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log-analyzer

Parse, search, and analyze application logs across plain text and JSON formats.

39|1|Updated Jul 2, 2026
Search Tags:#debugging#stack trace#log analysis#error monitoring#grep#jq#structured logging
One-click install
npx skills add https://github.com/HKU-MMLab/UniClawBench --skill log-analyzer-hku-mmlab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: log-analyzer
Source: https://github.com/HKU-MMLab/UniClawBench/tree/main/injection/101_skill_usage/task_101_03_access_log_regex/skills/log-analyzer
Command: npx skills add https://github.com/HKU-MMLab/UniClawBench --skill log-analyzer-hku-mmlab

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Debugging production issues often means digging through thousands of log lines across multiple services and formats. This Skill provides ready-to-use commands and scripts for searching, filtering, correlating, and summarizing logs so you can find root causes faster. ## Core Features & Use Cases - Pattern Search & Filtering: Find errors, exceptions, and specific request IDs using grep, awk, and jq across plain text, JSON, and access logs. - Stack Trace Analysis: Extract and deduplicate Java, Python, and Node.js stack traces, grouping them by root cause. - Multi-Service Correlation: Merge and sort logs from multiple services and trace a single request ID across distributed systems. - Error Reports & Monitoring: Generate error frequency reports, watch logs in real time with tail, and set up structured JSON logging in Node.js, Python, and Go. - Use Case: A production API is returning 500 errors. Use this Skill to filter the last hour of logs, trace the failing request ID across three services, and produce a ranked report of unique error messages. ## Quick Start Analyze the attached app.log file and summarize the top error messages with their frequency and hourly distribution.

Frequently Asked Questions about log-analyzer

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

FAQPage Schema
How do I search log files for errors using grep?▼

Use grep -i 'error\|exception\|fatal' app.log to find all error lines, and add -C 3 for surrounding context. For counting by type, pipe matched messages through sort and uniq -c to rank the most frequent errors.

How to parse JSON logs with jq?▼

Pipe JSON log lines into jq and filter with select, for example jq 'select(.level == "error")' to isolate errors. You can extract fields with jq -r '[.timestamp, .level, .message] | @tsv' and aggregate with sort and uniq -c.

How do I trace a request across multiple microservice logs?▼

Search for the request or correlation ID across all service log directories using grep -rH 'request-id' /var/log/services/. For JSON logs, filter each file with jq on the requestId field and merge results sorted by timestamp.

Can I analyze compressed or rotated log files?▼

Yes, use zgrep to search across rotated and gzip-compressed logs such as /var/log/app.log.1.gz without manual decompression. You can also zcat a compressed file, filter it, and recompress the results.

Why does grep output lag when following logs with tail -f?▼

Piped grep buffers its output by default, delaying results. Add the --line-buffered flag to grep so matches from tail -f appear immediately in real time.

What are the limitations of grep-based log analysis?▼

Grep and awk work well for filtering and counting but struggle with deeply nested JSON, multi-line entries, and very large datasets. For those cases use jq for structured fields, Python scripts for multi-line tracebacks, or a dedicated log platform.

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