Does this skill require a Sentry account?

Yes, you must have an active Sentry account and a configured Sentry MCP server with OAuth authentication to use this skill.

Can I filter errors by specific timeframes?

Yes, the skill supports various timeframes such as 1h, 24h, 7d, or custom ranges to focus on specific incident windows.

Can it identify which users are affected by an error?

Yes, it provides insights into the number of unique users and sessions impacted by specific error issues captured in Sentry.

How does this help with release management?

The skill automatically correlates error spikes with specific release tags, allowing you to determine if a recent deployment introduced regressions.

Sentry Error Investigation

Name: Sentry Error Investigation
Author: TheBushidoCollective

byTheBushidoCollective

•

分析与监控

Analyzes and triages recent Sentry errors to identify root causes, patterns, and user impact across software projects.

This skill integrates with Sentry via MCP to streamline the debugging workflow directly within Claude Code, allowing developers to quickly query error logs and identify spikes without leaving the terminal. It provides a comprehensive analysis of issues, including frequency trends, affected user sessions, and correlation with specific releases or environments. By surfacing stack traces and key patterns, it helps engineering teams prioritize critical bug fixes and maintain application stability through actionable, AI-driven insights.

主要功能

01Real-time error triage and frequency analysis

02Automated recommendations for root cause resolution

0395 GitHub stars

04Cross-project error pattern and trend identification

05Impact analysis on users and unique sessions

06Release and environment-specific error correlation

使用场景

01Investigating sudden error spikes in production environments

02Aggregating error data for health checks and stability reports

03Post-deployment regression monitoring and rapid triage

主要功能

01Real-time error triage and frequency analysis

02Automated recommendations for root cause resolution

0395 GitHub stars

04Cross-project error pattern and trend identification

05Impact analysis on users and unique sessions

06Release and environment-specific error correlation

使用场景

01Investigating sudden error spikes in production environments

02Aggregating error data for health checks and stability reports

03Post-deployment regression monitoring and rapid triage