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polaris-dxz
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log-analyzer

Analyze and correlate multi-source logs to detect anomalies and trends.

1|1|Updated Mar 22, 2026
Search Tags:#monitoring#logs#alerting#anomaly-detection#log-analysis#pattern-recognition
One-click install
npx skills add https://github.com/polaris-dxz/xclaw --skill log-analyzer-polaris-dxz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: log-analyzer
Source: https://github.com/polaris-dxz/xclaw/tree/main/apps/desktop/openclaw-runtime/config/skills/log-analyzer
Command: npx skills add https://github.com/polaris-dxz/xclaw --skill log-analyzer-polaris-dxz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

## What problem does it solve?

Analyzes and correlates multi-source logs to surface anomalies, trends, and root-causes, reducing mean time to detect and respond to incidents.

## Core Features & Use Cases

  • Multi-source log aggregation across application, system, and network logs.
  • Pattern recognition for anomaly frequency, error clustering, and trend detection.
  • Configurable alerts and notification channels to trigger rapid responses.

### Quick Start

Run the log_analyzer_tool.py analyze with your log file to begin analysis.

Frequently Asked Questions about log-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze multi-source logs to identify anomalies and trends?▼

Multi-source log analysis correlates application, system, and network logs to detect anomalies and trends. This requires Python with pandas and regex to identify abnormal frequencies and cluster errors.

Can I trigger alerts from application and system log pattern recognition?▼

Yes, pattern recognition in application and system logs detects abnormal frequencies and clusters errors to trigger alerts. Configurable notifications enable rapid incident response.

Do I need Python and pandas to run log anomaly detection?▼

Yes, log anomaly detection requires Python with pandas and regex libraries. The CLI interface uses these dependencies to process logs and stores execution records for traceability.

What's the best way to detect error clusters across network and system logs?▼

Detecting error clusters across network and system logs is best handled by aggregating multi-source logs and applying pattern recognition. This surfaces root causes and reduces incident response time.

How do I start analyzing a log file for abnormal frequencies?▼

Run the log analyzer CLI tool with your log file to begin analysis. The script processes the input to identify abnormal frequencies, cluster errors, and detect trends.

Does multi-source log correlation store execution records for traceability?▼

Yes, multi-source log correlation stores execution records for traceability. This ensures that anomaly detection, error clustering, and alert triggers remain auditable.

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