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This comprehensive guide demonstrates the fundamental workflow for creating, retrieving, and managing work orders using the MaintainX REST API. It provides actionable code examples using both cURL for quick testing and TypeScript for robust client implementation. It is essential for developers integrating a CMMS system or testing basic API connectivity.

This comprehensive guide provides step-by-step instructions and complete code examples for setting up and configuring the MaintainX REST API. It covers best practices for generating API keys, setting up secure environment variables, and implementing robust client wrappers in both TypeScript and Python. Use this skill when integrating CMMS data (such as work orders, assets, and locations) into external applications.

A comprehensive guide for configuring and managing multiple, isolated deployment environments (development, staging, production) for SaaS applications. It covers best practices such as using environment variables for configuration, implementing client factories for resource isolation, integrating secure secret management (e.g., GCP Secret Manager), and running validation scripts to ensure consistency and security across all deployment stages.

A comprehensive client library pattern for interacting with rate-limited APIs. This solution implements exponential backoff, robust retry logic (handling 429/5xx errors), concurrent request queuing, and cursor-based pagination. It ensures high throughput and reliable data fetching without exceeding API limits.

This guide provides production-grade patterns for building robust integrations with the MaintainX REST API. It covers essential techniques such as creating type-safe clients using TypeScript, implementing reliable cursor-based pagination, handling API rate limits with exponential backoff retry logic, and performing efficient batch operations. Ideal for developers building scalable, resilient API wrappers.

Used for building, debugging, and extending MCP servers and clients. This skill implements robust connections between AI systems and external tools/data sources using structured protocols like JSON-RPC 2.0. Expertise includes defining tool schemas (Zod/Pydantic), configuring transport layers (HTTP/SSE/stdio), and scaffolding full-stack projects in TypeScript or Python.

Defines MCP server tooling with file search, query execution, resource endpoints, prompt templates, and transport configurations alongside a client setup example for streamlined project automation.

A comprehensive guide for achieving compliance with the EU MDR 2017/745. It covers crucial aspects including medical device classification (Annex VIII), developing technical documentation (Annex II/III), planning clinical evaluation (CER), and establishing post-market surveillance (PMS/PSUR) systems. Ideal for regulatory specialists preparing for Notified Body audits or needing to gap-check technical files.

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 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.

Automates comprehensive monitoring and alerting of HTTP, database, API, background job, and client errors so teams can track, analyze, and resolve reliability issues before they impact users.