Skill UI

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

The official Java SDK for Azure Cosmos DB, providing comprehensive client capabilities for NoSQL operations. It supports global distribution, multi-model data structures, and reactive patterns (async/await). Ideal for developing high-throughput, scalable enterprise applications in Java that require reliable, low-latency data storage.

A comprehensive guide and client library for interacting with Azure Cosmos DB, a globally distributed, multi-model NoSQL database, using Python. It provides full support for performing document CRUD operations, executing complex partitioned and cross-partition queries, managing containers, and handling advanced concepts like throughput and asynchronous operations.

Master expert patterns for building robust serverless applications using Azure Functions. This guide covers multi-language support (.NET, Python, Node.js), including the isolated worker model, Durable Functions orchestration, cold start optimization, and implementing various triggers (HTTP, Timer, Blob). Essential for developing scalable, production-ready cloud backend services.

Master expert patterns for building robust serverless applications using Azure Functions. This guide covers multi-language support (.NET, Python, Node.js), including the isolated worker model, Durable Functions orchestration, cold start optimization, and implementing various triggers (HTTP, Timer, Blob). Essential for developing scalable, production-ready cloud backend services.

This skill provides a comprehensive and isolated setup for managing Deepgram API integrations across different deployment stages (Development, Staging, Production). It includes typed configuration management, a client factory pattern, and Docker Compose profiles, ensuring that each environment uses specific credentials, models, and feature settings (like diarization or PII redaction) without conflict. Ideal for robust, multi-stage application deployment.

This skill provides guidance and patterns for generating structured JSON files that define complex workflow pipelines. It is essential for chaining multiple distinct AI models together, enabling multi-step, automated data processing. Use this when a single task requires a sequence of interconnected AI operations or services.

Generate complete, production-ready REST API implementations from existing OpenAPI specifications or database schemas. This skill scaffolds all necessary CRUD endpoints (GET, POST, PUT, DELETE) across multiple frameworks (Express, FastAPI, Spring Boot, etc.), automatically incorporating features like pagination, filtering, validation, and robust authentication middleware. Ideal for rapidly bootstrapping complex backend services from defined data models.

This guide details how to implement robust, multi-layered Role-Based Access Control (RBAC) for managing LLM API calls to Groq in an enterprise setting. It covers best practices for API key scoping, application-level authorization (model selection, token limits), rate limiting, and detailed spending control mechanisms, ensuring secure and cost-effective usage across multiple teams.

A comprehensive guide to configuring and managing Groq API access across development, staging, and production environments. This skill establishes environment-specific configurations, ensuring optimal model selection, rate limit handling, and secure secret management (using AWS, GCP, Vault) for robust and scalable LLM integrations.

A comprehensive guide for configuring API access across development, staging, and production environments. This setup ensures strict isolation of API keys and environment-specific parameters (like models, speed, and caching). It covers best practices for secret management using major CI/CD and secret services (GitHub Actions, AWS Secrets Manager, GCP Secret Manager).