Skill UI

This guide provides production-ready coding patterns for interacting with various built-in Replit services. It covers best practices for setting up singleton clients, implementing robust error handling, and creating type-safe wrappers for Key-Value Database, Object Storage, PostgreSQL, and Auth middleware. Ideal for developers building complex Replit integrations or establishing standardized data access layers.

This skill provides a practical guide for comprehensive accessibility validation of web applications. It helps users test compatibility with various screen readers, validate ARIA implementations, and debug issues related to assistive technologies. Ideal for ensuring that web forms, dynamic content, and navigation are fully usable by people with disabilities.

Ports an existing Semgrep rule into each requested language by running applicability analysis, test-first creation, AST-informed rule translation, and validation so that every target-language variant has its own rule+test directory.

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.

This skill provides a comprehensive walkthrough for setting up and testing Sentry error monitoring using both Node.js and Python SDKs. It demonstrates how to capture various event types, including simple messages and full exceptions, and crucially, how to enrich these events with user context, searchable tags, and detailed breadcrumbs. This is essential for verifying robust error logging and monitoring in a production environment.

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 guide identifies and provides remedies for common anti-patterns when configuring and using the Sentry SDK in production environments. It covers critical pitfalls like hardcoded DSNs, incorrect sample rate settings, and failure to flush events, ensuring data integrity, preventing cost overruns, and maintaining comprehensive monitoring coverage across various platforms.

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.

A comprehensive guide to managing Sentry's event volume, quotas, and rate limits. Learn how to prevent 429 Too Many Requests errors and control costs by implementing client-side sampling (sampleRate/tracesSampleRate), using beforeSend filters to drop noisy errors, and understanding various quota types (Errors, Transactions, Replays). Essential for high-volume, cost-sensitive applications.

A comprehensive guide for writing efficient GLSL shaders (Vertex/Fragment) used in WebGL and game engines. Learn fundamental concepts like shader structure, uniforms, varying variables, and advanced techniques such as raymarching and post-processing effects, optimizing graphics rendering performance on the GPU.

This guide details how to configure the Speak language learning platform across multiple environments (dev, staging, production). It is essential for implementing robust SaaS deployments, ensuring that separate API keys, mock modes, and environment variables are correctly managed for seamless development and operational management.

This comprehensive guide details the process for upgrading the Speak SDK, migrating data between various language learning platforms (such as Duolingo or Babbel), and handling critical API version deprecations. It provides structured steps for version checking, dependency upgrades, and implementing complex data mapping logic to ensure a smooth transition to production-ready systems.