sentry

Getting Started

1. Install the skill using the command above
2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
3. Reference the skill in your prompt
4. The AI will use the skill's capabilities automatically

Example Prompts

• "Review the open pull requests and summarize what needs attention"
• "Generate a changelog from the last 20 commits on the main branch"

Overview

Sentry is an error monitoring and performance platform that captures unhandled exceptions, tracks request performance with Web Vitals, records session replays, and alerts on regressions. It supports JavaScript, Python, Go, and mobile platforms with auto-instrumentation, source-mapped stack traces, and release health tracking.

Instructions

• When integrating the SDK, call Sentry.init() with dsn, environment, release, and tracesSampleRate, choosing the framework-specific SDK (@sentry/nextjs, @sentry/sveltekit, sentry-sdk for Python) for automatic instrumentation.
• When configuring error tracking, set up Sentry.setUser() after login for user correlation, add custom tags with Sentry.setTag() for filtering, and configure ignoreErrors for known harmless errors from browser extensions and third-party scripts.
• When uploading source maps, use @sentry/vite-plugin or @sentry/webpack-plugin in the CI build step to map minified stack traces back to original source code, associating them with the release version.
• When monitoring performance, set tracesSampleRate to 0.1-0.2 in production, add custom spans with Sentry.startSpan() for business-critical operations, and monitor Web Vitals (LCP, CLS, INP) for real user experience.
• When setting up alerts, configure rules for error rate spikes rather than individual errors, integrate with Slack or PagerDuty, and filter by environment and error level.
• When using session replay, set replaysOnErrorSampleRate: 1.0 for all error sessions and replaysSessionSampleRate: 0.1 for general sampling, with privacy masking for sensitive data.

Examples

Example 1: Set up Sentry for a Next.js production app

User request: "Add Sentry error monitoring and performance tracking to my Next.js app"

Actions:

1. Install @sentry/nextjs and run the setup wizard to configure sentry.client.config.ts and sentry.server.config.ts
2. Configure Sentry.init() with environment, release, and tracesSampleRate: 0.2
3. Add source map upload to the CI build pipeline with @sentry/nextjs webpack integration
4. Set up Slack alerts for error rate spikes in the production environment

Output: A Next.js app with automatic error capture, source-mapped stack traces, performance monitoring, and Slack alerting.

Example 2: Track release health and identify regressions

User request: "Set up release tracking to identify which deployment introduced a bug"

Actions:

1. Configure release in Sentry.init() using the git commit SHA or semantic version
2. Integrate with GitHub to link releases to commits for suspect commit detection
3. Set up deploy tracking to mark when releases go to staging and production
4. Configure regression alerts that notify when a previously resolved issue reappears

Output: Release health monitoring with crash-free session tracking, suspect commits, and regression alerts.

Guidelines

• Set tracesSampleRate to 0.1-0.2 in production since 100% sampling is expensive and unnecessary.
• Upload source maps in CI since unreadable minified stack traces are not useful for debugging.
• Set environment and release on every Sentry.init() call to filter errors by staging versus production.
• Use Sentry.setUser() after login to correlate errors with specific users for support.
• Configure alert rules for error rate spikes rather than individual errors to reduce noise.
• Set ignoreErrors for known harmless errors from browser extensions, network timeouts, and third-party scripts.