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log-analyzer

Analyze application logs to identify errors and recurring operational problems.

195|12|Updated Jul 19, 2026
Search Tags:#debugging#log analysis#real-time monitoring#json logs#stack traces#error patterns#request correlation
One-click install
npx skills add https://github.com/ZJU-REAL/HugAgentOS --skill log-analyzer-zju-real
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: log-analyzer
Source: https://github.com/ZJU-REAL/HugAgentOS/tree/main/src/backend/skill_bundles/marketplace/log-analyzer
Command: npx skills add https://github.com/ZJU-REAL/HugAgentOS --skill log-analyzer-zju-real

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill reduces the time and effort required to investigate application failures by turning noisy logs into searchable, structured, and actionable evidence.

Core Features & Use Cases

  • Multi-Format Analysis: Search and analyze plain text, JSON, access, delimited, compressed, and mixed-format logs.
  • Error and Trace Investigation: Find error patterns, parse stack traces, summarize recurring failures, and correlate events across services.
  • Real-Time Monitoring: Follow live log output, filter important events, and watch for operational alerts.
  • Structured Logging Guidance: Set up JSON logging with Node.js, Python, and Go for easier filtering and aggregation.
  • Use Case: When a production request fails across several services, use its correlation ID to trace related events, identify the root exception, and generate an error frequency report.

Quick Start

Use the log-analyzer skill to inspect the attached application logs, identify the most frequent errors, and summarize likely root causes with relevant request IDs and timestamps.

Frequently Asked Questions about log-analyzer

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

FAQPage Schema
How do I trace a failed production request across multiple services using logs?▼

To trace a failed request across services, you correlate events using a shared correlation ID. The log-analyzer parses multi-service logs to identify the root exception and generate a report with relevant request IDs and timestamps.

Can I parse and filter structured JSON application logs?▼

Yes, you can parse and filter structured JSON application logs. The analyzer supports multi-format analysis, applying tools like jq and Python to search patterns and aggregate errors in plain text, JSON, and delimited log files.

What is the best way to find recurring error patterns and parse stack traces?▼

The best way to find recurring error patterns and parse stack traces is through error aggregation. The analyzer summarizes recurring failures and extracts stack traces from application logs to pinpoint root causes during incident investigation.

How do I set up structured logging for easier filtering in Node.js, Python, and Go?▼

To set up structured logging for easier filtering, you configure JSON logging formats. The analyzer provides structured logging guidance for Node.js, Python, and Go to enable efficient aggregation and pattern searches.

Does this log analysis approach work with compressed and mixed-format logs?▼

Yes, this log analysis approach works with compressed and mixed-format logs. It applies pattern searches and timestamp filtering across plain text, access, delimited, and compressed logs using grep, awk, and Python.

How do I monitor live log output and filter important operational events?▼

To monitor live log output and filter important events, you use real-time monitoring. The analyzer follows live log streams, watches for operational alerts, and applies pattern searches to surface critical failures immediately.

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