AI Coding Skills for Scala

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

Help HR managers, recruiters, and talent acquisition teams understand Backend Engineering concepts, hiring requirements, backend ecosystems, candidate evaluation, APIs, databases, scalability, cloud-adjacent backend infrastructure, and modern server-side workflows. Use when asked to "explain backend development", "screen backend candidates", "understand APIs and databases", "compare backend frameworks", "evaluate backend skills", "create backend interview questions", "understand microservices", "understand cloud backend systems", or any backend hiring and recruiting task.

Serverless and microservices development guidelines covering FastAPI, cloud-native patterns, API gateways, and best practices for scalable serverless architectures.

Optimizes hot-path scalar loops in .NET 8+ with cross-platform Vector128/Vector256/Vector512 SIMD intrinsics, or replaces manual math loops with single TensorPrimitives API calls. Covers byte-range validation, character counting, bulk bitwise ops, cross-type conversion, fused multi-array computations, and float/double math operations.

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

Author and review GitHub Actions workflow YAML safely so syntactically-valid YAML can't ship a workflow that GitHub Actions refuses to run. USE FOR: editing, adding, or reviewing any file under .github/workflows/, writing run-name/name/if/env/run values that contain ${{ }} expressions, diagnosing a run that fails with 'This run likely failed because of a workflow file issue' and no jobs starting, deciding when a workflow scalar must be quoted, validating workflows with actionlint. DO NOT USE FOR: authoring application YAML unrelated to GitHub Actions, Azure Pipelines, GitLab CI, or non-workflow YAML. SCOPE: this skill covers *syntactic/structural* correctness of workflow YAML (quoting, parsing, actionlint); for *semantic and functional* workflow design (what a workflow should do, agentic-workflow behavior), see .github/agents/agentic-workflows.agent.md — the two are complementary. INVOKES: actionlint (downloaded pinned binary) plus git/grep for inspection.

Deploy and configure Velociraptor for scalable endpoint forensic artifact collection during incident response using VQL queries, hunts, and pre-built artifact packs across Windows, Linux, and macOS environments.

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. (triggers: app/Http/Controllers/**/*.php, routes/*.php, controller, service, action, request, container)