What metrics does the Response Time Tracker provide?

It calculates P50, P95, and P99 percentiles, as well as average and maximum response times, to provide a comprehensive view of system latency.

How do I activate the tracking process?

You can trigger the skill using natural language commands like 'track response times', 'monitor API performance', or 'analyze latency' within Claude Code.

Does this skill integrate with existing monitoring tools?

Yes, it is designed to work with your application's monitoring infrastructure, APM tools, and custom instrumentation metrics.

Can it help identify slow database queries?

Yes, it tracks individual database query execution times and correlates them with overall application response times to isolate bottlenecks.

Response Time Tracker

Name: Response Time Tracker
Author: BbgnsurfTech

byBbgnsurfTech

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Analytics & Monitoring

Tracks and optimizes application response times across API endpoints, database queries, and service calls to resolve performance bottlenecks.

About

The Response Time Tracker skill enables Claude to proactively monitor and improve application performance by analyzing latency across multiple architectural layers. It provides deep visibility into API endpoints, database queries, and external service calls by generating detailed reports including P50, P95, and P99 percentiles. This skill is essential for developers who need to identify performance degradation, maintain Service Level Objectives (SLOs), and receive data-driven recommendations for system optimization.

Key Features

Automated identification of performance bottlenecks and root causes
Prioritized optimization recommendations based on captured metric data
3 GitHub stars
Detailed percentile reporting (P50, P95, P99) for granular latency analysis
Multi-layer monitoring across APIs, databases, and background services
Performance trend visualization and SLO compliance status reporting

Use Cases

Monitoring database query performance to optimize indexing and query structure
Tracking service level objectives (SLOs) to ensure consistent application reliability
Diagnosing slow API endpoints to improve user authentication or data retrieval speeds

About

Key Features

Automated identification of performance bottlenecks and root causes
Prioritized optimization recommendations based on captured metric data
3 GitHub stars
Detailed percentile reporting (P50, P95, P99) for granular latency analysis
Multi-layer monitoring across APIs, databases, and background services
Performance trend visualization and SLO compliance status reporting

Use Cases

Monitoring database query performance to optimize indexing and query structure
Tracking service level objectives (SLOs) to ensure consistent application reliability
Diagnosing slow API endpoints to improve user authentication or data retrieval speeds