Skill UI

A comprehensive guide to integrating Sentry into continuous integration and continuous deployment (CI/CD) pipelines (e.g., GitHub Actions, GitLab CI, CircleCI). This skill automates complex tasks such as creating releases, uploading source maps for readable stack traces, associating commits, and tracking deployment environments, ensuring rapid and accurate debugging of production errors.

Integrate Sentry into your CI/CD pipeline to achieve end-to-end release tracking. This skill guides you through using the Sentry CLI and SDK to record deployments, associate commits, upload source maps, and monitor crucial release health metrics. Key functions include detecting regressions, calculating crash-free session rates, and tracking user adoption curves for every deployed version.

Master advanced error capture and context enrichment using Sentry SDKs for both TypeScript and Python. This skill teaches how to reliably capture exceptions, track non-exception events (messages), and enrich reports with structured data like user context, tags, breadcrumbs, and custom fingerprints. It is essential for dramatically improving the ability to diagnose complex production issues quickly.

This guide provides a comprehensive, multi-language walkthrough for installing and initializing the Sentry SDK in various environments (Node.js, Python, Browser). It covers essential concepts like DSN setup, using secure environment variables, configuring release versions, and implementing basic error and performance tracking for robust observability in production and CI/CD pipelines.

This comprehensive guide provides a phased strategy for migrating error tracking services from legacy tools like Rollbar, Bugsnag, or New Relic to Sentry. It covers crucial steps including concept mapping between diverse providers, implementing safe parallel reporting (dual-write), validating data parity via API comparison, and performing the final cutover to ensure continuous monitoring and zero downtime.

A comprehensive checklist for ensuring Sentry is properly configured before any production deployment. This guide covers best practices for managing environment variables (DSN), implementing release tracking, optimizing error and trace sample rates, uploading source maps, and filtering noise. Essential for go-live readiness and auditing existing monitoring setups.

This guide details advanced patterns for building fault-tolerant Sentry integrations. It addresses critical real-world failure points, such as SDK initialization failures, network outages, and abrupt process shutdowns. By implementing graceful degradation, circuit breakers, and durable offline queuing, applications can ensure core functionality remains online and stable, even when the external error tracking service is unreachable or degraded.

A comprehensive guide to production-ready error tracking using Sentry SDK in TypeScript and Python. Learn how to implement structured error context via scopes, manage user journeys with breadcrumbs, filter out noise and PII using beforeSend/beforeBreadcrumb hooks, and use custom fingerprinting for accurate issue grouping. Essential for robust observability.

This skill guides developers through major Sentry SDK version upgrades (e.g., v7 to v8, Python v1 to v2). It helps detect deprecated APIs, runs automated codemods (like @sentry/migr8), and applies manual fixes for breaking changes, ensuring a smooth transition while maintaining robust error tracking and observability.

Automates the setup of ML experiment tracking with MLflow or W&B, configures the environment, initializes servers, and supplies snippets for logging parameters, metrics, and artifacts so runs stay reproducible and easy to compare.

Automates sklearn pipeline builder tasks within ML training, activating on relevant triggers to guide data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

This framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.