Data Visualization

dashboard-builder helps you turn metrics into a practical operational dashboard for Grafana, SigNoz, or similar tools. Use the dashboard-builder skill when you need a clear dashboard-builder guide for health, bottlenecks, throughput, and action-focused panels instead of a vanity board.

visualization-expert is a lightweight skill for chart selection, visualization best practices, and example matplotlib or plotly code. Use it to choose better charts, critique dashboards, and apply clear, accessible data visualization guidance from a single SKILL.md file.

grafana-dashboards helps agents design production Grafana dashboards for observability. Use it to plan RED and USE-based layouts, choose panel hierarchy, and draft dashboard structure for Prometheus-style metrics.

Use the data-storytelling skill to turn analysis into decision-ready narratives for reports, executive updates, and stakeholder communication with clear structure and action.

The kpi-dashboard-design skill helps teams plan decision-focused KPI dashboards with metric selection, dashboard hierarchy, chart patterns, and governance guidance for executive, tactical, and operational views.

Use the sympy skill for exact symbolic math in Python, including algebra, calculus, matrices, physics formulas, number theory, geometry, and code generation. It helps you keep expressions exact, choose the right SymPy modules, and avoid float-heavy mistakes. Best for users who need a practical sympy guide for symbolic workflows and sympy for Data Analysis.

qutip is a Python quantum physics simulation skill for open quantum systems, dissipation, time evolution, and quantum optics. Use this qutip guide for master equations, Lindblad dynamics, decoherence, cavity QED, state/operator simulation, and Scientific Python examples. Not for circuit-based quantum computing.

metrics-dashboard helps you define and design a product metrics dashboard with the right KPIs, visualizations, and alert thresholds. Use it to plan what to measure, how to group metrics, and which signals should trigger action for product, growth, or analytics workflows.

Perform cohort-analysis on user retention, engagement decay, and feature adoption by cohort. This cohort-analysis skill is built for Data Analysis workflows that need validation, calculation, visualization, and clear insights from structured user behavior data.

app-analytics helps you set up, interpret, and improve mobile app tracking with a practical measurement plan. Use it to choose the right tools, validate events, connect attribution to outcomes, and support Data Analysis for product, growth, subscriptions, or paid acquisition decisions.

vega is a chart-authoring skill for turning structured data into interactive, data-driven visualizations with Vega-Lite for most cases and Vega for advanced layouts. Use it for bar, line, scatter, heatmap, area, stacked, and multi-series charts when you have real data fields and need valid JSON specs.

The data-analytics skill creates PlantUML diagrams for data analysis workflows, including ETL, ELT, data lakes, warehouses, streaming pipelines, log analytics, and BI dashboards. It is optimized for clear source-to-destination flow, AWS analytics/database stencils, and practical data-analytics guide output—not generic software or cloud architecture diagrams.

shap skill for model interpretability and explainable AI. Use it to understand predictions, compute feature attributions, choose SHAP plots, and debug model behavior for Data Analysis across tree, linear, deep learning, and black-box models.

Seaborn is a seaborn skill for Python statistical visualization with pandas-friendly inputs and strong defaults. Use it for quick exploration of distributions, relationships, categorical comparisons, box plots, violin plots, pair plots, and heatmaps. Built on matplotlib for static, publication-ready charts.

scvelo is a Python skill for RNA velocity analysis in single-cell RNA-seq data. Use it to estimate cell state transitions from unspliced and spliced mRNA, infer trajectory direction, compute latent time, and identify driver genes. It is especially useful for scvelo for Data Analysis when you need directionality beyond standard clustering or pseudotime.

scientific-visualization is a meta-skill for publication-ready figures. Use it for journal submission plots with multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and Nature/Science/Cell-style formatting. It orchestrates matplotlib, seaborn, and plotly for scientific-visualization for Data Visualization work.

scanpy skill for single-cell RNA-seq data analysis in Python. Use it for QC, normalization, PCA, UMAP/t-SNE, clustering, marker gene discovery, trajectory analysis, and publication-quality plots. Best for exploratory scRNA-seq workflows built around AnnData, with clear scanpy usage and install guidance.

networkx is a Python skill for creating, analyzing, and visualizing graphs and complex networks. Use it for networkx usage in shortest paths, centrality, clustering, community detection, graph construction, and networkx for Data Analysis workflows. Best for node-edge data where structure and relationships matter.

matplotlib skill for Python plotting with full control over axes, labels, legends, layouts, and export formats. Use it for scientific figures, multi-panel analyses, custom chart types, and reproducible visualizations when you need more precision than a generic chart prompt. It is a strong matplotlib guide for Data Analysis and publication-ready plots.

The matlab skill helps you generate, debug, and adapt MATLAB or GNU Octave code for matrix operations, data analysis, visualization, statistics, optimization, and scientific computing. Use it for runnable MATLAB usage, MATLAB for Data Analysis, MATLAB-to-Python translation, or Octave-compatible scripts when you need less trial-and-error than a generic prompt.

The infographics skill helps you create publication-ready visuals from a topic, dataset, or narrative. It supports infographics for Data Visualization with Nano Banana Pro generation, Gemini 3 Pro quality review, optional research, accessible palettes, and iterative refinement for marketing, reports, timelines, comparisons, and social layouts.

geopandas skill for Python geospatial vector data analysis, including shapefiles, GeoJSON, and GeoPackage files. Use it to read, clean, join, buffer, clip, reproject, and export spatial data with less guesswork.

etetoolkit is a phylogenetic tree toolkit for ETE workflows. Use the etetoolkit skill to parse, edit, compare, root, prune, and visualize trees in Newick, NHX, PhyloXML, or NeXML. It supports phylogenomics, orthology/paralogy analysis, NCBI taxonomy, and publication-style PDF or SVG output.

The deeptools skill helps with NGS analysis workflows in deepTools: BAM to bigWig conversion, QC, sample comparison, and heatmaps or profile plots for ChIP-seq, RNA-seq, ATAC-seq, and related assays. Use it as a practical deeptools guide when you need reproducible command-line analysis and visualization.