What log sources does Operations Investigator support?

It is primarily designed to query CloudWatch logs, but it leverages hosting handlers to interface with various cloud environments as specified in your configuration.

How does it handle distributed service architectures?

The skill correlates events across different services and log groups, allowing you to trace the flow of a request and identify where a chain of events broke down.

Can I filter logs by specific error types?

Absolutely. You can provide specific filter patterns to narrow down the investigation to particular exceptions, status codes, or keywords.

Can it help with post-mortem documentation?

Yes, it automatically generates structured incident reports including timelines, error patterns, and root cause analysis to assist with post-incident reviews.

Does it provide actionable recommendations?

Yes, after analyzing the log data and identifying the root cause, the skill suggests specific recommendations to fix the current issue and mitigate future risks.

Operations Investigator

Name: Operations Investigator
Author: fractary

byfractary

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Analytics & Monitoring

Investigates system incidents and analyzes CloudWatch logs to identify root causes and generate detailed incident reports.

About

Operations Investigator is a specialized skill designed to streamline incident response by automating log analysis across distributed services. It empowers developers and SREs to query CloudWatch logs with precise filters, correlate disparate events into a cohesive timeline, and detect recurring error patterns. By bridging the gap between raw log data and actionable insights, the skill facilitates rapid root cause analysis (RCA) and provides clear recommendations to resolve production issues and prevent future occurrences in cloud environments.

Key Features

Automated incident timeline generation and structured reporting
Cross-service event correlation for troubleshooting distributed architectures
2 GitHub stars
Advanced CloudWatch log querying with custom filters and flexible timeframes
Pattern recognition for identifying common errors like timeouts and connection failures
Evidence-based root cause analysis incorporating log excerpts and runtime data

Use Cases

Diagnosing the source of a sudden spike in 5xx errors across multiple microservices
Generating detailed post-mortem reports following production outages or performance regressions
Tracing specific request flows through logs to identify where a process failed or timed out

About

Key Features

Automated incident timeline generation and structured reporting
Cross-service event correlation for troubleshooting distributed architectures
2 GitHub stars
Advanced CloudWatch log querying with custom filters and flexible timeframes
Pattern recognition for identifying common errors like timeouts and connection failures
Evidence-based root cause analysis incorporating log excerpts and runtime data

Use Cases

Diagnosing the source of a sudden spike in 5xx errors across multiple microservices
Generating detailed post-mortem reports following production outages or performance regressions
Tracing specific request flows through logs to identify where a process failed or timed out