Where are the monitoring reports saved?

Reports are generated in JSON format and stored within the .fractary/plugins/faber-cloud/monitoring directory of your project for historical tracking.

Can it help me identify the cause of a service degradation?

Yes, it analyzes error rates, duration spikes, and resource utilization to identify patterns and provide specific recommendations for resolution.

Which cloud resources can this skill monitor?

This skill is optimized for AWS resources including Lambda functions, RDS databases, ECS clusters, S3 buckets, and API Gateways.

How does it access my cloud metrics?

The skill utilizes the project's configured AWS handlers to query CloudWatch API for real-time statistics and resource statuses.

Cloud Infrastructure Monitor

Name: Cloud Infrastructure Monitor
Author: fractary

byfractary

•

Analytics & Monitoring

Monitors and analyzes the health, performance, and metrics of deployed cloud infrastructure to prevent incidents and optimize resource usage.

About

The Cloud Infrastructure Monitor skill empowers Claude to proactively manage and observe your cloud deployments by interfacing directly with AWS services. It queries real-time CloudWatch metrics for resources such as Lambda, RDS, ECS, and API Gateway to detect anomalies, track SLIs/SLOs, and identify performance trends. By comparing runtime status against defined thresholds, the skill generates comprehensive health reports and provides actionable optimization recommendations, ensuring your production environments remain stable and performant.

Key Features

Anomaly detection and performance trend analysis
Threshold evaluation and proactive issue identification
Detailed health report generation with JSON exports
2 GitHub stars
Automated resource health status categorization (Healthy, Degraded, Unhealthy)
Real-time CloudWatch metrics querying (CPU, Memory, Latency, Errors)

Use Cases

Analyzing performance spikes and latency bottlenecks in serverless architectures
Performing comprehensive health audits across staging and production environments
Tracking SLI/SLO metrics to maintain service level agreements

About

Key Features

Anomaly detection and performance trend analysis
Threshold evaluation and proactive issue identification
Detailed health report generation with JSON exports
2 GitHub stars
Automated resource health status categorization (Healthy, Degraded, Unhealthy)
Real-time CloudWatch metrics querying (CPU, Memory, Latency, Errors)

Use Cases

Analyzing performance spikes and latency bottlenecks in serverless architectures
Performing comprehensive health audits across staging and production environments
Tracking SLI/SLO metrics to maintain service level agreements