What tools need to be installed to use this skill?

You need the gcloud CLI, jq, and DuckDB installed on your system, and you must be authenticated via gcloud auth login.

How does the skill handle large log volumes?

It follows a best-practice workflow of fetching logs into local JSON files and using DuckDB for high-speed analysis, avoiding repeated network requests.

Can it help with performance tuning?

Yes, it includes specialized filters for latency analysis and HTTP request summaries to help identify performance regressions.

Which GCP services can I investigate?

The skill supports any service reporting to Cloud Logging, including GKE, Cloud Run, Cloud Functions, App Engine, Load Balancers, and Audit Logs.

Does this work with AWS CloudWatch or Azure Monitor?

No, this skill is specifically optimized for Google Cloud Logging and requires the gcloud CLI.

Google Cloud Logging Investigator

Name: Google Cloud Logging Investigator
Author: pokutuna

bypokutuna

분석 및 모니터링

Analyzes Google Cloud Logging data using gcloud, jq, and DuckDB to diagnose errors and performance issues.

소개

This skill empowers Claude to perform deep-dive investigations into Google Cloud (GCP) environments by efficiently querying and processing logs. It leverages the gcloud logging read command to fetch data and utilizes jq or DuckDB for advanced filtering, aggregation, and pattern recognition. Whether troubleshooting 500 errors, analyzing request latency, or tracing distributed requests across services like GKE and Cloud Run, this skill provides a structured workflow to identify root causes quickly while optimizing for query speed and cost by prioritizing indexed fields and local file processing.

주요 기능

High-performance log aggregation and percentile analytics via DuckDB
Automated latency bottleneck identification for HTTP requests
Structured log querying with optimized gcloud logging filters
0 GitHub stars
Cross-service distributed tracing using Trace IDs and Span IDs
Advanced JSON processing using pre-configured jq filters for errors and traces

사용 사례

Visualizing request flows across microservices using Cloud Trace integration
Investigating production 500 errors and extracting stack traces from GKE or Cloud Run
Analyzing API latency distributions (p50, p95, p99) to identify slow endpoints

소개

주요 기능

High-performance log aggregation and percentile analytics via DuckDB
Automated latency bottleneck identification for HTTP requests
Structured log querying with optimized gcloud logging filters
0 GitHub stars
Cross-service distributed tracing using Trace IDs and Span IDs
Advanced JSON processing using pre-configured jq filters for errors and traces

사용 사례

Visualizing request flows across microservices using Cloud Trace integration
Investigating production 500 errors and extracting stack traces from GKE or Cloud Run
Analyzing API latency distributions (p50, p95, p99) to identify slow endpoints