Skill UI

This framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.

Provides a comprehensive incident response runbook for managing outages of the Speak API. It covers critical procedures including API triage, implementing offline fallback modes, and ensuring robust recovery steps. Ideal for DevOps teams maintaining language learning platforms reliant on real-time AI services (GPT-4o, Whisper).

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 the complete, production-grade reference architecture for integrating the Speak language learning platform. It covers all necessary components, including the client, API gateway, assessment engine, and progress store. Use this blueprint to ensure a robust, scalable, and reliable implementation for managing large-scale language learning operations.

This skill simulates a complete, multi-agent software development lifecycle following the principles of Agile and Scrum. It guides the project through six distinct, iterative phases: Architectural Planning, Parallel Implementation, Comprehensive Testing (Unit/E2E), Review, and Finalization. The process uses a convergent diffusion model, refining specifications and resolving conflicts across multiple specialized agents until the project converges and is deemed complete.

Automates guidance for statistical significance calculations in data analytics, offering triggered best practices, example implementations in SQL, visualization, and BI workflows, plus validation against common standards.

Automates guidance for streaming inference setup, covering best practices in ML deployment, serving, monitoring, and production-ready configurations whenever you ask about streaming inference patterns or implementation.

A workflow for executing implementation plans by spinning up a fresh subagent for each task and enforcing consecutive spec compliance and code quality reviews so the session stays focused and high-quality without manual context switches.

Master advanced techniques for building fault-tolerant applications integrated with Supabase. This guide implements critical resilience patterns, including circuit breakers, offline queuing (using IndexedDB), graceful degradation, exponential backoff retries, and dual-write strategies. Use this skill to handle network outages, API failures, and transient errors, ensuring your application remains highly available and reliable in production environments.

A detailed, multi-phase workflow that strictly enforces the Test-Driven Development (TDD) discipline using the Red-Green-Refactor cycle. This guide covers the entire software development lifecycle, from test specification and architecture design to minimal implementation, rigorous refactoring, and comprehensive integration testing. It mandates high code and test coverage (e.g., 80% line coverage) to ensure robust, maintainable, and high-quality production code.

This skill helps developers adhere to Test-Driven Development (TDD) principles by generating comprehensive, failing unit and integration tests. It is specifically designed for the 'Red' phase, ensuring that expected behaviors, edge cases (null, boundary values), and error handling are rigorously defined before any implementation code is written. It forces the definition of the 'what' before the 'how'.

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.