Skill UI

Combines ESM3 and ESM C protein language models to generate sequences, predict structures, condition functions, and extract embeddings via local or Forge API inference for protein design and engineering workflows.

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.

Hardware-agnostic quantum ML framework enabling gradient-based training of quantum circuits, hybrid quantum-classical models, and seamless integration with PyTorch/JAX/TensorFlow alongside device portability across IBM/Google/Rigetti/IonQ.

PyHealth is a modular Python toolkit designed for building deep learning pipelines using complex medical data, including EHR records, physiological signals, and medical imaging. It provides a standardized 5-stage workflow: Dataset → Task → Model → Trainer → Metrics. This allows users to train sophisticated models for clinical prediction (e.g., mortality, readmission, drug recommendation) while handling medical code mapping (ICD, ATC, RxNorm) and ensuring proper data splitting for rigorous research.

Provides AI-ready drug discovery datasets, splits, and benchmarks covering ADME, toxicity, DTI, DDI, and molecular generation so researchers can train, compare, and optimize therapeutic ML models with curated splits and oracles.

Guides using SHAP to explain ML predictions, compute feature importance, generate diagnostic plots, debug models, analyze bias, and compare models across tree, deep, linear, or black-box architectures.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

A comprehensive skill for leveraging massive pre-trained transformer models across diverse AI domains, including Natural Language Processing (NLP), Computer Vision (CV), and Audio. Use this skill for advanced tasks such as text generation, classification, question answering, summarization, object detection, and model fine-tuning on proprietary datasets. It provides streamlined pipelines and granular control for deployment.

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.

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.

A comprehensive security auditing framework designed to systematically detect sensitive information disclosure from Large Language Models (LLMs). It uses multi-phase, escalating dialogue probes to test for leaks such as system prompts, API keys, PII, and internal configurations. Essential for red teaming and rigorous model security assessment.