Skill UI

This skill provides a comprehensive framework for configuring Mistral AI across distinct environments (development, staging, and production). It manages environment-specific secrets (using GCP, AWS, or Vault), defines critical parameters like default models, rate limits, and feature flags, ensuring secure, isolated, and reliable deployments for AI services in multi-stage pipelines.

This guide provides a production-ready, layered architecture pattern for integrating Mistral AI into TypeScript/Node.js projects. It implements best practices including singleton client management, Zod-validated configuration, custom error handling, caching mechanisms, and robust service layer design (e.g., RAG pipelines). Use this when setting up new Mistral AI applications or needing standardized, reliable project structure.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

Delivers end-to-end monitoring setups—structured logging, Prometheus/Grafana dashboards, alerting rules, distributed tracing, and load/profile testing—so teams can observe services, debug production issues, and plan capacity.

Documents setting up OpenTelemetry tracing/metrics, Prometheus/Grafana monitoring, structured logging, and alerting rules so teams can instrument services, track performance, and respond to incidents.

A comprehensive guide for developing custom external web service APIs for the Moodle Learning Management System (LMS). It covers Moodle's required API framework, coding standards, and the essential three-method pattern (execute_parameters, execute, execute_returns) necessary for integrating custom functionality, such as course management, quiz operations, and reporting, with external applications.

A comprehensive guide to implementing Mutual TLS (mTLS) for achieving zero-trust, secure service-to-service communication within a service mesh. This skill provides templates and concepts for configuring mTLS using industry standards like Istio, Cert-Manager, and SPIFFE/SPIRE, ensuring robust certificate management and encrypted internal communication.

This skill provides a comprehensive decision framework and set of patterns for architects designing cloud-agnostic applications across major providers like AWS, Azure, and GCP. It details service comparisons (Compute, Storage, DB), multi-cloud patterns (e.g., Best-of-Breed, DR), cost optimization strategies, and structured migration plans, ensuring robust and resilient enterprise deployments.

Build enterprise-grade NestJS REST or GraphQL modules, controllers, services, guards, interceptors, and tests with DI, validation, Swagger, and authentication flows.

This specialist provides expert architectural guidance for designing and implementing scalable modular monolith backends using NestJS. It leverages Domain-Driven Design (DDD), Clean Architecture, and event-driven patterns to establish clear bounded contexts and module boundaries. Use this skill when building complex systems that require high modularity, maintainability, and a planned path toward microservices, ensuring state isolation and loose coupling within a single deployment unit.

Automates NestJS module scaffolding with best-practice templates for backend services, covering Node.js, Python, Go, databases, caching, messaging, and microservices architecture.

Hands-on lab guide to configuring HTTP, HTTPS, SNMP, and SMB services for pentesting, covering setup, firewall rules, enumeration tools, and log inspection in controlled environments.