Skill UI

This skill guides the process of transforming complex, raw data into compelling, actionable narratives. It covers the core structure (Setup, Conflict, Resolution), narrative arcs, and the three pillars (Data, Narrative, Visuals) needed to create high-impact presentations for executives, investors, and non-technical stakeholders. It ensures insights drive clear decisions.

Plotly is a Python graphing library for interactive, publication-quality visuals that bring hover, zoom, animation, and dashboard-ready exports to exploratory analysis and presentations.

Plotly is a powerful Python graphing library designed to create highly interactive, publication-quality visualizations. It supports over 40 chart types, including scatter plots, heatmaps, candlestick charts, and geographical maps. Ideal for building sophisticated dashboards, conducting deep exploratory data analysis, and creating rich, web-embeddable presentations with features like zooming, panning, and hover tooltips.

DeepChem is a comprehensive Python library designed to apply machine learning to chemistry, materials science, and biology. It enables sophisticated molecular property prediction (ADMET, solubility, toxicity), drug discovery, and materials design. The library supports diverse featurization methods (fingerprints, graph representations) and advanced model architectures, including GNNs and transfer learning, making it ideal for analyzing complex chemical datasets like MoleculeNet.

This skill automatically processes MP4 videos by extracting key frames at configurable intervals. It then applies Tesseract OCR to recognize text on these frames, generating a comprehensive, structured Markdown report. The report includes full video metadata and timestamped, searchable transcripts, making it ideal for analyzing lectures, presentations, or screencasts programmatically.

This skill generates a comprehensive budget variance analysis report. It takes actual and budgeted figures, identifies variances exceeding a materiality threshold, and provides structured commentary. The output includes variance summaries, detailed root cause analysis (distinguishing between permanent and timing issues), recommended management actions, and revised full-year financial forecasts, suitable for C-suite presentations or board packs.

A comprehensive guide for researchers developing models in decision theory under risk and uncertainty. It outlines how to strengthen theoretical representations by ensuring the model is anchored by clear axiomatic foundations, a mathematically tractable functional form, and crucially, verifiable behavioral content (i.e., what the model predicts or forbids). This helps bridge the gap between abstract theory and empirical data.