Skill UI

This guide details the process of building Model Context Protocol (MCP) servers. These servers are crucial for enabling Large Language Models (LLMs) to interact with external APIs and services, facilitating complex, real-world task completion. It provides comprehensive coverage on architectural planning, choosing appropriate languages (Python, TypeScript, C#), and selecting transport mechanisms (HTTP, stdio) for robust integration within enterprise ecosystems like Azure and Foundry.

Provides cross-language patterns for developing memory-safe systems code. This skill covers crucial concepts like RAII, ownership models, smart pointers, and robust resource management. It is essential for preventing common vulnerabilities such as use-after-free, memory leaks, and ensuring reliable resource handling across complex systems.

This skill automates the setup and configuration of Ollama, allowing users to deploy Large Language Models (LLMs) locally. It performs system assessment (OS, GPU, RAM), selects optimal models based on hardware availability, and handles installation across macOS, Linux, and Docker. Use this when you require free, self-hosted AI alternatives, need offline inference capabilities, or wish to eliminate reliance on costly hosted API services.

A comprehensive guide for auditing and ensuring regulatory compliance (SOC2, GDPR, HIPAA) when integrating Large Language Models (LLMs) via OpenRouter. It covers best practices for data handling, access control, PII redaction, implementing robust audit trails, and configuring provider routing to maintain data sovereignty and meet strict industry standards.

A comprehensive guide on implementing failover and fallback mechanisms for Large Language Models (LLMs) when using OpenRouter. This skill covers native model fallbacks, provider routing strategies, and advanced client-side fallback chains. Use this pattern when building mission-critical or highly resilient systems that must maintain service availability even if a primary model or an entire provider goes offline. It ensures robust API integration and graceful degradation.

This guide provides a comprehensive walkthrough for setting up and authenticating with the OpenRouter API. It covers generating secure API keys, configuring environment variables, and initializing clients in Python and TypeScript. Learn best practices for secure integration, including key management, verification, and key rotation, ensuring reliable connectivity to various large language models (LLMs).

This skill provides comprehensive tools to monitor the real-time availability, status, and performance of various large language models (LLMs) accessed via OpenRouter. It supports active health probes, catalog checks, and scheduled monitoring via Python and Bash, ensuring robust fault detection and failover logic for production systems that rely on external AI APIs.

This skill provides comprehensive methods for implementing intelligent model routing using the OpenRouter API. It allows developers to dynamically select the optimal Large Language Model (LLM) based on specific criteria: task complexity (e.g., classification, code generation), required capability (e.g., deep reasoning vs. simple Q&A), cost budget, and latency tolerance. Implement cost-aware, task-based, and complexity-based routing to maximize system efficiency and minimize operational expenses when building multi-model applications.

This skill provides comprehensive best practices and Python code examples for optimizing API calls to Large Language Models (LLMs) via OpenRouter. It covers benchmarking model latency, implementing streaming to achieve low Time-To-First-Token (TTFT), and processing requests concurrently using asynchronous programming (asyncio). Use this when developing real-time, high-throughput applications that require predictable and fast model responses.

This skill implements a sophisticated, config-driven rules engine for OpenRouter API calls. It dynamically determines the optimal large language model (LLM) by evaluating a comprehensive context, including user subscription tier, remaining cost budget, specific task type (code, chat), required tools, and latency requirements. This is essential for building production-grade, cost-aware, and robust LLM orchestration systems.

This skill provides comprehensive guides on implementing real-time streaming responses using OpenRouter's Server-Sent Events (SSE) capability. It covers various languages (Python, TypeScript, FastAPI, JavaScript), enabling developers to build modern, fast-feeling chat interfaces. By streaming tokens as they are generated, it significantly reduces the time-to-first-token (TTFT) and improves user experience for long completions and chat applications.

This comprehensive guide provides developers with step-by-step instructions for migrating applications from direct calls to proprietary Large Language Model (LLM) APIs (such as OpenAI or Anthropic) to utilizing the unified OpenRouter gateway. It details essential code changes, including updating base URLs, managing API keys, adding custom headers, and correctly prefixing model identifiers. This ensures a seamless transition, enabling multi-model compatibility and simplified API management.