Skill UI

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.

A comprehensive diagnostic bundle designed for developers integrating with the Speak API. It systematically collects critical information, including authentication status, audio format validation (checking codecs, sample rates, and file size using ffprobe), and network connectivity tests for both Speak and OpenAI endpoints. Use this tool when troubleshooting complex language learning integration issues or verifying API setup.

This guide outlines the end-to-end process for deploying Speak language learning features to modern cloud environments, including Vercel, Cloud Run, or containerized setups. It covers initial configuration, implementing the core logic using TypeScript, and performing production verification. Use this skill when setting up a new Speak integration in a production environment or managing the platform's deployment lifecycle.

This skill provides comprehensive observability for the Speak language learning platform. It monitors critical metrics such as API health status, assessment latency, session usage, and detailed pronunciation score distributions. It is essential for developers implementing robust, production-grade monitoring systems and managing large-scale language learning operations.

This guide provides a comprehensive method for optimizing the latency and reliability of the Speak API integration. It covers techniques such as audio preprocessing, implementing response caching, and managing connection pooling. Use this skill when establishing a production-grade, low-latency language learning platform or managing high-volume API operations.

This guide provides robust patterns for managing external API rate limits, specifically detailing the implementation of exponential backoff, request queuing, and throttling strategies. It uses TypeScript examples to ensure stable and reliable API interactions. Use this when building production-grade applications that require frequent, high-volume calls to rate-limited services, such as language learning APIs.

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.

This guide outlines essential security best practices for integrating the Speak API. It covers critical areas like securing API keys using secrets managers, ensuring audio data privacy by deleting local files, and maintaining compliance with major regulations such as COPPA and FERPA. Use this knowledge when building production-ready, secure language learning applications.

Speeds up LLM inference by combining speculative decoding with Medusa multiple heads and lookahead techniques so chatbots, code assistants, and other real-time workloads can run with 1.5-3.6× lower latency while preserving target-model quality.

Guides Spring Boot 3.x engineers through configuration scaffolds, REST controllers, Spring Security 6 authentication flows, Spring Data JPA repositories, and reactive WebFlux endpoints, making it easier to build microservices and Java REST APIs.

Checklist covering Spring Security best practices for authentication, authorization, validation, CSRF, secrets, headers, rate limiting, dependency scans, and logging in Java Spring Boot services.

Structured pre-PR verification loop for Spring Boot projects that runs fast-build checks, static analysis, tests with coverage, security scans, formatting gates, and diff reviews to catch issues before deployment.