Skill UI

A Python library for molecular machine learning, offering diverse featurizers, MoleculeNet benchmarks, graph neural networks, and pretrained models for property and ADMET prediction.

End-to-end analysis toolkit for Neuropixels extracellular recordings using SpikeInterface. Supports loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift correction, spike sorting with Kilosort4 and other algorithms, quality metrics computation, and AI-assisted unit curation for publication-ready neural data analysis.

TorchDrug is a modular PyTorch library for graph learning tasks including molecular property prediction, molecule generation, and knowledge graph reasoning. It provides datasets, models, and training engines for building workflows in computational chemistry and biology. Ensure compatibility with PyTorch 1.8-2.0 and Python 3.7-3.10 before implementation.

Master building robust AI workflows with Anthropic's core APIs. This guide details two powerful patterns: real-time streaming via SSE events for interactive chat UIs, and the cost-efficient Message Batches API for asynchronously processing up to 100,000 requests. Includes comprehensive code examples for Python and TypeScript.

This guide provides comprehensive strategies to significantly reduce spending on the Anthropic Claude API. Key techniques include model routing (selecting the most cost-effective model for the task), prompt caching for repeated prompts, using Message Batches for asynchronous tasks, and implementing real-time spend tracking and budget alerts. Essential for managing large-scale, high-cost AI deployments and optimizing cloud LLM expenditures.

A comprehensive guide providing minimal, working examples for integrating with the Anthropic Claude Messages API. Covers core functionalities including basic text completion, advanced vision capabilities for image analysis, and real-time streaming responses. Ideal for developers starting a new Claude integration or testing core API patterns.

A comprehensive guide detailing common pitfalls and anti-patterns encountered when integrating with the Anthropic Claude API. It highlights crucial differences from other models (e.g., OpenAI), such as the mandatory nature of `max_tokens`, correct handling of system prompts, and precise response parsing techniques. Essential for developers debugging subtle API integration issues and ensuring best practices.

This guide teaches developers how to implement robust, event-driven architecture patterns with the Anthropic Claude API. It covers two key methods: Server-Sent Events (SSE) for real-time streaming responses, and the Message Batches API for high-volume, asynchronous bulk processing. Use this when building real-time chat features or processing large datasets offline.

A comprehensive guide to minimizing expenses when utilizing the Anthropic Claude API. Learn advanced strategies including right-sizing models (Haiku, Sonnet, Opus), prompt caching (up to 90% savings), message batching, and effective token reduction techniques. Essential for building cost-efficient AI applications.

This guide provides comprehensive, step-by-step instructions for installing and authenticating the Anthropic SDK. It covers setup for both Python and Node.js environments, detailing how to securely configure API keys using environment variables. Essential for developers starting new projects that integrate with Claude models.

This guide covers advanced usage of the Anthropic Messages API. Learn how to implement real-time streaming responses, utilize system prompts for persona control, process visual inputs (Vision), and enforce structured data output (JSON). This is essential for building sophisticated, robust, and multi-modal Claude-powered applications.

This skill provides the comprehensive workflow for interacting with the Anthropic Messages API. It covers advanced features including streaming responses, multimodal vision input, generating structured JSON output using system prompts, and managing multi-turn conversations. It redirects users to the current, primary guide for all Claude API patterns.