Skill UI

This comprehensive guide details production-grade architectural patterns for integrating MaintainX with large enterprise systems, including ERPs, SCADA, and data warehouses. It covers best practices for event-driven synchronization using webhooks and Pub/Sub, implementing bi-directional sync gateways, and establishing scalable analytics data pipelines.

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.

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.

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.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

A comprehensive skill for building and maintaining efficient, scalable monorepositories. This involves strategies for enabling code sharing, ensuring consistent tooling across multiple packages and applications, and managing atomic changes. Use this skill for setting up new monorepo architectures, migrating existing repositories, and optimizing complex CI/CD pipelines.

OpenRouter provides a unified API gateway to access leading AI models (OpenAI, Anthropic, Google, Meta, etc.) from a single endpoint. This skill is essential for developers who need to compare model performance (latency, tokens) across different providers, build resilient cross-provider fallback workflows, or manage complex AI pipelines without writing provider-specific code. It supports feature normalization and BYOK (Bring Your Own Key) for high-volume production use.

This skill provides comprehensive reference architectures for building production-grade AI applications using OpenRouter as a unified LLM gateway. It covers three scaling patterns: simple single-service, standard microservice (with caching and budget enforcement), and enterprise event-driven systems (with message queues and workers). Use this when planning system design, reviewing complex AI pipelines, or ensuring scalability and cost control in your LLM integration.

A comprehensive specialist that combines automated static analysis (SonarQube, CodeQL, Semgrep) with advanced AI capabilities. This tool performs multi-layered code reviews assessing security vulnerabilities, performance bottlenecks, architectural coherence, and maintainability across various programming languages, integrating seamlessly into CI/CD pipelines.

This guide details how to configure robust CI/CD pipelines for Perplexity Sonar API integrations using GitHub Actions. It emphasizes best practices for automated testing, including separating costly live integration tests from lightweight mocked unit tests, ensuring code quality while managing API costs and rate limits.

This guide details the process of migrating search and LLM functionalities from legacy APIs (Google CSE, Bing, SerpAPI) to Perplexity Sonar. It demonstrates how to utilize the Strangler Fig pattern to build a robust adapter layer. The primary benefit is consolidating a complex, multi-step pipeline (Search -> Raw Results -> LLM Summarization) into a single, efficient API call, ensuring automatic citation handling and streamlined development.

This skill enables the development of robust, event-driven pipelines interacting with the Perplexity Sonar API. Since Perplexity lacks native webhooks, this system simulates event patterns using advanced techniques: Streaming Server-Sent Events (SSE) for real-time data, job queues (like BullMQ/SQS) for asynchronous batch research, and cron-triggered monitoring for scheduled news and trend alerts. It provides patterns for building complex, reliable, and scalable AI applications.