How does it present its findings?

It generates a structured report including log excerpts, error counts, identified patterns, a timeline of events, and specific remediation recommendations.

Does it support multiple environments?

Yes, the skill allows you to specify environments such as 'test' or 'prod' to ensure you are investigating the correct context.

What logs does this skill analyze?

The skill is specifically designed to query and analyze CloudWatch logs via command-line handlers, providing insights into AWS-hosted applications.

Can it help during a production outage?

Yes, it is built to correlate events and identify patterns during critical incidents to significantly speed up the recovery process.

Operations Investigator

Name: Operations Investigator
Author: fractary

$fractary$ byfractary

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Analíticas y Monitorización

Analyzes CloudWatch logs and correlates cross-service events to identify root causes and generate detailed incident reports.

The Operations Investigator skill empowers Claude to act as a specialized SRE or DevOps engineer by querying live log data, detecting error patterns, and performing deep-dive incident forensics. It automates the tedious process of searching through CloudWatch logs, building timelines of failure events, and providing actionable recommendations to resolve production issues. This skill is essential for teams looking to reduce Mean Time to Resolution (MTTR) by leveraging AI to synthesize complex log data across distributed services and identify the specific origin of system failures.

Características Principales

01Automated incident report generation with chronological timelines

02Root cause analysis powered by error pattern recognition

03Advanced CloudWatch log querying with complex filter patterns

04Cross-service event correlation for microservices debugging

052 GitHub stars

06Real-time identification of anomalies and system bottlenecks

Casos de Uso

01Tracing a single request flow through multiple backend components to find a failure point

02Investigating a sudden spike in 5xx errors across production services

03Generating a detailed post-mortem report following a system outage or performance degradation

Características Principales

01Automated incident report generation with chronological timelines

02Root cause analysis powered by error pattern recognition

03Advanced CloudWatch log querying with complex filter patterns

04Cross-service event correlation for microservices debugging

052 GitHub stars

06Real-time identification of anomalies and system bottlenecks

Casos de Uso

01Tracing a single request flow through multiple backend components to find a failure point

02Investigating a sudden spike in 5xx errors across production services

03Generating a detailed post-mortem report following a system outage or performance degradation