Skill UI

This skill provides a comprehensive framework for monitoring Mistral AI API usage in production. It includes an instrumented client wrapper (TypeScript) to automatically capture critical metrics like latency, token consumption, cost, and error rates. Furthermore, it integrates with Prometheus for metric storage and Grafana for visualization, allowing users to set up sophisticated alerting rules for performance degradation, rate limiting, or cost spikes.

This guide provides production-ready patterns for integrating the Mistral AI SDK in both TypeScript and Python. It covers essential best practices such as singleton client management, handling structured JSON output, implementing streaming responses, concurrent asynchronous processing, and robust error handling with exponential backoff. Use this when establishing team coding standards or refactoring Mistral API usage.

This comprehensive skill guides developers through major version upgrades of the Mistral AI SDK. It specifically detects breaking changes, such as the shift from CommonJS to ESM, and provides detailed steps, automated code transformation scripts, and best practices for migrating existing code. Use this when jumping major SDK versions or refactoring complex Mistral integrations to ensure a smooth transition to the latest API.

Auto-triggered assistant that guides mkdocs config generation, shares best practices, produces ready-to-use configs, and validates API docs, user guides, and architecture documentation.

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.

Mastering the full lifecycle of machine learning models from data ingestion to deployment. This expertise covers building robust, production-grade MLOps pipelines using orchestrators like Kubeflow, Airflow, or Prefect. Key skills include feature store implementation with Feast, rigorous data validation, experiment tracking (e.g., MLflow), model registry management, and automated deployment workflows. Essential for automating model training and ensuring reproducibility at scale.

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

Enforces mobile-first, touch-first decisions by requiring feasibility checks, mandatory readings, and anti-pattern bans before any UI work to prevent desktop or AI-default assumptions.

Mastery of modern mobile development across multiple platforms, including React Native, Flutter, and native iOS/Android. Expertise covers the entire lifecycle, specializing in architectural patterns (Clean, MVVM), performance optimization, offline-first data synchronization (GraphQL, SQLite), advanced DevOps practices (CI/CD, Fastlane), and App Store Optimization (ASO). Ideal for building robust, scalable, and highly performant cross-platform applications.

This expert specializes in secure mobile development practices, covering areas like input validation, WebView security, secure data storage (KeyStore/Keychain), and advanced network security (Certificate Pinning). Use this skill to write secure, resilient mobile code for both iOS and Android, mitigating platform-specific vulnerabilities and ensuring sensitive data protection.

Guides use of mock generator techniques for test automation, covering best-practice patterns, framework configurations, and validation steps when creating mocks.

Automates guidance on identifying and responding to model drift within ML deployment workflows, offering best practices, production-ready code, and validation support so you can keep inference systems stable.