qiskit-qward

Expert Python development skill for scientific library design and best practices. Use when writing Python code, designing APIs, creating Pydantic schemas, building abstract base classes, implementing design patterns (Strategy, Factory, Observer), writing type-safe code with type hints, structuring Python packages, creating tests with pytest, managing dependencies, optimizing performance, or following PEP conventions. Tailored for the QWARD Qiskit extension library.

IBM Qiskit quantum computing framework for circuit design, transpilation, execution, and analysis. Use when building quantum circuits, running on IBM Quantum hardware or simulators, running on Rigetti hardware via qBraid or AWS Braket directly, working with Qiskit Runtime primitives (Sampler/Estimator), optimizing transpilation, implementing quantum algorithms (VQE, QAOA, Grover), using QWARD's QuantumCircuitExecutor for simulate/run_ibm/run_qbraid workflows, direct AWS Braket submission with qiskit-braket-provider, noise model generation (IBM Heron R1-R3, Rigetti Ankaa-3), experiment campaigns with async job retrieval, or integrating with the QWARD metrics library. Covers Qiskit v2 primitives, session/batch execution modes, error mitigation, qBraid transpilation, AWS Braket integration, and visualization.

QWARD library for quantum circuit analysis, metrics extraction, performance evaluation, and visualization. Use when analyzing quantum circuits with Scanner, extracting metrics (QiskitMetrics, ComplexityMetrics, FidelityMetrics, ElementMetrics, StructuralMetrics, BehavioralMetrics, QuantumSpecificMetrics), visualizing results with the Visualizer API, implementing custom metric strategies, running experiments with BaseExperimentRunner, using noise model presets (IBM Heron, Rigetti Ankaa), or extending QWARD with custom metrics. Covers the Strategy pattern architecture, Pydantic schema validation, fluent API chaining, and the type-safe visualization system.

Trigger when: (1) User mentions "manim" or "Manim Community" or "ManimCE", (2) Code contains `from manim import *`, (3) User runs `manim` CLI commands, (4) Working with Scene, MathTex, Create(), or ManimCE-specific classes. Best practices for Manim Community Edition - the community-maintained Python animation engine. Covers Scene structure, animations, LaTeX/MathTex, 3D with ThreeDScene, camera control, styling, and CLI usage. NOT for ManimGL/3b1b version (which uses `manimlib` imports and `manimgl` CLI).

Data science skill for scientific visualization, statistical analysis, and machine learning with Python. Use when creating plots (matplotlib, seaborn), performing statistical tests (scipy, statsmodels, pingouin), building ML models (scikit-learn), analyzing QWARD metric DataFrames, generating publication-ready figures, conducting hypothesis testing, regression analysis, time series forecasting, or producing APA-formatted statistical reports. Covers pandas, numpy, matplotlib, seaborn, statsmodels, scipy, and scikit-learn workflows.