Skill UI

This skill provides advanced strategies for managing large language model (LLM) context windows and token usage via OpenRouter. Functions include context window lookup, context-aware model selection, conversation trimming, document chunking for RAG, and prompt caching. Use this when dealing with long conversations or large documents to maximize quality while minimizing API costs and preventing context limit overflow.

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.

Use this tool to query, filter, and compare over 400 available AI models from OpenRouter. It allows users to check pricing, context lengths, model capabilities (e.g., tool calling), and select the best model based on specific needs, ensuring optimal performance and cost efficiency in AI development.

Generate or edit high-quality photos, illustrations, and visual assets using OpenRouter models like Gemini 3.1 Flash and Flux 2 Pro; configure the API key once, then run the scripts for prompts, edits, or batch outputs for presentations, marketing, or general creative needs.

AI-powered search using Perplexity models via LiteLLM and a single OpenRouter API key for real-time, web-grounded answers with citations, ideal for up-to-date research, literature surveys, fact checking, and domain-specific queries beyond model cutoffs.

A powerful tool leveraging OpenRouter and the Gemini 3 Pro Image model to create, modify, and composite high-quality images. Supports three primary use cases: prompt-only generation from text, detailed single-image edits (inpainting/outpainting), and complex multi-image composition. Users can specify resolutions up to 4K, making it suitable for professional creative and design workflows.

Generate and edit images via the OpenRouter Image API, supporting Gemini, Seedream, Recraft, GPT-Image and Riverflow models for photos, illustrations, logos, artwork and reference-based compositing.

Generates or edits single images using advanced AI models via OpenRouter. Accepts descriptive text prompts for text-to-image generation (e.g., concept art, product renders) or an input image for image-to-image editing. Supports various aspect ratios and resolutions, making it ideal for professional visual content creation workflows.

Generates high-quality, short video clips (3-15s) using the Seedance 2.0 model. It accepts structured English prompts, an optional starting frame image, and style/identity reference images to ensure character and visual continuity. Supports multiple cloud backends (OpenRouter, Volcengine, BytePlus), making it ideal for storyboarding, cinematic concept visualization, or short film pre-production.

This skill executes the DeepSWE benchmark, a comprehensive coding-agent evaluation suite. It uses OpenRouter and mini-swe-agent to score various Large Language Models (LLMs) on 113 tasks, providing reproducible results for model comparison, development assessment, and research benchmarking. Use this when a standardized, deep evaluation of LLM coding capabilities is required.

This skill performs multi-baseline counterfactual cost analysis for AI model routing. It compares actual session spending against hypothetical scenarios using single models like Haiku, Sonnet, or Opus. It calculates savings percentages to determine if routing decisions are cost-effective. Negative savings indicate over-escalation, while positive savings show router efficiency. Useful for quarterly reviews, CI gates, and validating routing configurations.