Data Visualization Agent Skills

Transforms A/B test and product experiment data into actionable readouts with hypothesis, metrics, interpretation, and decision.

Generates spectrograms and multi-panel audio feature visualizations from audio files via a command-line tool.

Creates interactive, publication-quality charts with 40+ types, hover tooltips, zoom, and web-embeddable exports for dashboards and exploratory analysis.

Crea gráficos profesionales con Matplotlib, Seaborn y Plotly, desde exploración rápida hasta visualizaciones publicables, eligiendo el tipo de gráfico adecuado para cada historia de datos.

Builds operational monitoring dashboards for Grafana, SigNoz, and similar platforms, focusing on answering operator questions about health, bottlenecks, and changes rather than displaying every metric.

Analyzes uploaded data files with full Python access, producing cleaned datasets, statistical reports, charts, and dashboards via the CellCog coding agent.

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, dimensionality reduction, clustering, marker gene identification, and visualization.

Create data-driven charts with Vega-Lite and Vega, covering bar, line, scatter, heatmap, area, radar, and word cloud visualizations from structured data arrays.

Create publication-quality statistical graphics directly from tabular datasets, covering relational, distribution, categorical, regression, and matrix plots with minimal code.

Create publication-quality statistical graphics using Seaborn, with dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures.

Creates, manipulates, and analyzes complex networks and graphs using the NetworkX Python package, including graph algorithms, synthetic network generation, I/O, and visualization.

Create publication-quality statistical graphics in Python with dataset-oriented plotting, semantic mapping, and built-in statistical estimation.

Build, review, debug, or test declarative statistical visualizations in Python with Altair and Vega-Lite, including chart marks, typed encodings, transforms, parameters, layers, facets, and specification export.

Build, verify, and debug interactive Python visualizations with Plotly, including Plotly Express, graph_objects, subplots, facets, hover/customdata, axes, legends, FigureWidget events, and HTML/image export.