Can it detect performance drops after a specific deployment?

Yes, the /analyze-performance command is designed to identify performance trends and regressions, helping you correlate latency increases with specific releases or timeframes.

How does this skill connect to my Sentry data?

The skill utilizes the Sentry MCP (Model Context Protocol) server and requires OAuth authentication to securely fetch performance metrics from your specific Sentry projects.

Does it provide actionable advice for fixing issues?

Yes, based on the span-level analysis, the skill suggests specific optimizations such as adding database indexes, implementing caching strategies, or reviewing external dependencies.

What metrics are included in the performance report?

The generated reports include transaction throughput, Apdex scores, P50/P95/P99 latency percentiles, and a detailed breakdown of slow database queries and external API calls.

Sentry Performance Analyzer

Name: Sentry Performance Analyzer
Author: TheBushidoCollective

byTheBushidoCollective

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Analytics & Monitoring

Analyzes Sentry performance monitoring data to identify slow transactions, bottlenecks, and latency regressions.

About

The Sentry Performance Analyzer skill empowers developers to investigate application speed and health directly within Claude. By integrating with Sentry's MCP tools, it fetches and analyzes transaction metrics like P95 latency, throughput, and Apdex scores. It identifies specific bottlenecks in database queries and external API calls, detects regressions following new deployments, and provides actionable optimization recommendations to improve overall user experience and system reliability.

Key Features

Detects performance regressions and anomalies across different releases.
Analyzes database query performance and external service latency bottlenecks.
Identifies slowest transactions and endpoints using latency percentiles (P50, P95, P99).
72 GitHub stars
Provides prioritized optimization recommendations based on transaction impact.
Calculates throughput and Apdex scores to measure overall user satisfaction.

Use Cases

Monitoring overall application health and transaction throughput in real-time.
Identifying unoptimized database queries causing slow page loads.
Investigating sudden spikes in API latency after a production deployment.

About

Key Features

Detects performance regressions and anomalies across different releases.
Analyzes database query performance and external service latency bottlenecks.
Identifies slowest transactions and endpoints using latency percentiles (P50, P95, P99).
72 GitHub stars
Provides prioritized optimization recommendations based on transaction impact.
Calculates throughput and Apdex scores to measure overall user satisfaction.

Use Cases

Monitoring overall application health and transaction throughput in real-time.
Identifying unoptimized database queries causing slow page loads.
Investigating sudden spikes in API latency after a production deployment.