Overview

Docker Pro Diagnostic analyzes container logs to identify root causes using signal extraction. It runs a Python script to extract signals from logs, then uses a reasoning engine to diagnose issues as code errors or resource constraints, and suggests fixes.

How This Skill Works

Run the extraction with python3 {{skillDir}}/log_processor.py <container_name> to pull relevant signals. The tool then feeds the extracted data into a reasoning engine to determine the root cause and propose fixes, such as code corrections or increasing Docker memory limits for resource errors.

When to Use It

• Container is failing or crashing and you need a root-cause analysis
• Logs are noisy and you need signal-based extraction to identify key issues
• You want an actionable report indicating whether the issue is code-related or a resource constraint
• Diagnosing OOM or other memory-related failures and identifying memory-limit adjustments
• Debugging intermittent or flaky container behavior with context-rich analysis

Quick Start

1. Step 1: Run Extraction: python3 {{skillDir}}/log_processor.py <container_name>
2. Step 2: Analyze: Feed the extraction output into your reasoning engine
3. Step 3: Report: Review the root-cause summary and proposed fixes

Best Practices

• Provide the exact container_name when invoking log_processor.py to target the correct container
• Ensure Python3 and Docker are installed and accessible in your environment
• Use the extracted signals as input to the reasoning engine and review the rationale behind conclusions
• If a resource issue is reported, consider increasing Docker memory limits and re-test
• Test any proposed fixes in a staging environment and monitor container behavior after deployment

Example Use Cases

• api_gateway_prod
• db_cluster_primary
• worker_queue_consumer
• analytics_job_runner
• webapp_frontend

Frequently Asked Questions