Skill UI

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.

This guide provides comprehensive instructions for installing and configuring the official Mistral AI SDK (for both Python and Node.js). It covers setting up API keys via environment variables, verifying the connection, and implementing secure credential storage using Secret Managers (GCP/AWS) for production environments. Ideal for starting any new Mistral AI project.

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.

A comprehensive guide for migrating existing AI integrations from providers like OpenAI or Anthropic to Mistral AI. This blueprint covers the entire lifecycle: initial assessment of AI touchpoints, detailed model mapping (e.g., gpt-4o to mistral-large), implementing a provider-agnostic adapter pattern, and executing controlled, feature-flag-controlled rollout plans for major refactoring projects.

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 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 advanced techniques for optimizing Mistral AI API usage, focusing on latency reduction and throughput maximization. Learn to implement robust caching strategies, utilize streaming responses (SSE), select the optimal model for specific use cases (e.g., Mistral Small for chat, Codestral for code), manage concurrent requests using rate limiting, and efficiently use the Batch API. Essential for building high-performance, production-grade AI applications.

This comprehensive checklist guides the safe and robust deployment of Mistral AI integrations to a production environment. It covers critical operational steps, including secure credential management, rigorous code quality gates, establishing health endpoints, implementing circuit breakers for resilience, executing gradual rollouts, and defining emergency rollback procedures to ensure system stability and high availability.

Provides comprehensive strategies for managing API rate limits (RPM/TPM) when integrating with Mistral AI. This guide demonstrates implementing token-aware rate limiters, exponential backoff, and robust retry mechanisms using `Retry-After` headers, ensuring high throughput and stable application performance for large-scale LLM applications.

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 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.

A comprehensive guide detailing essential security measures for integrating Mistral AI models. Covers API key management, defending against prompt injection, implementing content moderation, sanitizing outputs, and secure logging practices, ensuring robust and safe application development.