Does it provide context for found errors?

Yes, when searching or filtering, the skill can provide context lines before and after the match to help you understand the events leading up to an error.

Can this skill handle very large log files?

Yes, the skill is designed to be efficient by using tools like log_tail and log_filter to process specific segments of logs without needing to read the entire file into memory.

Does it support JSON-formatted logs?

Absolutely. The skill recognizes common log formats including structured JSON logs, Syslog, and standard web server logs like Apache and Nginx.

How does it help in identifying recurring issues?

The log_aggregate and log_stats tools specifically group similar log entries together, allowing you to see the frequency of specific errors and identify trending issues.

Can I search for specific patterns like HTTP status codes?

Yes, the log_search tool supports regex and pattern matching, making it easy to find specific HTTP codes, transaction IDs, or custom error messages.

Log Analysis

Name: Log Analysis
Author: ShunsukeHayashi

byShunsukeHayashi

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Analytics & Monitoring

Analyzes and parses log files to identify patterns, extract errors, and troubleshoot application behavior using advanced filtering and search tools.

About

The Log Analysis skill provides Claude with a comprehensive toolkit for investigating system and application logs. It allows for efficient reading, searching, and pattern matching across various log formats, enabling users to quickly isolate root causes of errors. By providing capabilities for time-based filtering, error frequency statistics, and entry aggregation, this skill transforms raw log data into actionable insights for developers and DevOps teams during incident response or routine monitoring. It is an essential utility for anyone needing to navigate high-volume logs to find specific exceptions or performance trends.

Key Features

Automated error extraction and severity level filtering
Frequency analysis and log entry aggregation for trend identification
Pattern matching and regex search with context line retrieval
4 GitHub stars
Time-based log slicing for precise event investigation
Multi-format support for JSON, Syslog, and web server logs

Use Cases

Searching for specific transaction IDs or trace IDs across multiple log files
Identifying the root cause of production errors by filtering for critical logs
Analyzing application performance and error frequency over specific time periods

About

Key Features

Automated error extraction and severity level filtering
Frequency analysis and log entry aggregation for trend identification
Pattern matching and regex search with context line retrieval
4 GitHub stars
Time-based log slicing for precise event investigation
Multi-format support for JSON, Syslog, and web server logs

Use Cases

Searching for specific transaction IDs or trace IDs across multiple log files
Identifying the root cause of production errors by filtering for critical logs
Analyzing application performance and error frequency over specific time periods