Gmail and Jira Software integration

Analyze & Sort Suspicious Email Contents with ChatGPT and Jira Who is this for? This workflow is tailored for IT security teams, managed service providers (MSPs), and organizations aiming to streamline the detection and reporting of phishing emails. It's especially useful for teams handling high email volumes and requiring quick, automated analysis. What problem is this workflow solving? Phishing emails pose a significant cybersecurity threat, and manual review processes are time-consuming and prone to human error. This workflow automates the identification of malicious emails, provides AI-driven insights, and generates structured reports, enabling faster and more efficient responses to email-based threats. What this workflow does This workflow integrates Gmail or Microsoft Outlook to monitor and capture incoming emails. It processes the email content and headers, converts the email's body to a visual screenshot for clarity, and uses ChatGPT's advanced AI to analyze the email for phishing indicators. Based on the analysis, it categorizes emails as potentially malicious or benign, creating detailed Jira tickets for each case. Attachments, including the email body and screenshots, are automatically uploaded for comprehensive reporting. Key steps include: Email Integration: Captures emails from Gmail or Microsoft Outlook. Content Processing: Extracts and organizes email content and metadata. AI Analysis: Uses ChatGPT to evaluate email content and headers. Classification: Categorizes emails as malicious or benign. Automated Reporting: Creates Jira tickets with detailed analysis and attachments. Setup Authentication: Configure Gmail or Microsoft Outlook credentials in n8n. API Keys: Add credentials for the HTML screenshot service (hcti.io) and OpenAI. Jira Configuration: Set up project and issue types in the Jira nodes. Customization: Update sticky notes and nodes to fit your organizational requirements, such as modifying the AI prompt or Jira ticket fields. How to customize this workflow to your needs Adjust email triggers to include or exclude specific senders or subjects. Refine the AI prompt in the ChatGPT node to tailor phishing detection criteria. Modify Jira ticket content to include additional fields or match specific workflows. This workflow is ideal for automating email threat detection, reducing response times, and enhancing overall cybersecurity processes. By leveraging AI-powered insights, it helps organizations stay ahead of phishing attacks.

Description Automated workflow that generates a Sprint Report from Jira and delivers it by Gmail. The flow fetches sprint issues from Jira, validates and normalizes the data, calculates metrics (tickets, story points, blockers, completion rate), generates an HTML report, and sends it by email. Context This template helps teams keep stakeholders updated automatically of the current sprint. Instead of manually compiling Jira data, the report is generated and sent on schedule (e.g., every Friday at 17:00). It’s production-friendly, reusable, and works across Jira projects. Target Users Scrum Masters and Agile Coaches who need sprint reports for retrospectives. Product Owners who want a weekly overview of sprint progress. Project Managers tracking Jira delivery KPIs. Engineering teams wanting automated status reporting without extra overhead. Technical Requirements Jira Cloud project + API email + API token + permission to read issues. Gmail credential for notifications. Workflow Steps Trigger – Schedule (e.g., Friday at 17:00). Edit Fields – Configure Jira base URL, project key, email recipients. Get Many Issues – Fetch sprint issues with JQL (project = <KEY> AND sprint in openSprints()). Validation & Normalization – Clean/validate fields (status, assignee, priority, story points, sprint info). Metrics Calculation – Aggregate KPIs (done, in progress, blockers, story points, completion %). HTML Report Generation – Build a styled email-friendly HTML summary + detailed table. Send Gmail – Deliver report to stakeholders. Key Features Automated Sprint Reports: No manual copy-paste. Metrics overview: Tickets done vs total, blockers, story points. Detailed table: Issue key, summary, status, assignee, priority, SP. Email delivery: HTML report with Jira links sent to stakeholders. Fully customizable: Adjust fields, KPIs, and recipients easily. Expected Output 📊 HTML Sprint Report with KPIs and issue table. ✅ Email delivered to stakeholders via Gmail. 🔗 Jira links embedded for easy navigation. How it works ⏰ Trigger – Runs on schedule (e.g., every Friday at 17:00). 🧾 Fetch Issues – JQL filters sprint tickets. 📊 Metrics – Done vs total, SP progress, blockers. 💻 Generate HTML – Clean, styled table and summary. ✉️ Notify – Send Gmail with full sprint report to stakeholders. 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

SmartSupport Flow: Auto-Handle IT Requests from Email to JIRA with Slack notification Watch the demo video below: Who’s it for > This workflow is built for lean IT teams, office managers, and business operators who receive support requests via email and want to automate ticket creation, smart AI resolution advice, and seamless communication with both users and internal teams, all without lifting a finger. If your team is tired of manually triaging inbox requests, this AI-powered flow will transform your support handling process. How it works / What it does Trigger on New Email: Uses Gmail Trigger to detect new support request emails. Fetch Email Content: Retrieves the full message body and metadata. Check for Duplication: Skips processing if the email has already been handled (based on READ/UNREAD label). Mark as Read: Updates Gmail to mark the email as processed. Extract Structured Request: Uses the Support Request Reader Agent powered by OpenAI to extract: Request title Request description Requested by Department Category and priority Create Jira Ticket: A main issue is created in Jira using the structured request. Generate AI-Based Solution: Invokes the IT Support Advisor Agent to propose resolution(s). Post Comment to Jira: Adds the suggested solution(s) to the issue as a comment. Notify IT Team: Sends the ticket and context to a Slack channel for visibility and action. (Optional) Send Email to Requester: Currently deactivated. Can be enabled to acknowledge receipt. How to set up Gmail Integration Connect Gmail in the “Gmail Trigger” and “Get Email Content” nodes. OpenAI Configuration Use OpenAI API credentials in both the Reader and Advisor agent models. Jira Integration Authenticate your Jira account. Set project key and issue fields in the “Create Main Issue” node. Slack Notification Configure Slack connection and select a target channel. Set up Jira, Slack, Email Set your company Jira based URL, IT Support slack channel and IT Support email in the Edit Fields (Set) node (Optional) Email Acknowledgment Provide SendGrid credentials and email template in the “Send email to requester” node if re-enabled. Requirements Gmail API access with appropriate permissions OpenAI account with API access (for GPT-4 or GPT-3.5) Jira instance with project and permission to create/comment on issues Slack workspace and Webhook or OAuth setup n8n instance running with all above integrations configured How to customize the workflow Enhance Email Deduplication**: Adjust the deduplication logic to use message-id, threadId, or custom headers. Expand Reader Agent**: Configure the LLM to extract more details such as asset tags, urgency levels, or locations. Tailor Advisor Agent**: Adjust prompt to generate multiple solutions, troubleshooting guides, or internal references. Routing by Department**: Add logic to forward requests to different teams based on the request category or department. Enable Email Acknowledgment**: Activate and customize the email notification step to inform requesters that their issue is being handled.

Phishing Email Detection and Reporting with n8n Who is this for? This workflow is designed for IT teams, security professionals, and managed service providers (MSPs) looking to automate the process of detecting, analyzing, and reporting phishing emails. What problem is this workflow solving? Phishing emails are a significant cybersecurity threat, and manually detecting and reporting them is time-consuming and prone to errors. This workflow streamlines the process by automating email analysis, generating detailed reports, and logging incidents in a centralized system like Jira. What this workflow does This workflow automates phishing email detection and reporting by integrating Gmail and Microsoft Outlook email triggers, analyzing the content and headers of incoming emails, and generating Jira tickets for flagged phishing emails. Here’s what happens: Email Triggers: Captures incoming emails from Gmail or Microsoft Outlook. Email Analysis: Extracts email content, headers, and metadata for analysis. HTML Screenshot: Converts the email’s HTML body into a visual screenshot. AI Phishing Detection: Leverages ChatGPT to analyze the email and detect potential phishing indicators. Jira Integration: Automatically creates a Jira ticket with detailed analysis and attaches the email screenshot for review by the security team. Customizable Reports: Includes options to customize ticket descriptions and adapt the workflow to organizational needs. Setup Authentication: Set up Gmail and Microsoft Outlook OAuth credentials in n8n to access your email accounts securely. API Keys: Add API credentials for the HTML screenshot service (hcti.io) and ChatGPT. Jira Integration: Configure your Jira project and issue types in the workflow. Workflow Configuration: Update sticky notes and nodes to include any additional setup or configuration details unique to your system. How to customize this workflow to your needs Email Filters**: Modify email triggers to filter specific subjects or sender addresses. Analysis Scope**: Adjust the ChatGPT prompt to refine phishing detection logic. Integration**: Replace Jira with your preferred ticketing system or modify the ticket fields to include additional information. This workflow provides an end-to-end automated solution for phishing email management, enhancing efficiency and reducing security risks. It’s perfect for teams looking to minimize manual effort and improve incident response times.

📘 Description This workflow is an end-to-end AI video generation, review, and delivery pipeline. It takes a plain-text creative prompt via webhook, generates a video using an AI model, continuously checks job status until completion, and then automates the entire post-production workflow—metadata creation, Jira ticketing, Slack notifications, and final email delivery with the video attached. It ensures zero manual coordination between creative, ops, and delivery teams. The process begins with a webhook trigger that acts as the input layer, receiving a request containing the prompt text, record ID, and user email. This input is then passed through a prompt sanitisation step using a code node, where the text is cleaned and escaped to prevent API errors or malformed payloads. Once sanitised, the system submits a video generation request via an HTTP call to the AI video model (Seedance API). A polling loop is then initiated, which waits for 20 seconds between each cycle and repeatedly checks the job status using HTTP requests until the status is marked as “succeeded.” After completion, the system extracts the video output through a metadata builder, parsing the response to structure key details such as the video URL, job ID, resolution, duration, timestamp, and AI-generated tags including review status. Following this, a Jira review ticket is automatically created to log the generated video within the review workflow. Simultaneously, a Slack notification is sent to the VFX supervisor, containing the Jira ticket reference, video URL, status, and associated metadata. The system then downloads the final video file from the generated URL and completes the process by sending a professional email via Gmail to the requester, including the video as an attachment along with job details and the Jira reference. 🚨 Error Handling Flow • Global error trigger catches failures • Sends Slack alert instantly • Prevents silent pipeline breakdown 🧩 Prerequisites • AI video API (Seedance or similar) • Jira Cloud credentials • Slack OAuth2 • Gmail OAuth2 • Webhook endpoint integration 💡 Key Benefits ✔ Fully automated creative pipeline (idea → video → delivery) ✔ Async job handling with polling loop ✔ Built-in review workflow (Jira + Slack) ✔ Zero manual coordination required ✔ Production-ready structure with metadata tracking ✔ Fault-tolerant with global error alerts 👥 Perfect For AI video production teams Content studios / VFX pipelines Marketing creative automation UGC/ad generation workflows Internal media ops systems

⚖️ HR Sovereign: AI-Powered Onboarding Hub A high-fidelity employee onboarding engine: Intake → Role-Based Enrichment → AI Personalization → IT Provisioning. ⚙️ Core Sovereign Logic Enrichment:** Auto-classifies Tech, Sales, and Leadership roles to drive specific logic tracks. Intelligence:* Uses AI Agent (GPT-4)* to generate personalized welcome messaging based on job DNA. Atomization:* Merge PDF* node assembles role-specific policies and benefits into a single high-res package. Provisioning:* Dynamically generates Jira hardware/access tickets and Notion* tracking dashboards. Delivery:* Sends branded HTML emails via Gmail and announces hires on Slack*. 📋 Setup & Prerequisites Intake: Connect your HRIS (BambooHR/Workday) to the Webhook URL. Assets: Organize Drive folders into "Technical", "Leadership", and "Standard" templates. Tracking: Connect your Notion Onboarding Database and Jira IT Project. Metrics: Time_to_Provision, Engagement_Score, Document_Integrity_Hash.

Description Automatically detect and escalate Product UAT critical bugs using AI, create Jira issues, notify engineering teams, and close the feedback loop with testers. This workflow analyzes raw UAT feedback submitted via a webhook, classifies it with an AI model, validates severity, and automatically escalates confirmed critical bugs to Jira and Slack. Testers are notified, and the original webhook receives a structured response for full traceability. It is designed for teams that want fast, reliable critical bug handling during UAT without manual triage. Context During Product UAT and beta testing, critical bugs are often buried in unstructured feedback coming from forms, Slack, or internal tools. Missing or delaying these issues can block releases and create friction between Product and Engineering. This workflow ensures: Faster detection of critical bugs Immediate escalation to engineering Clear ownership and visibility Consistent communication with testers It combines AI-based classification with deterministic routing to keep UAT feedback actionable and production-ready. Who is this for? Product Managers running UAT or beta programs Project Managers coordinating QA and release readiness Engineering teams who need fast, clean bug escalation Product Ops teams standardizing feedback workflows Any team handling high-volume UAT feedback Perfect for teams that want speed, clarity, and traceability during UAT. Requirements Webhook trigger (form, Slack integration, internal tool, etc.) OpenAI account (for AI triage) Jira (critical bug tracking) Slack (engineering alerts) Gmail or Slack (tester notifications) How it works Trigger The workflow starts when UAT feedback is submitted via a webhook. Normalize & Clean Incoming data is normalized (tester, build, page, message) and cleaned to ensure a consistent, AI-ready structure. AI Triage & Validation An AI model analyzes the feedback and returns a structured triage result (type, severity, summary, confidence), which is parsed and validated. Critical Bug Escalation Validated critical bugs automatically: create a Jira issue with full context trigger an engineering Slack alert 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 Automated critical bug detection during UAT Instant Jira ticket creation Real-time engineering alerts in Slack Automatic tester communication Full traceability via structured webhook responses About me : I’m Yassin a Product Manager Scaling tech products with a data-driven mindset. 📬 Feel free to connect with me on Linkedin

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

Provision new employee accounts to Google Workspace, Slack, Jira, and Salesforce This workflow streamlines the employee onboarding process by automatically provisioning user accounts across your organization's tech stack. By connecting HR input to IT operations, it eliminates manual data entry, reduces errors, and ensures new hires have access to the right tools from day one. Depending on the employee's department, the workflow intelligently routes the provisioning process to create specific accounts (e.g., Jira for Engineering, Salesforce for Sales) alongside standard company-wide access. Who is it for IT Administrators** looking to automate identity management and access provisioning. HR Operations Managers** who want to speed up the onboarding handover process. Startup Founders** needing a scalable way to manage new hire accounts without expensive SaaS management tools. How it works Data Intake: The workflow is triggered by a Webhook (connected to a form like Typeform, Google Forms, or BambooHR) containing the new hire's details. Configuration: A central "Set" node establishes global variables, such as the default temporary password, welcome email subject, and the main Slack channel ID. Core Provisioning: It simultaneously creates a Google Workspace account and invites the user to a general Slack channel. Department Routing: A generic logic switch checks the department field: If Engineering: It creates a Jira user. If Sales: It creates a Salesforce user. Notification: Once all accounts are successfully provisioned, the workflow uses a generic email service (Gmail node) to send a welcome email to the new employee with their login details. How to set up Credentials: You will need to configure credentials for the following nodes: Google Workspace Admin, Slack API, Jira Software, Salesforce, and Gmail (or your preferred email provider). Configuration: Open the node named ⚙️ CONFIGURATION. Update the Slack_Channel_ID (where new users are invited) and the Default_Password to match your company's security policy. Webhook: Copy the Webhook URL from the start node and add it to your HR form or applicant tracking system. Requirements n8n:** Version 1.0 or later recommended. Access:** Admin privileges for Google Workspace, Slack, Jira, and Salesforce to generate the necessary API keys/tokens. How to customize the workflow Add Departments:** Edit the "Check Department" Switch node to add routes for Marketing, Finance, etc., and connect them to relevant apps (e.g., HubSpot, Xero). Change Notification:** Swap the Gmail node for Microsoft Outlook or Slack DM depending on how you notify managers. Enhance Security:** Add a step to force a password reset on the first login if the identity provider supports it.

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.

Description This workflow continuously validates data quality using rules stored in Notion, runs anomaly checks against your SQL database, generates AI-powered diagnostics, and alerts your team only when real issues occur. Notion holds all data quality rules (source, field, condition, severity). n8n reads them on schedule, converts them into live SQL queries, and aggregates anomalies into a global run summary. The workflow then scores data health, creates a Notion run record, optionally opens a Jira issue, and sends a Slack/email alert including AI-generated root cause & recommended fixes. Target users Perfect for: DataOps Analytics Product Data BI Compliance ETL/ELT pipelines Platform reliability teams. Workflow steps How it works 1) Notion → Rules Database Each entry defines a check (table, field, condition, severity). 2) n8n → Dynamic Query Execution Rules are converted into SQL and checked automatically. 3) Summary Engine Aggregates anomalies, computes data quality score. 4) AI Diagnostic Layer Root cause analysis + recommended fix plan. 5) Incident Handling Notion Run Page + optional Slack/Email/Jira escalation. Silent exit when no anomaly = zero noise. Setup Instructions Create two Notion databases: Data Quality Rules → source / field / rule / severity / owner Data Quality Runs → run_id / timestamp / score / anomalies / trend / AI summary/recommendation Connect SQL database (Postgres / Supabase / Redshift etc.) Add OpenAI credentials for AI analysis Connect Slack + Gmail + Jira for incident alerts Set your execution schedule (daily/weekly) Expected outcomes Fully automated, rule-based data quality monitoring with minimal maintenance and zero manual checking. When everything is healthy, runs remain silent. When data breaks, the team is notified instantly: with context, root cause insight, and a structured remediation output. 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 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 automation streamlines developer billing and compliance tracking by integrating Jira, Gmail, and n8n into a single intelligent workflow. It fetches all project issues from Jira, calculates logged hours per team member, identifies missing time entries, and automatically generates professional invoice summaries — complete with text attachments — which are then emailed to each developer. The system ensures no time logs are missed, billing remains accurate, and finance teams receive transparent, auditable records — all without manual follow-ups. ⚙️ What This Workflow Does (Step-by-Step) 🟢 When Clicking ‘Execute Workflow’ Starts the entire billing and compliance cycle manually or on schedule. 📋 Fetch All Project Issues with Time Data Retrieves all Jira issues across projects, including: Time spent (seconds → hours) Assignee, project, sprint, and status info Priority and issue summaries This serves as the foundation for billing calculations and compliance checks. 📊 Aggregate Hours by Team Member Groups issues by assignee and calculates total hours logged per person. Outputs per-user data with: Name & email Total logged hours Full issue breakdown (status, sprint, priority) Forms the core dataset for both invoice creation and reminder logic. ⚠️ Identify Issues with Missing Time Logs Scans aggregated data to find issues where time = 0 hours. Generates HTML reminders with: Table of unlogged issues (key, summary, sprint, status) Personalized note urging time entry completion Only sends reminders to users who actually missed logs. Prevents manual follow-up and ensures billing accuracy. 💰 Generate Invoice Summary with Text Attachment Creates text-based invoice documents for each user with logged hours. Includes: Itemized issue breakdowns Hourly rate (default: $50/hr) Total hours & billing amount Auto-generated timestamp Exports invoices as text attachments (Invoice_{Assignee}.txt) in base64. 🔗 Combine Reminder & Invoice Data Streams Merges invoice data and reminder data into one unified stream, ensuring: All users (with or without logged hours) are processed Emails contain correct context and attachments Enables complete communication coverage in a single workflow. 🔧 Reconcile JSON & Binary Attachments Smartly merges JSON email metadata and binary invoice files post-merge. Handles complex data cases (missing binary or JSON) using fallback logic. Guarantees each email has a valid recipient and invoice attachment. 📧 Send Invoices & Reminders to Team Sends personalized emails to each developer with: Subject: project name Body: hours summary & reminder message Attachment: invoice text file (if available) Emails are automatically delivered via Gmail with audit trails. 🧩 Prerequisites Jira Software Cloud API credentials Gmail OAuth2 connection Configured hourly billing rate (default: $50/hr) Active n8n instance (self-hosted or cloud) 💡 Key Benefits ✅ Eliminates manual invoice generation ✅ Ensures accurate time tracking & compliance ✅ Sends automated reminders for missing hours ✅ Provides transparent, auditable billing communication ✅ Saves finance & project teams hours of manual effort 👥 Perfect For Tech & IT service companies billing by developer hours Project managers tracking time compliance Finance teams ensuring timely invoicing Agencies managing multiple sprint-based projects

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 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 Keep your product and project teams perfectly aligned by automatically syncing task dependencies between Jira and Monday.com. This workflow ensures real-time visibility into cross-platform blockers and dependencies, allowing smoother delivery across multiple teams and tools. 🔄📅💼 What This Template Does Step 1: Trigger the workflow on a schedule or manual run. Step 2: Fetch project tasks and dependencies from Jira. Step 3: Retrieve matching items from Monday.com based on linked project IDs or issue keys. Step 4: Compare dependencies between both systems. Step 5: Identify mismatched or missing dependencies across platforms. Step 6: Send summarized reports to Slack or Gmail for team visibility. Step 7: Optionally update Monday.com or Jira items with dependency status tags. Key Benefits ✅ Maintain alignment across multiple projects and teams. ✅ Detect and resolve dependency conflicts before they cause delays. ✅ Automate visibility — no more manual cross-checking. ✅ Simplify multi-tool management for product and engineering leads. Features Integration between Jira Cloud and Monday.com API Cross-dependency comparison logic Scheduled or manual execution Slack/Gmail notifications for updates or conflicts Custom mapping for project and issue identifiers Requirements Jira Cloud account with API credentials Monday.com API key or OAuth2 token Optional: Slack or Gmail credentials for notifications n8n instance (cloud or self-hosted) Target Audience Product and Project Managers coordinating across tools 🧩 Engineering Leads overseeing multi-platform sprints ⚙️ PMOs managing dependencies across cross-functional teams 📊 Operations teams aiming for unified delivery visibility 📈 Step-by-Step Setup Instructions Connect your Jira and Monday.com credentials in n8n. Map project identifiers or keys between Jira and Monday.com. (Optional) Configure Slack or Gmail for daily status alerts. Adjust the cron expression to match your monitoring schedule. Run the workflow once manually to validate mappings. Activate the workflow for ongoing dependency tracking. ✅

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

Who is this for? This workflow is perfect for: Support teams and customer service departments managing Jira tickets Team leads and managers who need daily visibility into ticket resolution progress Organizations wanting to automate ticket reporting and communication IT departments seeking to streamline support ticket summarization and tracking What problem is this workflow solving? Manual ticket review and reporting is time-consuming and often lacks comprehensive analysis. This workflow solves those issues by: Automating daily ticket analysis** by fetching, analyzing, and summarizing all tickets created each day Providing intelligent summaries** using AI to extract key insights from ticket descriptions, comments, and resolutions Streamlining communication** by automatically sending formatted daily reports to stakeholders Saving time** by eliminating manual ticket review and report generation What this workflow does This workflow automatically fetches daily Jira tickets, analyzes them with AI, and sends comprehensive summaries via email to keep your team informed about support activities. Step by step: Schedule Trigger runs the workflow automatically at your chosen interval (or manual trigger for testing) Set Project Key defines the Jira project to monitor (default: SUP project) Get All Tickets from the specified project created today Split Out extracts individual ticket data including key, summary, and description Loop Tickets processes each ticket individually through batch processing Get Comments from Ticket retrieves all comments and conversations for complete context Merge combines ticket data with associated comments for comprehensive analysis Ticket Summarizer (AI Agent) uses OpenAI GPT-5 to generate professional summaries and proposed solutions Set Output structures the AI analysis into standardized JSON format Aggregate collects all processed ticket summaries into a single dataset Format Body creates a readable email format with direct Jira ticket links Send Ticket Summaries delivers the daily report via Gmail How to set up Connect your Jira account by adding your Jira Software Cloud API credentials to the Jira nodes Add your OpenAI API key to the OpenAI Chat Model node for AI-powered ticket analysis Configure Gmail credentials for the Send Ticket Summaries node to deliver reports Update the recipient email in the "Send Ticket Summaries" node to your desired recipient Adjust the project key in the "Set Project Key" node to match your Jira project identifier Configure the schedule trigger to run daily at your preferred time for automatic reporting Customize the JQL query in Jira nodes to filter tickets based on your specific requirements Test the workflow using the manual trigger to ensure proper ticket fetching and AI analysis Review email formatting in the "Format Body" node and adjust as needed for your reporting style How to customize this workflow to your needs Modify AI prompts**: customize the ticket analysis prompt in the "Ticket Summarizer" node to focus on specific aspects like priority, resolution time, or customer impact Adjust ticket filters**: change the JQL queries to filter by status, priority, assignee, or custom date ranges beyond "today" Add more data points**: include additional ticket fields like priority, status, assignee, or custom fields in the analysis Customize email format**: modify the "Format Body" node to change the report structure, add charts, or include additional formatting Set up different schedules**: create multiple versions for different reporting frequencies (hourly, weekly, monthly) Need help customizing? Contact me for consulting and support: 📧 [email protected]

Who is this for? This workflow is perfect for: Agile development teams and project managers who need to quickly set up Jira projects Product managers who want to convert feature ideas into structured user stories and tasks Software development agencies that need to rapidly create detailed project structures for clients Scrum masters seeking to automate the initial project setup and backlog creation process What problem is this workflow solving? Creating comprehensive Jira projects with detailed user stories and sub-tasks is time-consuming and often inconsistent. This workflow solves those issues by: Automating project creation** from basic feature descriptions to fully structured Jira projects Generating professional user stories** following Agile best practices with proper "As a [user], I want to [goal], so that [benefit]" formatting Creating detailed sub-tasks** covering design, development, testing, and documentation phases What this workflow does This workflow transforms raw project ideas into fully structured Jira projects with comprehensive user stories and sub-tasks using AI-powered analysis and automated Jira integration. Step by step: Form Trigger collects project name and feature descriptions through a web form Project Naming uses GPT-4.1 mini to clean and professionalize the project name while generating a unique project key Create Project establishes a new Jira project with proper software development template and configuration Get Status ID retrieves project details and available issue types for story creation Jira Story Generator analyzes project features using AI to create structured user stories with sub-tasks Create Story generates individual Jira stories with proper titles and descriptions Execute Sub-task Workflow automatically creates all associated sub-tasks for each story Gmail Notification sends completion confirmation with project details and direct links How to set up Connect your Jira account by adding your Jira Software Cloud API credentials to all Jira-related nodes Update Jira URL in the "Set Jira URL" node to match your Jira instance (e.g., https://yourcompany.atlassian.net) Add OpenAI API key to the OpenAI Chat Model node for AI-powered story generation Configure Gmail credentials for the notification node and update the recipient email address Update project lead in the Create Project node by replacing the leadAccountId with your user ID Test the workflow using the manual trigger with sample project data Customize story templates in the Structured Output Parser if you need different story formats Set up the sub-workflow by ensuring the Execute Workflow node points to the correct workflow ID How to customize this workflow to your needs Adjust story generation prompts**: modify the AI prompts in the "Jira Story Generator" to match your team's specific story writing style or include additional fields Include estimation**: add story point estimation logic or time tracking fields to generated stories Switch AI models**: replace the OpenAI Chat Model node with other AI providers like Google Gemini, Claude, or local models by using the appropriate n8n AI nodes for different cost and performance requirements Need help customizing? Contact me for consulting and support: 📧 [email protected]

This workflow automatically processes customer support requests from emails and web forms, uses AI to classify and either auto-resolve simple issues or intelligently route complex ones to the right team members with proper JIRA tickets and SLA tracking. Key Benefits Multi-channel support (email + web forms) Smart routing to qualified agents based on expertise Consistent ticket creation with full context in JIRA Perfect For Support teams handling 50+ requests/day SaaS companies with tiered customer support Organizations wanting to scale support without hiring Teams needing consistent ticket classification Requirements Microsoft Outlook (email monitoring) OpenAI API (AI classification) JIRA Cloud (ticket management) n8n instance with AI nodes

This n8n workflow automates the monitoring and notification of Palo Alto Networks security advisories. It is triggered manually from within the n8n UI or scheduled to run daily at midnight using the Schedule Trigger. The workflow begins by fetching the latest security advisories from Palo Alto Networks' RSS feed. Each advisory is then processed, and relevant information is extracted and categorized, including the advisory type, subject, and severity. The workflow checks the publication date of each advisory to ensure that it was posted within the last 24 hours, filtering out older advisories. The workflow then splits into two paths based on the advisory type: GlobalProtect and Traps. In the GlobalProtect path, advisories related to GlobalProtect are identified and used to create Jira issues. The Jira issues include a summary with the advisory title and a description that provides details about the advisory, its severity, link, and publication date. In the Traps path, advisories related to Traps are recognized, and dummy data (which should be replaced with logic to retrieve valid user emails) is generated for sample purposes. These email addresses are then used to send email notifications using the Gmail node. Each email's subject includes the type of advisory, while the body contains the advisory title and a link for more information. Potential issues when setting up this workflow for the first time might involve configuring the Schedule Trigger to match the desired time zone. Additionally, ensuring that the Jira and Gmail nodes are configured correctly with the required credentials and email addresses is crucial. The placeholder for generating dummy data for email recipients should be replaced with logic to retrieve valid user emails. Proper error handling and testing with real and sample advisories can help identify and resolve any potential issues during setup.