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albertdobmeyer
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log-analyzer

Analyze application logs to surface errors, exceptions, and root causes.

2|1|Updated Mar 2, 2026
Search Tags:#debugging#monitoring#logs#log analysis#incident response#json logs#stack traces
One-click install
npx skills add https://github.com/albertdobmeyer/opentrapp --skill log-analyzer-albertdobmeyer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: log-analyzer
Source: https://github.com/albertdobmeyer/opentrapp/tree/main/workloads/skills/skills/log-analyzer
Command: npx skills add https://github.com/albertdobmeyer/opentrapp --skill log-analyzer-albertdobmeyer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you quickly find, filter, and interpret logs when an application is failing, slow, or behaving unexpectedly. It reduces the time spent scanning noisy log output by hand and turns raw log lines into actionable debugging clues.

Core Features & Use Cases

  • Error and exception hunting: Search for failures, warnings, stack traces, and repeated error patterns across plain text logs.
  • Structured log analysis: Parse JSON or other structured logs to filter by level, timestamp, request ID, or nested context fields.
  • Cross-service correlation: Trace a single request or correlation ID across multiple services to reconstruct what happened during an incident.
  • Operational monitoring: Summarize log volume, error frequency, and top recurring issues for debugging and incident response.
  • Example: If production starts returning 500 errors, use this Skill to isolate the affected time window, identify the most common exception message, and follow the request ID through related services.

Quick Start

Ask the log-analyzer skill to inspect the attached log file, identify the most frequent errors, and summarize the likely root cause with any relevant request IDs or timestamps.

Frequently Asked Questions about log-analyzer

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

FAQPage Schema
How do I find errors and stack traces in large application logs?▼

To find errors in application logs, you can filter for failures, warnings, and stack traces using pattern matching. This isolates exceptions and recurring error patterns across large or rotated plain text log files.

How do I parse JSON logs to filter by request ID and timestamp?▼

Parsing JSON logs to filter by request ID and timestamp requires structured parsing using tools like jq or Python. This extracts specific log levels and nested context fields for precise debugging.

What is the best way to trace a correlation ID across multiple services?▼

Tracing a correlation ID across multiple services involves cross-service correlation to reconstruct incident timelines. By filtering logs by a specific request ID, you can track a single request through related microservices.

Can I use this for incident response and monitoring log volume?▼

Yes, this supports incident response and monitoring by summarizing log volume and error frequency. It helps identify top recurring issues and isolate affected time windows during operational triage.

Does log analysis work with plain text and structured access logs?▼

Yes, log analysis works with plain text logs, JSON logs, stack traces, and access logs. It applies pattern matching and structured parsing to handle both unstructured and formatted application outputs.

How do I summarize the likely root cause from noisy application logs?▼

To summarize the likely root cause from noisy logs, the analysis identifies the most frequent exception messages and extracts relevant request IDs and timestamps. This turns raw log lines into actionable debugging clues.

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