Skill UI

Implements standardized function/tool calling across various large language models (LLMs) via OpenRouter. This unified approach allows developers to build sophisticated, multi-step agents and complex workflows, regardless of the underlying provider (e.g., GPT-4o, Claude 3.5). It simplifies the process of achieving structured output and tool-augmented reasoning in production systems.

This guide provides a comprehensive quickstart for integrating with OpenRouter, a unified endpoint for accessing multiple large language models (LLMs). It includes examples in cURL, Python, and TypeScript, demonstrating how to send chat completion requests, handle complex JSON responses, manage model parameters, and calculate usage cost across various providers (e.g., Llama, GPT-4o, Claude). Ideal for developers testing API connectivity or building multi-model applications.

A comprehensive guide detailing common real-world mistakes and pitfalls when integrating with the OpenRouter API. Covers critical best practices for robust LLM deployment, including correct model ID formatting, cost control via max_tokens, secure API key management, handling rate limits, and optimizing client connection reuse. Essential for developers building multi-provider AI applications.

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 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.

Provides comprehensive patterns for creating production-grade client wrappers when integrating with OpenRouter via the OpenAI SDK. This skill covers handling advanced topics such as robust retry mechanisms, structured typing, caching strategies, and middleware implementation. It is essential for developers building stable, reliable SDKs or commercial client libraries that require enterprise-level API robustness.

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.

A comprehensive guide to implementing production-ready patterns for PostHog analytics integration. This includes establishing singleton clients for both server-side (Python/Node) and client-side environments. It provides type-safe event capture utilities, React hooks for handling feature flags (including variants), and the full Next.js App Router setup for robust pageview tracking. Patterns are available for both TypeScript/JavaScript and Python environments.

A senior specialist for building modern, production-grade React applications using React 19, Server Components, and Next.js App Router. Expertise includes implementing complex state management (Redux, Zustand), optimizing performance with memoization, handling data fetching with TanStack Query, and ensuring strict TypeScript typing and accessibility standards. Ideal for architecting large-scale frontend systems.

Explains how to create route.ts endpoints in the Next.js App Router, covering HTTP methods, request/response handling, streaming, cookies, redirects, CORS, and caching so you can build REST APIs.