Master high-performance rendering for large datasets with Datashader. Use this skill when working with datasets exceeding 100M+ points, optimizing visualization performance, or implementing efficient rendering strategies with rasterization and colormapping techniques.
Master quick plotting and interactive visualization with hvPlot. Use this skill when creating basic plots (line, scatter, bar, histogram, box), visualizing pandas DataFrames with minimal code, adding interactivity and hover tools, composing multiple plots in layouts, or generating publication-quality visualizations rapidly.
Comprehensive Jupyter notebook operations - create, execute, and analyze notebooks with full cell manipulation
Standards for efficient, readable, and performant data manipulation using Python's Pandas library.
Create a lightweight summary of experiment results from a completed (fine-tuned and evaluated) experiment. Use after run-experiment to capture key metrics from the experiment in textual form.
SOTA metrics for evaluating range bar (price-based sampling) financial data. Use when computing Sharpe ratios, risk metrics, ML prediction quality for range bars. TRIGGERS - range bar metrics, evaluate range bars, Sharpe ratio range bars, WFO metrics, walk-forward metrics, BiLSTM evaluation, crypto metrics, daily aggregation, sqrt(7), sqrt(365), PSR DSR MinTRL, IC information coefficient.
Statistical analysis strategies and data exploration techniques
This skill calculates key financial ratios and metrics from financial statement data for investment analysis
Create Mermaid diagrams (activity, deployment, sequence, architecture) from text descriptions or source code. Use when asked to "create a diagram", "generate mermaid", "document architecture", "code to diagram", "create design doc", or "convert code to diagram". Supports hierarchical on-demand guide loading, Unicode semantic symbols, and Python utilities for diagram extraction and image conversion.
Generate Mermaid diagrams for educational content. Use when visualizing concepts, architectures, or workflows.
Comprehensive plotting and visualization in Python - matplotlib (static publication-quality plots), seaborn (statistical visualization), and plotly (interactive plots); includes plot types, customization, best practices, and library selection guidance
Creates modern LaTeX tables with tabularray package for fixed-width columns, proper alignment, and clean syntax. Use when creating tables or working with column layouts.
Use when performing regression analysis with confidence intervals, solving ODEs, fitting models to experimental data, or caching expensive scientific computations - provides convenient wrappers around scipy that automatically calculate confidence intervals and prediction bounds for linear, nonlinear, and polynomial regression
Create Mermaid diagrams and convert them to images. Use when needing to visualize flows, architecture, or data structures.
Audit dictionary usage against the Dictionary Usage Doctrine. Produces a severity-grouped report with minimal refactor suggestions.
Create new community metrics by adding enum values, recording functions, wiring, backfill migrations, and API integration. Use when adding new community metrics, creating metrics, or tracking community activity.
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
PopulationSim provides population-level intelligence using public data sources. Use this skill for ANY request involving: (1) population demographics or profiles, (2) geographic health patterns or disparities, (3) social determinants of health (SDOH), (4) SVI or ADI analysis, (5) cohort definition or specification, (6) clinical trial feasibility, site selection, or enrollment projection, (7) service area analysis, (8) health equity assessment, (9) census data or ACS variables, (10) CDC PLACES health indicators.
Calculate statistical significance for A/B tests. Sample size estimation, power analysis, and conversion rate comparisons with confidence intervals.
