Skill UI

Monitor the adoption, feature usage, and overall Return on Investment (ROI) of AI tools like Windsurf. This skill provides comprehensive analytics, including daily active users, completion acceptance rates, and estimated time saved. It allows teams to set up usage alerts, identify underutilized seats, and build custom extensions to measure deep usage patterns, ensuring maximum team efficiency and quantifying AI impact.

Astropy is a Python library for astronomical research that handles unit-aware quantities, celestial coordinates, FITS files, cosmological models, precise time systems, and tabular data, letting you transform, convert, and analyze observational or theoretical datasets.

Cirq is Google Quantum AI’s Python framework for building, simulating, and deploying noise-aware quantum circuits on both simulators and real hardware, complete with experiment templates, transformation utilities, and hardware-specific toolkits.

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.

PyOpenMS provides Python bindings for OpenMS to drive complete mass spectrometry workflows, covering data IO, spectral processing, feature/peptide-protein identification, quantitation, and metabolomics pipelines.

Qiskit is the world's leading open-source framework for building, optimizing, and executing quantum circuits. It provides comprehensive capabilities for quantum error mitigation, running algorithms on diverse hardware backends (including IBM Quantum and simulators), and supporting applications in chemistry and machine learning. Essential for researchers entering the quantum computing field.

QuTiP is a Python toolbox for simulating open and closed quantum systems, offering solvers for master equations, trajectory methods, visualization, and analysis tailored to quantum research, education, and exploratory physics studies.

This skill provides zero-shot time series forecasting using Google's TimesFM foundation model. It forecasts any univariate time series (like sales, sensor data, or weather) without requiring custom model training. The model delivers point forecasts along with calibrated prediction intervals (quantiles), offering a robust and probabilistic view of future trends. It is ideal for rapid, reliable forecasting across diverse time-based datasets.

Guides growth-stage companies through CISO-level security programs by quantifying risks in dollars, sequencing compliance (SOC 2/ISO 27001/HIPAA/GDPR), architecting zero-trust defenses, leading incident response, and translating budgets for boards or sales conversations.

Analyzes comprehensive fitness logs to track quantitative trends in volume, intensity, and frequency. It tracks progress across multiple disciplines (running, strength) and correlates exercise metrics with vital health indicators like blood pressure and weight, providing personalized, science-backed training and recovery advice.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

Provides a concise quantitative analysis experience for financial or research scenarios by structuring numerical data insights and interpretations.