Does it monitor third-party integrations?

Yes, it tracks the latency of external service calls to ensure that third-party APIs are not causing performance regressions in your primary application.

What metrics does this skill track?

It tracks P50, P95, and P99 percentiles, as well as average and maximum response times across all monitored application layers.

Can I use this for specific database queries?

Yes, the skill allows for granular tracking of individual SQL or NoSQL queries to help you identify specific slow-running database interactions.

How does this help with Service Level Objectives (SLOs)?

By consistently monitoring response times against your targets, the skill identifies deviations from baseline performance, allowing you to proactively address degradation.

Application Response Time Tracker

Name: Application Response Time Tracker
Author: BbgnsurfTech

byBbgnsurfTech

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Analytics & Monitoring

Monitors and analyzes application latency across APIs, databases, and services to identify and resolve performance bottlenecks.

About

This skill empowers Claude to proactively manage application performance by providing real-time tracking and statistical analysis of response times. By calculating key metrics like P50, P95, and P99 percentiles, it helps developers pinpoint exactly where latency occurs—whether in database queries, external API calls, or background processes—making it an essential tool for maintaining Service Level Objectives (SLOs) and troubleshooting performance degradation during the development lifecycle.

Key Features

Multi-layer monitoring across APIs, databases, and external services
Real-time identification of application performance bottlenecks
1 GitHub stars
Advanced statistical analysis including P50, P95, and P99 percentiles
Granular tracking for specific endpoints and individual queries
Automated reporting of average and maximum response times

Use Cases

Diagnosing and fixing slow API endpoints during performance audits
Tracking latency trends to ensure compliance with Service Level Objectives (SLOs)
Monitoring database query execution times to optimize data access layers

About

Key Features

Multi-layer monitoring across APIs, databases, and external services
Real-time identification of application performance bottlenecks
1 GitHub stars
Advanced statistical analysis including P50, P95, and P99 percentiles
Granular tracking for specific endpoints and individual queries
Automated reporting of average and maximum response times

Use Cases

Diagnosing and fixing slow API endpoints during performance audits
Tracking latency trends to ensure compliance with Service Level Objectives (SLOs)
Monitoring database query execution times to optimize data access layers