Skill UI

This comprehensive guide details the deployment process for Mistral AI-powered applications across various cloud and container platforms, including Vercel, Docker, Google Cloud Run, and self-hosted vLLM setups. It covers secure API key management, platform-specific secret configuration, and the full steps required to establish robust, scalable production pipelines for your AI model.

A comprehensive operational runbook for managing outages and errors related to Mistral AI integrations. It provides structured procedures, including severity classification, quick API health checks, decision trees for common errors (401, 429, 5xx), immediate mitigation steps, and post-incident evidence collection and reporting.

A comprehensive guide to establishing a fast, reproducible local development lifecycle for Mistral AI integrations. This workflow includes setting up project scaffolding, implementing hot reloading with tsx, performing unit tests with Vitest mocking, and executing live integration tests against the Mistral API. Ideal for setting up reliable AI application development environments.

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.

A comprehensive guide to implementing advanced asynchronous patterns with Mistral AI. This skill covers using the Agents API for stateful, multi-turn workflows, the Batch API for cost-effective bulk inference, and event-driven streaming architectures. It is essential for building robust, long-running, or high-volume AI applications that require reliable background job handling.

Controls Mixpanel analytics via Rube MCP to aggregate events, run segmentation, funnels, cohorts, profiles, and JQL/insight queries while ensuring schema discovery precedes automation.

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.

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

Documenting React Native and Flutter patterns for building responsive, performant product lists with typed navigation, safe areas, and platform-aware styles, plus anti-patterns and checklist to keep JS threads smooth.

Automates Mocean operations through Composio’s Mocean toolkit via Rube MCP, enforcing a workflow that always queries available tools, verifies Mocean connections, reuses sessions, and includes explicit memory placeholders before executing multi-tool plans.

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.

Automates Moco workflows through the Composio toolkit via the Rube MCP interface, encouraging discovery, connection validation, and schema-compliant tool execution.