Skill UI

SGLang is a high-performance serving framework for LLMs and VLMs that uses RadixAttention prefix caching to deliver structured outputs (JSON/regex/grammar) and agentic workflows with function calls, providing up to 5× faster inference than vLLM across multi-GPU production deployments.

Guides building, tuning, and validating production Apache Spark jobs, covering DataFrame/RDD choices, partitioning, broadcast joins, skew handling, caching, and cluster configuration for big data pipelines.

This guide provides advanced patterns and best practices for optimizing Apache Spark jobs. It covers critical areas like effective partitioning, memory tuning, minimizing data skew, and optimizing different types of joins (broadcast, bucket). Use this when troubleshooting slow, large-scale data processing workflows or needing to scale Spark pipelines efficiently and reliably.

This skill guides the configuration of Speak for enterprise environments, such as schools and large organizations. It covers implementing Role-Based Access Control (RBAC), setting up Single Sign-On (SSO), managing diverse roles (teacher/student), handling class management, and establishing usage reporting. This setup is essential for deploying a secure, scalable language learning platform tailored for institutional use.

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 skill provides a comprehensive guide for developers to configure and verify webhooks for the Speak language learning platform. It addresses handling critical lifecycle events, including lesson completion, progress tracking, and achievement notifications. Use this when building production-ready integrations or managing enterprise-level data synchronization from Speak's API.

A specialized reverse-engineering skill designed to extract comprehensive functional and non-functional specifications from existing, undocumented, or legacy codebases. It operates by analyzing code structure, tracing data flows, and identifying observable behaviors. The output is structured using EARS (Easy Approach to Requirements Syntax), providing clearer requirements documentation, architectural diagrams, and dependency maps for onboarding or system modernization.

This skill provides detailed guidance on authoring effective `specs.md` files for autonomous development workflows. It teaches users how to clearly define sprint goals, establish explicit scope boundaries (In/Out of Scope), and configure rigorous testing requirements (QA, UI Testing). This ensures that agentic development remains focused and prevents scope creep.

Automates splitting datasets into training, validation, and test subsets by generating and running Python code so you can quickly prepare data for machine learning model training and evaluation.

Guides Java engineers through Spring Boot backend architecture, REST API design, service layering, repository patterns, caching, async handlers, logging, middleware, and resilience practices for scalable production services.

This skill provides a structured approach to systematically identify security threats by applying the STRIDE methodology (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege). It is crucial for conducting thorough threat modeling sessions, analyzing system architectures, reviewing security design decisions, and creating robust security documentation for compliance and auditing purposes.

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.