What metrics does the Response Time Tracker provide?

The skill calculates P50, P95, and P99 percentiles, as well as average and maximum response times for comprehensive performance visibility.

What parts of the application stack does it cover?

It monitors API endpoints, database queries, external service calls, frontend rendering processes, and background job executions.

Can I monitor specific database queries with this skill?

Yes, you can instruct the skill to track performance for specific database queries to identify exact bottlenecks in your data layer.

Does this skill help with SLO monitoring?

Absolutely. It is specifically designed to help you monitor Service Level Objectives related to response times and receive alerts about performance degradation.

Application Response Time Tracker

Name: Application Response Time Tracker
Author: intent-solutions-io

byintent-solutions-io

Analytics & Monitoring

Monitors and optimizes application performance by tracking latencies across APIs, databases, and background jobs using percentile metrics.

About

This skill enables Claude to proactively monitor application performance by calculating critical latency metrics like P50, P95, and P99 across various system layers. It helps developers identify performance bottlenecks in API endpoints, database queries, and external service calls, providing actionable insights to maintain Service Level Objectives (SLOs) and prevent performance degradation before it impacts users.

Key Features

Proactive monitoring of background job execution times
Detailed percentile analysis including P50, P95, and P99 metrics
Real-time identification of application performance bottlenecks
0 GitHub stars
Automated latency reporting and optimization recommendations
Multi-layer monitoring for APIs, databases, and frontend rendering

Use Cases

Diagnosing and resolving slow API endpoints or database queries
Analyzing performance impacts of external service calls and background tasks
Monitoring Service Level Objectives (SLOs) for production applications

About

Key Features

Proactive monitoring of background job execution times
Detailed percentile analysis including P50, P95, and P99 metrics
Real-time identification of application performance bottlenecks
0 GitHub stars
Automated latency reporting and optimization recommendations
Multi-layer monitoring for APIs, databases, and frontend rendering

Use Cases

Diagnosing and resolving slow API endpoints or database queries
Analyzing performance impacts of external service calls and background tasks
Monitoring Service Level Objectives (SLOs) for production applications