What is the primary output of this skill?

It generates a structured Log Analysis Summary including error statistics, critical findings with timestamps, and a list of specific files for further manual investigation.

Does it support compressed log files?

Yes, it includes implementation steps for using zgrep to analyze rotated and compressed logs (e.g., .gz files) typically found in /var/log.

Can this skill analyze binary journald logs?

No, it specifically targets the text-based exports found within the 'sos_commands' directory of a standard sosreport to ensure compatibility and speed.

What types of critical errors can this skill detect?

The skill is optimized to find kernel panics, OOM (Out of Memory) killer events, segmentation faults, service failures, and application stack traces.

How does it handle extremely large log files?

For files exceeding 100MB, the skill employs sampling techniques using head and tail commands to prevent memory exhaustion while still capturing recent and initial errors.

Sosreport Logs Analysis

Name: Sosreport Logs Analysis
Author: openshift-eng

byopenshift-eng

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Analíticas y Monitorización

Analyzes system and application log data from sosreport archives to identify root causes of system failures, crashes, and performance issues.

Acerca de

This skill empowers Claude to perform deep-dive forensics on Linux sosreport archives by systematically scanning journald, dmesg, and application-specific logs. It automates the detection of critical system events such as kernel panics, OOM killer actions, and service failures, providing structured summaries that correlate error patterns with specific timestamps and components. It is an essential tool for SREs and system administrators who need to rapidly diagnose infrastructure issues by extracting actionable insights from massive log volumes.

Características Principales

Error frequency categorization and chronological timeline generation
Automated detection of kernel panics, OOM killer events, and segfaults
Application-specific analysis for Java, Python, and web server logs
Smart handling of large or compressed log files using sampling and zgrep
Comprehensive parsing of both journald and traditional var/log files
29 GitHub stars

Casos de Uso

Troubleshooting failed systemd services and inter-service dependency issues
Diagnosing sudden system reboots or freezes using kernel message analysis
Identifying memory-hungry processes through OOM killer forensic data

Acerca de

Características Principales

Error frequency categorization and chronological timeline generation
Automated detection of kernel panics, OOM killer events, and segfaults
Application-specific analysis for Java, Python, and web server logs
Smart handling of large or compressed log files using sampling and zgrep
Comprehensive parsing of both journald and traditional var/log files
29 GitHub stars

Casos de Uso

Troubleshooting failed systemd services and inter-service dependency issues
Diagnosing sudden system reboots or freezes using kernel message analysis
Identifying memory-hungry processes through OOM killer forensic data