Skill UI

This comprehensive guide details how to harden Sentry's security posture and ensure compliance with regulations like GDPR. It covers best practices for managing sensitive data, implementing PII scrubbing, and configuring `beforeSend` hooks to automatically redact sensitive information such as passwords, API keys, tokens, IP addresses, and emails from client-side and server-side events.

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.

This guide provides a comprehensive setup for Vercel observability. It covers enabling built-in analytics, collecting detailed runtime logs, implementing structured logging, configuring external log drains (e.g., Datadog, Sentry), and integrating OpenTelemetry tracing. Use this when monitoring production deployments and setting up robust error tracking.

This skill enforces Sentry's rigorous blog writing standards, ensuring all content—from deep technical dives to product announcements—reads like it was written by a senior developer. It guides users on adopting a specific, opinionated, and technically credible voice, structuring posts around problem-solving, and avoiding generic corporate jargon.

This skill helps users create a new Git branch that strictly adheres to established project naming conventions (e.g., Sentry standards). It automatically determines the appropriate branch type (such as `feat`, `fix`, `chore`, etc.) and generates a descriptive, standardized branch name by analyzing the provided work description or local code differences (diff). It guides the entire branching workflow, ensuring proper lineage and classification for code submissions.

Improves Go error handling by treating errors as structured data. This utility adds critical context—such as domain, user IDs, stack traces, and machine-readable error codes—to standard Go errors. This capability is vital for enhancing observability, enabling APM tools (like Sentry or Datadog) and on-call engineers to diagnose complex issues quickly without relying on manual log correlation.

A comprehensive collection of composable handlers and middlewares for Go's standard slog package. This skill enables developers to build robust, multi-stage logging pipelines by implementing critical steps like log sampling (throughput control), attribute formatting (PII stripping), advanced routing, and sinking logs to various professional backends (e.g., Sentry, Loki, Datadog). Ideal for production-grade services requiring high observability.

A comprehensive automation toolkit for managing Sentry error tracking and monitoring operations. Users can programmatically investigate issues, configure project-level alert rules, manage release versions, and monitor organizational structure. Essential for DevOps and development teams needing deep integration and programmatic control over error data.

This CLI tool automates the entire issue triage lifecycle for Linear and Jira. Users can pull backlogs, cluster duplicates, apply labels, reassign owners, and post detailed summaries—all directly from the shell. It is ideal for weekly backlog cleanup, post-release bug sweeps, and synchronizing data between multiple tools (e.g., Sentry to Linear).

This tool automates the diagnosis of production errors tracked by Sentry. By leveraging the Composio CLI, it retrieves comprehensive issue details, full stack traces, breadcrumbs, and associated commits. It maps stack frames to local source code, allowing agents to propose fixes and triage issues directly, eliminating manual copy-pasting.