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Research ideation partner. Generate hypotheses, explore interdisciplinary connections, challenge assumptions, develop methodologies, identify research gaps, for creative scientific problem-solving.
Debug package usage guide. Use when adding debug logging, understanding log namespaces, or implementing debugging features. Triggers on debug logging requests or logging implementation.
Use when customizing gluestack-ui themes and design tokens. Covers theme provider setup, design tokens, dark mode, NativeWind integration, and extending themes.
Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
Deep codebase analysis to generate 8 comprehensive documentation files. Adapts based on path choice - Greenfield extracts business logic only (tech-agnostic), Brownfield extracts business logic + technical implementation (tech-prescriptive). This is Step 2 of 6 in the reverse engineering process.