Can I use this with my existing monitoring stack?

Absolutely. This skill is designed to integrate with popular tools like Prometheus, Grafana, and PagerDuty to provide a unified view of application health.

Does this skill help with alerting?

Yes, it defines specific thresholds for different error types and sets up alerting configurations to notify you before issues impact your users.

What types of errors can this skill monitor?

It can track a wide range of issues including HTTP 4xx/5xx errors, application exceptions, database connection failures, external API timeouts, and background job failures.

How does it help with root cause analysis?

By analyzing the frequency and patterns of errors across different architectural layers, it helps pinpoint whether an issue stems from code, infrastructure, or external dependencies.

Error Rate Monitor

Name: Error Rate Monitor
Author: intent-solutions-io

byintent-solutions-io

Analíticas y Monitorización

Monitors and analyzes application error rates to improve system reliability and proactively resolve performance issues.

Acerca de

The Error Rate Monitor skill empowers Claude to identify, track, and analyze various error types across an application’s architecture, including HTTP status codes, database timeouts, and background job failures. By establishing granular monitoring criteria and alert thresholds, it automates the setup of observability workflows, helping developers maintain high uptime and meet reliability goals through proactive incident detection and root cause analysis.

Características Principales

Integration-ready logic for Prometheus and Grafana
Threshold-based alerting configuration
Multi-source error analysis for HTTP, DB, and APIs
0 GitHub stars
Pattern identification for recurring failure modes
Granular tracking for specific application components

Casos de Uso

Setting up comprehensive error monitoring for a new web application
Configuring real-time alerts for critical 500-series errors and database timeouts
Analyzing failure patterns in background job processors to identify bottlenecks

Acerca de

Características Principales

Integration-ready logic for Prometheus and Grafana
Threshold-based alerting configuration
Multi-source error analysis for HTTP, DB, and APIs
0 GitHub stars
Pattern identification for recurring failure modes
Granular tracking for specific application components

Casos de Uso

Setting up comprehensive error monitoring for a new web application
Configuring real-time alerts for critical 500-series errors and database timeouts
Analyzing failure patterns in background job processors to identify bottlenecks