Block 1 - When clicking ‘Execute workflow’
- Type / Role
- n8n-nodes-base.manualTrigger - manualTrigger
- Config choices
- Version 1
This workflow is provided as-is. Please review and test before using in production.
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...
n8n-nodes-base.manualtrigger, n8n-nodes-base.stickynote, n8n-nodes-base.jira, n8n-nodes-base.if, n8n-nodes-base.googlesheets, n8n-nodes-base.filter, n8n-nodes-base.set, n8n-nodes-base.slack
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Rahul Joshi.
Original n8n.io sourceThis 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.
🟢 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:
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:
📈 Log Dependency Updates to Tracking Dashboard Appends all analyzed updates into Google Sheets, recording:
📊 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.
Jira Software Cloud API credentials Azure OpenAI (GPT-4o) access Slack API connection Google Sheets OAuth2 credentials
✅ 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
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
| Workflow | Dependency update risk analysis with GPT-4o, Slack, Jira & Google Sheets |
|---|---|
| Complexity | advanced |
| Nodes | 23 |
| Categories | DevOps, AI Summarization |
| Author | Rahul Joshi |
| Published | 17 Oct 2025 |
Use the JSON export at /data/workflows/9835/9835.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
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...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your DevOps, AI Summarization use case.