Skill UI

A comprehensive set of architectural and coding standards for building high-reliability, observable, and maintainable backend services. It mandates the use of layered architecture, explicit error handling (Sentry), robust validation (Zod), and strict dependency management for Node.js, Express, and TypeScript projects.

Implement comprehensive monitoring, structured logging, and performance tracking for Clerk authentication flows. This solution covers logging sign-in/out events, tracking middleware performance, handling webhook events, and integrating detailed error tracking with Sentry, ensuring robust debugging and auditing capabilities in production environments.

This skill generates commit messages strictly following Sentry's conventions and the Conventional Commit format. It mandates a structured commit message including a type, scope, subject, body (explaining what and why), and a footer for issue references. Use this whenever committing code changes to ensure a highly readable, traceable, and standardized version history.

This skill acts as a compatibility alias for pull request (PR) creation. It is designed to handle requests using the legacy name 'create-pr', redirecting the user to the canonical and updated workflow found in 'sentry-skills:pr-writer'. Use this when referencing the old skill name to ensure the latest PR writing standards are applied.

A comprehensive, AI-assisted methodology for diagnosing complex software errors in production environments. This skill guides users through the entire debugging lifecycle, from initial triage and gathering observability data (Sentry, DataDog, Jaeger) to generating hypotheses, selecting appropriate debugging strategies (Time-Travel, Chaos Engineering), and performing AI-powered root cause analysis. It culminates in proposing validated fixes and prevention measures.

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.

Read-only assistant that inspects Sentry issues, summarizes recent production errors, and pulls health metrics via the Sentry API when users ask for error reports or recent crashes.

This skill provides systematic, advanced diagnostic paths for complex Sentry issues. It addresses common pitfalls such as silently dropped events, failed source map resolution, gaps in distributed tracing, SDK memory leaks, and conflicts with other observability libraries. Use this guide when standard debugging fails and events disappear mysteriously.

A comprehensive guide to configuring Sentry error tracking and performance monitoring across modern application architectures. Learn how to implement coherent, end-to-end distributed tracing for monoliths, microservices, serverless functions (Lambda/Cloud Functions), event-driven systems, SPAs, and mobile applications, ensuring all application hops are monitored from a single source.

Automates Sentry monitoring workflows through Composio’s Rube MCP by orchestrating issue investigation, project issue management, alert rule configuration, release tracking, and organizational monitoring.

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.

This guide provides comprehensive steps to diagnose and fix the most frequent Sentry SDK integration issues. It covers critical topics such as DSN validation, missing event capture, source map failures, rate limiting, and proper flushing patterns in Node.js, Python, and serverless environments. Use this when your error monitoring system is not working as expected.