Skill UI

Automates Ocrspace tasks via Composio’s toolkit and Rube MCP, guiding tool discovery, connection checks, schema-compliant execution, and stateful workflows for reliable OCR operations.

Automates Onesignal User Auth tasks by directing Rube MCP to discover current tool schemas, manage toolkit connections, and execute Composio workflows for auth operations.

Automates onnx converter work for ML deployment, providing best-practice guidance on serving, pipelines, monitoring, and optimization while generating production-ready code and configs.

Automate OpenAI API workflows through the Composio MCP integration, covering text and multimodal responses with structured outputs, embeddings, image generation, and model discovery.

A comprehensive workflow designed for clinical professionals to search, retrieve, and synthesize medical evidence. It allows users to query clinical literature with specialized filters (e.g., specialty, evidence level), check potential drug interactions against patient profiles, and look up the latest specialty guidelines from major health organizations (like ACC/AHA, NICE). The output provides structured citations, confidence scores, and evidence grading to support informed clinical decision-making.

This guide provides comprehensive steps to install and authenticate the OpenEvidence SDK/API. It covers setting up environment variables, installing required packages (supporting both Node.js and Python), and verifying the connection using sample code. This is essential for developers integrating OpenEvidence's medical AI capabilities for clinical decision support and evidence-based queries into a new application.

High-performance RLHF framework that combines Ray, vLLM, and ZeRO-3 to accelerate PPO/GRPO/RLOO/DPO training for 7B-70B+ models on distributed GPU clusters, providing a single-stack workflow for reward models and policy optimization.

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.

Optimizes deep learning models by analyzing architecture, data, and metrics to select optimizers, learning rate schedules, and regularization strategies, then outputs optimized code and evaluates accuracy, time, and resource usage.