Skill UI

Automates the setup of memory profilers for performance testing workflows, guiding users through best practices, code/configuration generation, and validation against industry standards whenever memory profiler setup is mentioned.

Automate the validation and testing of prompts and AI responses within your CI/CD pipeline using Mistral AI. This integration provides robust prompt regression testing, model quality checks, automatic cost estimation for API usage, and deployment gates, ensuring the stability and reliability of your AI features upon every pull request.

This comprehensive guide provides minimal working examples for integrating with the Mistral AI API. It demonstrates core LLM functionalities including basic chat completion, streaming responses, multi-turn conversations, and generating structured JSON output. Use this guide when starting a new Mistral integration, testing API connections, or learning advanced AI patterns using TypeScript or Python.

A comprehensive skill set for building, deploying, and managing production-grade Machine Learning systems. Expertise spans modern frameworks (PyTorch, TensorFlow), model serving using Kubernetes, advanced feature engineering (Feast, Tecton), and robust MLOps practices (monitoring, A/B testing, CI/CD). Focuses on delivering scalable, reliable, and business-value-driven AI solutions.

This skill provides comprehensive guidance for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, from initial data ingestion and rigorous feature engineering to model training, advanced validation (A/B testing, performance drift), and robust deployment strategies (Canary, Blue-green). It enables the design and orchestration of scalable, reproducible, and observable ML systems using tools like Airflow and Kubeflow.

Automatically generates robust, schema-compliant mock API servers directly from OpenAPI specifications. This tool supports advanced testing features, including stateful CRUD operations, Faker-driven dynamic data generation, configurable latency simulation, and detailed request recording for contract testing. Ideal for frontend parallel development and isolated integration testing.

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.

Mutation Test Runner delivers automated help for configuring and executing mutation testing workflows, offering best practices, code samples, and validation steps whenever mutation test runner patterns or automation requests appear.

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.

Guides performance testing teams through network latency tester tasks by suggesting best practices, generating production-ready scripts/configs, and validating results across load, stress, benchmarking, and monitoring scenarios.

Guides users through advanced network security scanning tasks by offering best-practice workflows, code/config generation, and validation for compliance, penetration testing, and enterprise security scenarios.

This comprehensive guide provides a step-by-step workflow for establishing a professional Obsidian plugin development environment. It covers cloning the official sample, installing necessary dependencies (Node.js, TypeScript, esbuild), configuring essential project metadata (`manifest.json`, `versions.json`), and setting up a symlinked development vault for seamless testing and building.