduled automation and enjoy a fully managed approval process. ✅

Description: This workflow automates team capacity monitoring using Jira data to identify over-allocated team members and alert managers instantly. It ensures proactive workload management by fetching active issues, calculating utilization rates, logging capacity metrics, and sending detailed email alerts when members exceed 100% capacity. It helps project managers prevent burnout, balance workloads, and maintain operational efficiency — all with zero manual tracking. What This Workflow Does (Step-by-Step) 🟢 Manual Trigger – Start the capacity analysis manually on demand. 📋 Fetch Active Jira Issues – Retrieves all “In Progress” tasks from Jira to analyze workloads. ✅ Data Validation – Checks whether Jira returned valid data before continuing. True Path: Moves to capacity calculation. False Path: Logs query failure to error tracking sheet. 📊 Capacity Calculator – Aggregates logged hours per user and calculates utilization percentage based on an 8-hour daily capacity. 📈 Log Capacity Data to Tracking Sheet – Appends capacity metrics (Assignee, Total Hours, Utilization %, Status) to a Google Sheet for historical tracking and trend analysis. ⚠️ Over-Allocation Check – Identifies team members exceeding 100% utilization (status = “Overallocated”). 📢 Alert Report Generator – Builds a dynamic report summarizing all over-allocated members, their logged hours, utilization %, and corrective suggestions. Generates both alert and “All Clear” reports based on findings. 📧 Send Over-Allocation Alert to Manager – Sends an automated Gmail alert to the project manager, including severity-based subject lines and detailed breakdown of each over-allocated member. 🚨 Log Query Failures to Error Sheet – Records any Jira API or data retrieval issues in the “error log sheet” for monitoring and debugging. Prerequisites Jira account with API access Google Sheets for “Team Capacity Tracking” and “Error Log” Gmail credentials for automated email delivery Key Benefits ✅ Early detection of team over-allocation ✅ Automated data logging and historical tracking ✅ Real-time email alerts to prevent burnout ✅ Data-driven sprint planning and workload balancing ✅ Zero manual monitoring required Perfect For Project Managers and Scrum Masters tracking team load Engineering teams managing multiple active sprints Organizations looking to automate workload visibility HR and PMOs monitoring resource utilization trends

Description  Automates daily EOD summaries from Jira issues into an Excel sheet, then compiles a weekly summary using Azure OpenAI (GPT-4o-mini) and delivers it to stakeholders via email. Gain consistent reporting, clear insights, and hands-free delivery. ✨📧 What This Template Does  Fetches Jira issues and extracts key fields. 🧩 Generates End‑of‑Day summaries and stores them in Excel daily. 📄 Aggregates the week’s EOD data from Excel. 📚 Creates a weekly summary using Azure OpenAI (GPT-4o-mini). 🤖 Delivers the weekly report to stakeholders via email. 📬 Key Benefits  Saves time with fully automated daily and weekly reporting. ⏱️ Ensures consistent, structured summaries every time. 📏 Improves clarity for stakeholders with readable insights. 🪄 Produces mobile-friendly email summaries for quick consumption. 📱 No-code customization inside n8n. 🛠 Features  Jira issue ingestion and transformation. Daily EOD summary generation and Excel storage. Weekly AI summarization with Azure OpenAI (GPT-4o-mini). Styled HTML email output to stakeholders. Scheduling for hands-free execution. Requirements  An n8n instance (cloud or self-hosted). Jira access to read issues. Azure OpenAI (GPT-4o-mini) for weekly AI summarization. Email service (Gmail/SMTP) configured in n8n credentials. Excel/Sheet storage set up to append and read daily EOD entries. Target Audience  Engineering and product teams needing routine summaries. Project managers tracking daily progress. Operations teams consolidating weekly reporting. Stakeholders who prefer clean email digests. Step-by-Step Setup Instructions  Jira: Connect your Jira credentials and confirm issue read access. Azure OpenAI: Deploy GPT-4o-mini and add Azure OpenAI credentials in n8n. Gmail/SMTP: Connect your email account in n8n Credentials and authorize sending. Excel/Sheet: Configure the sheet used to store daily EOD summaries. Import the workflow, assign credentials to nodes, replace placeholders, then run and schedule. Security Best Practices  Use scoped API tokens for Jira with read-only permissions. 🔐 Store Azure OpenAI and email credentials in n8n’s encrypted credentials manager. 🧯 Limit email recipients to approved stakeholder lists. 🚦 Review logs regularly and rotate credentials on a schedule. ♻

Description  Automatically assigns new tasks from an Excel/Google Sheets source to the best-fit employee based on expertise, then creates issues in Jira. Gain fast, consistent routing that reduces manual triage and speeds delivery. 🧠📊➡️🗂️ What This Template Does  Fetches new task rows and related areas from Google Sheets/Excel. Analyzes each item with an AI Agent using Azure OpenAI. Selects the best-fit employee by matching the area to expertise stored in the sheet. Returns structured outputs (task, assignee, expertise, ID, bug/task) and creates the Jira issue. Applies rule-based handling for bugs vs tasks via a Switch node. Key Benefits  ⏱ Save time by automating task assignment from new entries. 🎯 Improve accuracy with expertise-based matching. 📋 Keep clean, structured outputs for downstream systems. 🔁 Seamless handoff from Sheets to Jira with no manual steps. Features  Google Sheets Trigger: Reads new task name and related area from the sheet. AI Agent (Azure OpenAI): Evaluates expertise fit and decides the best assignee. Structured Output Parser: Returns exactly five fields: task_name, assignee_name, expertise, employee_id, item_type (bug/task). Jira Create Issue: Creates issues in Jira using selected assignee and item type. Switch (Rules): Routes logic for bugs vs tasks for consistent categorization. Requirements  n8n instance: Cloud or self-hosted. Google Sheets access: Sheet containing employee roster with columns for Name, Expertise, and ID; connect credentials in n8n. Azure OpenAI (GPT-4o-mini): Configure the Azure OpenAI Chat Model credentials for the AI Agent. Jira credentials: Authorized account with permissions to create issues. Output Parser setup: Structured Output Parser configured to the five-field schema: task_name, assignee_name, expertise, employee_id, item_type. Target Audience  🧩 IT Support and Ops teams routing incoming work. 🧭 Project managers orchestrating assignments at scale. 🛠 Engineering managers seeking consistent triage. 📈 Business operations teams automating intake to delivery. Step-by-Step Setup Instructions  Connect Google Sheets credentials and map the task and area fields; ensure roster columns (Name, Expertise, ID) are present. Add Jira credentials and set the Create Issue node to your target project and issue type. Configure Azure OpenAI (GPT-4o-mini) for the AI Agent and provide credentials. Import the workflow, assign all credentials, and align the Structured Output Parser to the five-field schema. Run a test with sample rows; confirm assignee selection and Jira issue creation; then enable scheduling. Security Best Practices  Use least-privilege API tokens for Google Sheets and Jira. Restrict sheet access to only required users and service accounts. Validate and sanitize incoming task data before issue creation. Store credentials securely in n8n and rotate them regularly. Log only necessary fields; avoid sensitive data in workflow logs.

Description Automated workflow that creates Jira issues directly from Google Forms. The flow validates and normalizes the data, creates the Jira issue, writes the key back to the Google Sheet, and sends a Gmail notification. Context This template bridges lightweight Google Forms with enterprise Jira. It enables instant ticket creation while keeping Jira the single source of truth. The flow is idempotent (no duplicates) and production-friendly, with clean field normalization and safe mappings. Target Users Product / Ops teams running request portals on Google Forms Engineering managers who need quick Jira integration without custom UI Project managers who track intake in Google Sheets but want Jira as the system of record Orgs that want controlled ticket creation without exposing Jira directly Technical Requirements Jira Cloud project + API email + API token + “Create issues” permission Google Form + response Sheet Gmail credential for notifications Workflow Steps Trigger when a row is added Normalize Fields – Trim/clean text Create Jira Issue – POST to Jira REST; safe mappings Update Google Sheet – Match by Horodateur or rowNumber; write jira_key, issue_url, status, updated_at. Send Gmail – HTML email with key, title, link, priority, requester. Key Features Real-time (no polling): Forms → trigger→ n8n Idempotent updates using the Form timestamp (“Horodateur”) Clean normalization: summary/description/labels all standardized once Safe Jira mappings: priority via ID Notification: branded HTML email with all key fields Expected Output Google Form to create the issue Sheet updated with jira_key, issue_url, status, updated_at A valid Jira issue in the configured project Email sent to stakeholders / requester How it works ⏰ Trigger – As soon as a row is added, the workflow is triggered 🧱 Normalize – Clean summary/description/labels; pick reporter_email 🧾 Create – POST to /rest/api/3/issue, capture { id, key, self } 📗 Update – Write jira_key, issue_url, status, updated_at back to the Sheet ✉️ Notify – Send Gmail HTML confirmation to stakeholders/requester Tutorial video: Watch the Youtube Tutorial video About me : I'm Yassin, IT Project Manager, Agile & Data specialist. Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin

Android Feature Flag Cleanup Bot (GitLab + LaunchDarkly) This n8n automation detects unused (“dead”) feature flags in an Android Kotlin/Java codebase by comparing your GitLab repository code against LaunchDarkly’s feature flag list. It logs results in Google Sheets, creates Jira tickets for cleanup and sends Slack alerts automatically. Who’s it for Android engineering teams using Kotlin/Java. Teams managing feature flags in LaunchDarkly. DevOps/QA teams wanting to reduce technical debt from stale flags. How it works Weekly Trigger runs the process. GitLab Node fetches repository code. Regex Extraction finds all feature flags in code. LaunchDarkly API retrieves all configured flags. Comparison Logic marks flags as “dead” if unused in code and archived or off in production. Google Sheets stores flagged results. Jira creates a ticket for each dead flag. Slack notifies the team. How to set up Import JSON into n8n. Connect credentials for: GitLab OAuth2 Google Sheets Jira Slack webhook URL Update: GitLab repo details in the GitLab node. LaunchDarkly API key in HTTP Request node. Google Sheet ID in Google Sheets node. Jira project & issue type in Jira node. Slack message formatting in Slack node. Activate workflow. Requirements n8n** (self-hosted or cloud) GitLab repository with Kotlin/Java code LaunchDarkly account + API token Google Sheets API access Jira API access Slack incoming webhook How to customize Change regex pattern in “Detect flags” node if your flag naming convention differs. Adjust dead flag logic in “Find dead flags” node (e.g., treat test env separately). Modify Slack message to include more details (e.g., description from LaunchDarkly). Add email notifications for broader distribution. Add-ons Email Alerts** via Gmail/SMTP. GitHub / GitLab MR** to remove dead flags automatically. Confluence Integration** to document flag cleanup history. Use Case Examples Weekly automated cleanup alerts for large engineering teams. Maintaining clean feature flag lists in high-traffic apps. Compliance-driven projects requiring flag lifecycle tracking. Common troubleshooting | Issue | Possible Cause | Solution | | ------------------------------------ | --------------------------------------------- | -------------------------------------------------------- | | Workflow fails at GitLab node | Invalid repo path or missing OAuth scope | Update repo path & check GitLab OAuth permissions | | LaunchDarkly API request returns 401 | Invalid or expired API key | Generate a new API key in LaunchDarkly & update node | | Google Sheets node fails | Wrong Sheet ID or missing sharing permissions | Confirm Sheet ID and share with connected Google account | | Jira ticket not created | Missing required fields | Set project key, issue type, and summary in Jira node | | Slack alert not sent | Webhook URL invalid or revoked | Regenerate Slack webhook and update in node | Need Help? If you’d like, we can help set up and customize this workflow for your exact repo, flag rules and team notification preferences — including regex adjustments, extra reporting or adding automatic cleanup PRs. Contact our n8n automation team at WeblineIndia.