log-analyzer

Analyze log files to extract error patterns and detect anomalies.

Updated May 13, 2026
One-click install
npx skills add https://github.com/Mekann2904/mekann --skill log-analyzer-mekann2904
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: log-analyzer
Source: https://github.com/Mekann2904/mekann/tree/main/.pi/lib/skills/log-analyzer
Command: npx skills add https://github.com/Mekann2904/mekann --skill log-analyzer-mekann2904

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you quickly sift through large log files to identify errors, analyze patterns, and pinpoint the root cause of issues, saving you valuable debugging time.

Core Features & Use Cases

  • Error Detection: Quickly find and categorize different types of errors (e.g., ERROR, CRITICAL, FATAL).
  • Pattern Analysis: Group similar error messages and track specific requests or user IDs through logs.
  • Performance Metrics: Analyze response times and error rates over time.
  • JSON Log Parsing: Efficiently query and analyze structured JSON logs using jq.
  • Use Case: When your application is experiencing intermittent failures, use this Skill to analyze recent logs, identify recurring error patterns, and correlate them with specific timeframes or user requests to diagnose the problem.

Quick Start

Use the log-analyzer skill to find all lines containing 'ERROR' in the file '/var/log/app.log'.

Frequently Asked Questions about log-analyzer

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

FAQPage Schema
How do I analyze application logs to find the root cause of intermittent failures?

Analyzing application logs pinpoints intermittent failures by extracting error patterns, classifying log levels, and correlating events by timestamp. This process tracks specific requests or user IDs through logs to diagnose recurring issues.

Can I parse structured JSON logs to identify error patterns and calculate error rates?

Parsing structured JSON logs identifies error patterns and calculates time-series error rates using jq queries. It efficiently extracts grouped error messages and analyzes response times for performance monitoring.

How do I group similar error messages when troubleshooting system logs?

Grouping similar error messages when troubleshooting system logs is achieved through pattern aggregation. It categorizes different error types like ERROR and CRITICAL, tracking specific requests to reveal root causes.

Does this log analysis approach work for both application logs and access logs?

Log analysis applies to application logs, system logs, and access logs. It detects anomalies and extracts error patterns across these formats for root cause analysis and performance monitoring.

What is the best way to calculate time-series error rates from large log files?

Calculating time-series error rates from large log files involves pattern aggregation and timestamp correlation. It classifies log levels and detects anomalies to monitor performance and pinpoint root causes efficiently.

Why do I need log parsing for debugging instead of manually reading log files?

Log parsing for debugging saves time by quickly sifting through large files to identify errors and analyze patterns. Manual reading is inefficient, whereas parsing correlates events by timestamp to pinpoint root causes.