Does this skill work with JSON-formatted logs?

Yes, it includes specialized utility scripts for parsing and filtering JSON logs using jq, allowing you to filter by severity level or specific metadata fields.

How does this skill handle extremely large log files?

It follows a 'Filter First' strategy, using grep and bash tools to extract only relevant lines before attempting to read the content, ensuring efficiency and preventing context window overflow.

Can I trace a single user request across multiple logs?

Yes, the skill includes specific workflows and scripts to trace correlation IDs or request IDs across multiple log files to reconstruct a transaction history.

What kind of statistical analysis can it perform on logs?

The skill can aggregate errors by frequency, show error distribution over time (hourly or minute buckets), and identify slow operations by duration.

Log Analysis & Debugging

Name: Log Analysis & Debugging
Author: rileyhilliard

byrileyhilliard

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Analytics & Monitoring

Analyzes large-scale application logs efficiently through targeted filtering and iterative refinement to identify root causes and patterns.

About

The Reading Logs skill provides a high-performance framework for Claude to navigate complex log environments without exhausting context windows. Built on the 'Filter First' iron law, it utilizes advanced grep strategies, time-window isolation, and specialized utility scripts to aggregate errors, trace correlation IDs across files, and extract stack traces. This skill is essential for developers performing incident response, debugging performance bottlenecks, or analyzing recurring error patterns in high-volume production logs.

Key Features

62 GitHub stars
Error aggregation and frequency analysis for pattern detection
Targeted filtering using grep and regex to isolate relevant log events
Time-window isolation to narrow searches to specific incident periods
Request ID tracing to follow specific transactions across multiple files
Specialized parsing for JSON logs and stack trace extraction

Use Cases

Debugging recurring application issues by analyzing frequency and distribution
Extracting and grouping stack traces from large log files for root cause analysis
Investigating production timeouts and errors using correlation IDs

About

Key Features

62 GitHub stars
Error aggregation and frequency analysis for pattern detection
Targeted filtering using grep and regex to isolate relevant log events
Time-window isolation to narrow searches to specific incident periods
Request ID tracing to follow specific transactions across multiple files
Specialized parsing for JSON logs and stack trace extraction

Use Cases

Debugging recurring application issues by analyzing frequency and distribution
Extracting and grouping stack traces from large log files for root cause analysis
Investigating production timeouts and errors using correlation IDs