Skill UI

This skill provides comprehensive guidance and established patterns for conducting rigorous scientific research and complex data analysis. It helps users structure their thinking process, ensuring a systematic and methodical approach to scholarly inquiry. Ideal for academic projects, literature reviews, and complex data interpretation.

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature extraction on temporal data.

Automates detection and deep exploratory analysis across 200+ scientific data formats, assessing quality, extracting metadata, and recommending visualizations while generating markdown reports that guide downstream workflows and highlight structure and distributions.

Guides structured hypothesis generation by combining literature searches, evidence synthesis, competing explanations, and experimental design, with mandatory AI-generated scientific schematics for complete reports.

Structured manuscript and grant review guidance that walks reviewers through methodology, statistics, reporting compliance, and constructive feedback for rigorous scientific evaluation across disciplines.

Conversational research ideation assistant that helps scientists generate novel hypotheses, explore interdisciplinary analogies, challenge assumptions, and outline experiment pathways during early-stage planning.

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.

scikit-bio provides a Python-native biological data toolkit for reading/writing FASTA/Newick files, manipulating sequences, building alignments and phylogenies, and calculating diversity metrics for microbiome and ecology workflows.

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.

SciPy provides open-source algorithms and utilities for scientific computing, including integration, optimization, statistics, and signal processing, enabling researchers and engineers to build reliable numerical applications.