Diagnose Pipeline Logs

📘 Tool Description & How To Use The Diagnosis Engine

🧠 System Overview

This Diagnosis Engine is an offline, client-side diagnostic system built around the Pattern-Based Root Cause Isolation Model (PBRCIM). It analyzes log patterns and compares expected execution paths with observed failures to help identify likely root causes. The engine runs entirely in your browser, with no server, AI API, or internet connection required. All analysis is performed locally using structured detection rules.

🎯 What the Engine Does

Built for Site Reliability Engineers (SREs), DevOps engineers, and system architects, this engine helps investigate failures that are difficult to isolate from stack traces and noisy logs alone.

Automated Post-Mortem Generation: Incident reports can require significant time to reconstruct from logs. The engine can turn diagnostic findings into a structured, industry-standard post-mortems in milliseconds.

Every generated report includes:

• Execution path and likely root cause
• Corrective actions and prevention guidance
• Verification checklist

Ready to be copied directly into your incident tickets or documentation.

🔍 Supported Failure Domains

• Infrastructure & Orchestration: Docker daemon faults, Kubernetes pod stalls, container OOMs.
• Runtime & Memory: Node.js V8 heap exhaustion, Python AttributeError/NoneType exceptions.
• Build & Compilation: Webpack resolution blocks, TypeScript compilation, asset pipeline halts.
• Network & Services: EADDRINUSE conflicts, gateway timeouts, database pool exhaustion.
• CI/CD & Version Control: Pipeline permissions, runner crashes, Git metadata corruption.

🔧 How to Use This Engine

1. Open your DevOps platform or CI/CD system (e.g., GitHub Actions, GitLab CI, Jenkins, other).
2. Locate the failed job or pipeline run you want to diagnose.
3. Copy the full error log or console output from the terminal, dashboard, or log viewer.
4. Return here and paste the log into the text area labeled “Paste your log here…”.
5. Click the 🔍 Diagnose button.
6. The engine scans and analyzes the pasted content locally.
7. Review the structured diagnosis that appears in the result area above the action buttons, including possible causes and corrective suggestions.
8. Use the 📋 Copy or 💾 Download buttons to export the diagnosis, or 🧹 Clear to start over.

💡 Tip: You can paste logs from Docker, Git, Yarn, Webpack, or any other development tool. The engine checks for known failure patterns and returns a clear explanation of the likely cause and possible next steps.

🧪 How It Works (PBRCIM in Practice)

1. Pattern Scanning: Logs are scanned using layered detection rules.
2. Intent Comparison: Expected execution flow is compared against observed behavior.
3. Mismatch Isolation: Divergence points are identified even when no explicit error is thrown.
4. Multiple Findings: A single log can contain more than one fault signal.
5. Diagnostic Output: Findings are presented with likely causes and suggested corrective actions.

This is how PBRCIM is applied in the engine: expected and observed behavior are compared, patterns are checked, and the results are used to identify likely failure points.