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.

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 framework enforces organizational governance and policy guardrails for Sentry usage. It standardizes configuration across services, mandatorily scrubbing PII, enforcing naming conventions, and managing trace rate caps based on service criticality. It integrates into CI/CD to prevent compliance drift and ensure adherence to standards like GDPR and HIPAA.

This guide details how to use the Sentry CLI to manage the full application release lifecycle. Users can create versioned releases (using Semver or SHA), upload source maps to deminify stack traces, and associate specific git commits. This ensures that every error is accurately linked to the exact code version and deployment that caused it, significantly improving debugging and observability in CI/CD pipelines.

A comprehensive guide to configuring and utilizing ShellCheck for static analysis of shell scripts (Bash, sh, POSIX). This tool detects common programming pitfalls, enforces best practices, and helps ensure script reliability and portability. Ideal for setting up linting in CI/CD pipelines, pre-commit hooks, or maintaining code quality standards across a development team.

A comprehensive meta-skill for validating, testing, and scoring the quality of technical skills within an ecosystem. It performs deep structural checks (SKILL.md compliance), executes Python scripts for syntax, runtime, and output validation, and generates multi-dimensional quality scores using letter grades and tier recommendations. Ideal for new skill authorship, auditing existing components, or integrating rigorous QA gates into CI/CD pipelines.

This guide details setting up a robust CI integration pipeline for Speak language learning platform integrations. It utilizes GitHub Actions with mocked API testing and audio validation to ensure code reliability and functional correctness before deployment. Ideal for developers managing complex language learning products and APIs.

Automates SSH key manager workflows within DevOps Basics, offering guided best practices, code/config generation, and validation for foundational workflows such as git, containers, CI/CD, and infrastructure.

Automate the entire Supabase development lifecycle within CI/CD pipelines using GitHub Actions. This tool links projects, validates database migrations, deploys Edge Functions, generates TypeScript types, and runs comprehensive tests against local Supabase instances. Essential for maintaining robust code quality and ensuring schema consistency before deployment.

This expert skill specializes in guiding comprehensive GitHub issue resolution. It covers systematic bug investigation, feature implementation, and collaborative development workflows. Expertise includes issue triage, root cause analysis, test-driven development, and professional pull request management, ensuring vague reports are transformed into production-ready code following modern CI/CD practices.

This skill provides comprehensive, structured guidance for testing Temporal workflows written in Python. It covers best practices for unit testing (using time-skipping), integration testing (mocking dependencies), and determinism validation through replay testing. Ideal for ensuring high code coverage and robust CI/CD pipelines for complex asynchronous workflows.