AI Coding Skills for Scala

Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.

This skill should be used when the user asks to "design system architecture", "evaluate microservices vs monolith", "create architecture diagrams", "analyze dependencies", "choose a database", "plan for scalability", "make technical decisions", or "review system design". Use for architecture decision records (ADRs), tech stack evaluation, system design reviews, dependency analysis, and generating architecture diagrams in Mermaid, PlantUML, or ASCII format.

Generate OpenAPI 3.1 documentation from your API codebase. Auto-detects Express, Fastify, NestJS, Django, FastAPI, Flask, Rails, Spring, Go, and more. Extracts routes, request/response schemas, auth requirements, and validation rules. Sets up interactive docs with Swagger UI, Redoc, or Scalar. Use when you need API documentation, OpenAPI spec, Swagger docs, endpoint reference, or REST API docs.

Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.

Build a production-ready data API from scratch: scaffold REST endpoints with models and validation, generate integration tests that verify every route, then load test for scalability. Use when you need an API backend, data service, CRUD layer, or microservice with verified correctness and performance.

Design, refactor, review, or implement Flutter app architecture using MVVM, layered UI/Data/optional Domain boundaries, feature-first or layer-first project structure, repositories, services, dependency injection, Result and Command patterns, offline-first or optimistic UI flows. Use when asked to add a Flutter feature, audit layer dependencies, fix cross-feature imports, migrate to feature-first, choose architecture for a Flutter project, or create scalable maintainable Flutter code organization.

Comprehensive guidelines for building scalable Django REST APIs with proper architecture, authentication, and performance optimization.

Automates GDPR Data Subject Access Request (DSAR) workflows including identity verification, PII discovery across databases and files using regex and NER, data mapping, response templating per Article 15 requirements, deadline tracking, and audit logging. Covers ICO/EDPB guidance compliance, exemption handling, and scalable batch processing. Use when building or auditing DSAR response capabilities under GDPR/UK GDPR.

Conduct a CTO-perspective technical strategy review of a codebase. Evaluates architecture decisions and build-vs-buy trade-offs, scaling readiness at 10x and 100x, engineering velocity and developer experience, technical debt ratio and blast radius, security posture at executive level, team scalability for hiring, and infrastructure cost efficiency. Produces a strategic risk matrix, architecture scorecard, and ranked investment priorities. Use when you need a technical strategy review, architecture assessment, scaling readiness check, tech debt audit, engineering velocity evaluation, Series A technical due diligence, or CTO-level briefing before a board meeting or fundraise.

Build scalable design systems with Tailwind CSS v4, design tokens, component libraries, and responsive patterns. Use when creating component libraries, implementing design systems, or standardizing UI patterns.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Expert guidance for game development with C#/Unity, Lua scripting, and best practices for scalable game architecture

Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.

Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including Vector<T>, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.

Enforce core architectural standards for scalable Laravel applications. Use when structuring controllers, service layers, action classes, Form Requests, or Service Container bindings in Laravel projects.

Expert in Angular TypeScript development with scalable, high-performance patterns

Full quality pass -- chains parallel UX + scalability audit, then QA verification, then consistency gate. Fixes everything it finds. Works with any stack.