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Found 354 Skills
Convert CSV/Excel/JSON data into beautiful visualization report pages
Design and build dashboards that track key performance indicators. Select relevant metrics, visualize data effectively, and communicate insights to stakeholders.
Create data-driven charts with Vega-Lite (simple) and Vega (advanced). Best for bar, line, scatter, heatmap, area charts, and multi-series analytics. Use when you have numeric data arrays needing statistical visualization. Vega for radar charts and word clouds. NOT for process diagrams (use mermaid) or quick KPI cards (use infographic).
Map "Profit Growth × Financial Conditions (Financial Environment)" to the "Investment Clock" to determine the current quadrant, whether it has been rotating clockwise or counterclockwise recently, and the position difference compared to the previous cycle.
Install and use chartli to render terminal charts from numeric text files or stdin.
Generate dark-mode-compatible inline SVG data visualization charts for blog posts. Supports horizontal bar, grouped bar, donut, line, lollipop, area, and radar charts with automatic platform detection (HTML vs JSX/MDX). Enforces chart type diversity, accessible markup (role=img, aria-label), source attribution, and transparent backgrounds. Use when user says "blog chart", "generate chart", "data visualization", "svg chart", "blog graph", "visualize data", or when the blog-write workflow identifies chart-worthy data points (3+ comparable metrics, trends, before/after data).
Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).
Transform CSV/Excel data into narrative reports with auto-generated insights, visualizations, and PDF export. Auto-detects patterns and creates plain-English summaries.
R programming for data analysis, visualization, and statistical workflows. Use when working with R scripts (.R), Quarto documents (.qmd), RMarkdown (.Rmd), or R projects. Covers tidyverse workflows, ggplot2 visualizations, statistical analysis, epidemiological methods, and reproducible research practices.
Using Deck.gl in Observable Framework for large-scale geospatial data visualization.
State-of-the-art data visualization for React/Next.js/TypeScript with Tailwind CSS. Creates compelling, tested, and accessible visualizations following Tufte principles and NYT Graphics standards. Activate on "data viz", "chart", "graph", "visualization", "dashboard", "plot", "Recharts", "Nivo", "D3". NOT for static images, print graphics, or basic HTML tables.
Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.