Skill UI

This advanced guide provides strategies for scaling Sentry in high-traffic, enterprise-level applications handling millions of events daily. It details implementing adaptive sampling based on endpoint criticality, using adaptive error deduplication to manage quotas, and optimizing the SDK for minimal runtime overhead, ensuring robust visibility even under extreme load.

This skill guides the setup of distributed tracing and performance monitoring using Sentry. It covers configuring the SDK (e.g., setting tracesSampleRate) to capture latency, identify performance bottlenecks, and trace requests across the entire application stack. It demonstrates how to create manual spans for business logic and utilizes profiling for deeper performance insights, ensuring comprehensive application observability.

A comprehensive guide to optimizing Sentry's performance monitoring pipeline. Learn how to minimize SDK overhead and event costs while maximizing signal quality. Techniques covered include replacing static sampling rates with dynamic `tracesSampler`, configuring profiling rates, preventing cardinality explosion through proper transaction naming, and implementing custom measurements for deeper business logic visibility. Essential for high-traffic, production applications.

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.

Guides engineers through setting up Server Sent Events connections as part of API integration work, providing best practices, sample configurations, and validated code for third-party webhooks, SDKs, and OAuth flows.

This comprehensive guide outlines the best practices and structured methodology for developing specialized skills for AI coding agents. It provides detailed instructions on integrating Azure SDKs and Microsoft Foundry services, covering crucial topics like secure authentication patterns (DefaultAzureCredential), modular workflow design, and maintaining concise, effective knowledge context for optimal agent performance.

This comprehensive guide demonstrates how to build a robust AI language tutoring session using the Speak SDK. It covers initiating conversations, submitting student responses (text or audio), performing deep pronunciation assessment at the phoneme level, and generating detailed session summaries with grammar and vocabulary metrics. Ideal for developing language education platforms and testing API integration.

This guide details how to set up the Speak language learning platform integration. It covers installing necessary SDKs (Speak SDK, OpenAI), configuring API keys, and establishing the connection required for sophisticated AI-powered tutoring. Use this when building applications that require real-time speech recognition, pronunciation feedback, and multilingual language support.

This guide provides a comprehensive deep dive for migrating language learning platforms, covering everything from importing student progress to transitioning from legacy speech APIs. It details configuration using modern SDKs, integrating services like OpenAI's GPT-4o and Whisper, and includes verification steps. It is essential for managing complex platform migrations and ensuring a smooth operational transition.

This guide provides production-ready patterns for integrating the Speak language learning API. It covers managing conversation sessions, performing pronunciation assessment, preprocessing audio files using ffmpeg, implementing robust retry mechanisms (with backoff), and tracking user learning progress. Use this when developing a full-featured, high-reliability language learning application or troubleshooting complex API integrations.