{
  "workflow": {
    "id": 9835,
    "name": "Dependency update risk analysis with GPT-4o, Slack, Jira & Google Sheets",
    "views": 125,
    "recentViews": 0,
    "totalViews": 125,
    "createdAt": "2025-10-17T12:35:12.215Z",
    "description": "## 📘 Description\nThis workflow automates dependency update risk analysis and reporting using Jira, GPT-4o, Slack, and Google Sheets.\nIt 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.\nThis ensures fast, data-driven decisions for dependency upgrades, improved code stability, and reduced security risks — with zero manual triage.\n\n## ⚙️ What This Workflow Does (Step-by-Step)\n\n🟢 When Clicking “Execute Workflow”\nManually triggers the dependency risk analysis sequence for immediate review or scheduled monitoring.\n\n📋 Fetch All Active Jira Issues\nRetrieves all active Jira issues to identify tickets related to dependency or package updates.\n Provides the complete dataset — including summary, status, and assignee information — for AI-based risk evaluation.\n\n✅ Validate Jira Query Response\nVerifies that Jira returned valid issue data before proceeding.\n If data exists → continues filtering dependency updates.\n If no data or API error → logs the failure to Google Sheets.\n Prevents workflow from continuing with empty or broken datasets.\n\n🔍 Identify Dependency Update Issues\nFilters Jira issues to find only dependency-related tickets (keywords like “update,” “bump,” “package,” or “library”).\n This ensures only relevant version update tasks are analyzed — filtering out unrelated feature or bug tickets.\n\n🏷️ Extract Relevant Issue Metadata\nExtracts essential fields such as key, summary, priority, assignee, status, and created date for downstream AI processing.\n Simplifies the data payload and ensures accurate, structured analysis.\n\n📢 Alert DevOps Team in Slack\nImmediately notifies the assigned DevOps engineer via Slack DM about any new dependency update issue.\n Includes formatted details like summary, key, status, priority, and direct Jira link for quick access.\n Ensures rapid visibility and faster response to potential risk tickets.\n\n🤖 AI-Powered Risk Assessment Analyzer\nUses GPT-4o (Azure OpenAI) to intelligently evaluate each dependency update’s risk level and impact summary.\n Considers factors such as:\n- Dependency criticality\n- Version change type (major/minor/patch)\n- Security or EOL indicators\n- Potential breaking changes\n \nOutputs a clean JSON with fields:\n\n{\"risk_level\": \"Low | Medium | High\",\"impact_summary\": \"Short human-readable explanation\"}\nHelps DevOps teams prioritize updates with context.\n\n🧠 GPT-4o Language Model Configuration\nConfigures the AI reasoning engine for precise, context-aware DevOps assessments.\n Optimized for consistent technical tone and cost-efficient batch evaluation.\n\n📊 Parse AI Response to Structured Data\nSafely parses the AI’s JSON output, removing markdown artifacts and ensuring structure.\n Adds parsed fields — risk_level and impact_summary — back to the Jira context.\n Includes fail-safes to prevent crashes on malformed AI output (fallbacks to “Unknown” and “Failed to parse”).\n\n💬 Post AI Risk Assessment to Jira Ticket\nAutomatically posts the AI’s analysis as a comment on the Jira issue:\n- Displays 🤖 AI Risk Assessment Report header\n- Shows Risk Level and Impact Summary\n- Includes a checklist of next steps for developers\n-  Creates a permanent audit trail for each dependency decision inside Jira.\n\n📈 Log Dependency Updates to Tracking Dashboard\nAppends all analyzed updates into Google Sheets, recording:\n- Date\n- Jira Key & Summary\n- Risk Level & Impact Summary\n- Assignee & Status\nThis builds a historical dependency risk database that supports:\n- Trend monitoring\n- Security compliance reviews\n- Dependency upgrade metrics\n- DevOps productivity tracking\n\n📊 Log Jira Query Failures to Error Sheet\nIf the Jira query fails, the workflow automatically logs the error (API/auth/network) into a centralized error sheet for troubleshooting and visibility.\n\n## 🧩 Prerequisites\nJira Software Cloud API credentials\nAzure OpenAI (GPT-4o) access\nSlack API connection\nGoogle Sheets OAuth2 credentials\n\n## 💡 Key Benefits\n ✅ Automated dependency risk assessment\n ✅ Instant Slack alerts for update visibility\n ✅ Historical tracking in Google Sheets\n ✅ Reduced manual triage and faster decision-making\n ✅ Continuous improvement in release reliability and security\n\n## 👥 Perfect For\n- DevOps and SRE teams managing large dependency graphs\n- Engineering managers monitoring package updates and risks\n- Security/compliance teams tracking vulnerability fix adoption\n- Product teams aiming for stable CI/CD pipelines",
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      "name": "Automated Dependency Update Tracking",
      "tags": [],
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          "name": "When clicking ‘Execute workflow’",
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          "parameters": {
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            "height": 384,
            "content": "## 🧩 Parse AI Response to Structured Data\n**Action:**  \n- Converts AI-generated output into structured JSON fields.  \n**Description:**  \n- Extracts risk_level and impact_summary.  \n- Removes markdown artifacts and trims whitespace.  \n- Provides fallback values if parsing fails (“Unknown” risk, “Failed to parse AI output”).  \n- Ensures safe and consistent payload for Jira comments and Google Sheets logging.\n"
          },
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            "content": "## ⚙️ GPT-4o Model Configuration\n**Action:**  \n- Configures AI model for accurate risk analysis.  \n**Description:**  \n- Defines model parameters like temperature, max tokens, and system prompt.  \n- Ensures consistent, repeatable results in dependency analysis.  \n- Focuses on DevOps context including package criticality and versioning.  \n- Optimized for speed, reliability, and cost efficiency.\n"
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            "content": "## 🧠 AI-Powered Risk Assessment Analyzer\n**Action:**  \n- Uses GPT-4o to assess risk of dependency updates.  \n**Description:**  \n- Evaluates risk level (Low/Medium/High) and impact summary.  \n- Considers dependency type, version changes, security indicators, and breaking change potential.  \n- Provides structured JSON for downstream nodes.  \n- Reduces manual triage and ensures consistent assessment across issues.\n"
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            "content": "## 🔵 Alert DevOps Team in Slack\n**Action:**  \n- Sends a Slack notification about new dependency updates.  \n**Description:**  \n- Message includes issue key, summary, status, priority, assignee, and Jira link.  \n- Notifies DevOps and security teams immediately.  \n- Provides formatted, actionable information for rapid response.  \n- Creates audit trail of notifications for team transparency.\n"
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            "content": "## 💬 Post AI Risk Assessment to Jira Ticket\n**Action:**  \n- Adds AI-generated risk assessment as a Jira comment.  \n**Description:**  \n- Includes risk level, impact summary, and next steps checklist.  \n- Provides permanent record for audit and compliance.  \n- Allows developers to see AI assessment without leaving Jira.  \n- Promotes standardized update validation and quick decision-making.\n"
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            "content": "## 🟠 Extract Relevant Issue Metadata\n**Action:**  \n- Extracts key fields from Jira issues for downstream processing.  \n**Description:**  \n- Keeps only essential fields: key, summary, assignee, priority, status, URL, and issue links.  \n- Removes unnecessary metadata to reduce payload size.  \n- Prepares structured JSON for Slack notifications and AI risk assessment.  \n- Ensures consistency across all downstream nodes.\n"
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            "content": "## 🔴 Identify Dependency Update Issues\n**Action:**  \n- Filters Jira issues to isolate dependency/package updates.  \n**Description:**  \n- Searches summaries and descriptions for keywords like “update,” “bump,” “dependency,” “package,” or “library.”  \n- Case-insensitive matching for maximum coverage.  \n- **Matched Path:** Send to AI Risk Analysis.  \n- **Unmatched Path:** Skip for efficiency.  \n- Reduces noise and focuses AI analysis on relevant security/dependency issues.\n"
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            "content": "## ✅ Validate Jira Query Response\n**Action:**  \n- Verifies that Jira successfully returned issue data.  \n**Description:**  \n- Checks if response length > 0 before continuing.  \n- **True Path (Data Exists):** Proceeds to filter dependency-related issues.  \n- **False Path (No Data/Error):** Routes to error logging.  \n- Prevents downstream processing errors from empty datasets.  \n- Catches Jira API failures, authentication issues, or permission problems early.  \n- Critical quality gate that ensures workflow reliability and proper error handling.\n"
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            "content": "## 🟡 Log Jira Query Failures to Error Sheet\n**Action:**  \n- Records Jira API failures or empty responses into a Google Sheet.  \n**Description:**  \n- Captures error type, message, and timestamp for debugging.  \n- Supports identification of recurring API or authentication issues.  \n- Separates errors from main workflow to maintain process continuity.  \n- Helps troubleshoot integration problems effectively.\n"
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            "content": "## 🟣 Fetch All Active Jira Issues\n**Action:**  \n- Retrieves all project issues from Jira for processing.  \n**Description:**  \n- Connects via Jira API using OAuth or credentials.  \n- Pulls keys, summaries, statuses, priorities, assignees, and metadata.  \n- Supports pagination or “returnAll: true” for a complete dataset.  \n- Forms the base dataset for further analysis.\n"
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            "content": "## 📈 Log Dependency Updates to Tracking Dashboard\n**Action:**  \n- Saves all processed dependency updates into a Google Sheet for tracking.  \n**Description:**  \n- Logs key, summary, assignee, status, risk level, impact summary, and timestamp.  \n- Enables historical trend analysis and reporting.  \n- Supports dashboards in Looker Studio, Tableau, or Notion.  \n- Provides data for compliance audits, risk distribution, and DevOps performance metrics.\n"
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            "text": "=You are Techdome’s AI DevOps assistant.\nAnalyze this Jira issue update and assign a risk level (Low, Medium, High) with a short reasoning.\n\nHere’s the issue data in JSON:\n{{ JSON.stringify($json) }}\n\nReturn the response only in this JSON format:\n{\n  \"risk_level\": \"\",\n  \"impact_summary\": \"\"\n}\n",
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              "name": "azureOpenAiApi Credential"
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            "comment": "=🤖 *AI Risk Assessment Report*  *Risk Level:* {{$json[\"risk_level\"]}}   *Impact Summary:* {{$json[\"impact_summary\"]}}  ✅ *Next Steps:*   - [ ] Review dependency changelog   - [ ] Run regression tests   - [ ] Verify compatibility with core modules   - [ ] QA sign-off before merge",
            "options": {},
            "issueKey": "={{ $('Extract Relevant Issue Metadata').item.json.key }}",
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        },
        "displayName": "Code",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 5,
            "name": "Development"
          },
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 838,
        "icon": "fa:mouse-pointer",
        "name": "n8n-nodes-base.manualTrigger",
        "codex": {
          "data": {
            "resources": {
              "generic": [],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.manualworkflowtrigger/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0"
          }
        },
        "group": "[\"trigger\"]",
        "defaults": {
          "name": "When clicking ‘Execute workflow’",
          "color": "#909298"
        },
        "iconData": {
          "icon": "mouse-pointer",
          "type": "icon"
        },
        "displayName": "Manual Trigger",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 844,
        "icon": "fa:filter",
        "name": "n8n-nodes-base.filter",
        "codex": {
          "data": {
            "alias": [
              "Router",
              "Filter",
              "Condition",
              "Logic",
              "Boolean",
              "Branch"
            ],
            "details": "The Filter node can be used to filter items based on a condition. If the condition is met, the item will be passed on to the next node. If the condition is not met, the item will be omitted. Conditions can be combined together by AND(meet all conditions), or OR(meet at least one condition).",
            "resources": {
              "generic": [],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.filter/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Flow",
                "Data Transformation"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Filter",
          "color": "#229eff"
        },
        "iconData": {
          "icon": "filter",
          "type": "icon"
        },
        "displayName": "Filter",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 1119,
        "icon": "fa:robot",
        "name": "@n8n/n8n-nodes-langchain.agent",
        "codex": {
          "data": {
            "alias": [
              "LangChain",
              "Chat",
              "Conversational",
              "Plan and Execute",
              "ReAct",
              "Tools"
            ],
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Agents",
                "Root Nodes"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "AI Agent",
          "color": "#404040"
        },
        "iconData": {
          "icon": "robot",
          "type": "icon"
        },
        "displayName": "AI Agent",
        "typeVersion": 3,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1253,
        "icon": "file:azure.svg",
        "name": "@n8n/n8n-nodes-langchain.lmChatAzureOpenAi",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatazureopenai/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Language Models",
                "Root Nodes"
              ],
              "Language Models": [
                "Chat Models (Recommended)"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Azure OpenAI Chat Model"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "Azure OpenAI Chat Model",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      }
    ],
    "categories": [
      {
        "id": 16,
        "name": "DevOps"
      },
      {
        "id": 49,
        "name": "AI Summarization"
      }
    ],
    "image": []
  }
}