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Found 35 Skills
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
This skill should be used when analyzing CSV datasets, handling missing values through intelligent imputation, and creating interactive dashboards to visualize data trends. Use this skill for tasks involving data quality assessment, automated missing value detection and filling, statistical analysis, and generating Plotly Dash dashboards for exploratory data analysis.
Build production-grade interactive dashboards with Plotly Dash - enterprise features, callbacks, and scalable deployment
Use when "data visualization", "plotting", "charts", "matplotlib", "plotly", "seaborn", "graphs", "figures", "heatmap", "scatter plot", "bar chart", "interactive plots"
EDA, dashboards, Matplotlib, Seaborn, Plotly, and BI tools. Use for creating visualizations, exploratory analysis, or dashboards.
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Publication-ready matplotlib figures for Nature/high-impact journals and academic papers. Covers bar charts, grouped bars, heatmaps, line/trend plots, forest plots, microscopy-style image panels, schematic + quantitative composites, radar plots, and multi-panel layouts with Nature-style typography (Arial/sans-serif), restrained color systems, and SVG/PDF export conventions. Use when creating scientific figures that must match Nature publication standards. Do NOT use for interactive dashboards (Plotly, Bokeh) or Illustrator/Figma-first infographic workflows.
Use this for exploratory data analysis (EDA), generating visualizations, finding trends, and deriving insights from datasets using Python (Pandas/Seaborn/Plotly) or SQL.
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
Generate financial analytics and insights from ~/Documents/finances/ data. Terminal report shows: net worth trends (30d/90d/1y), asset allocation, liability table with APR and monthly interest, cash flow (last 30 days), Bitcoin detail with sparkline. HTML dashboard: interactive plotly charts for all of the above over time. Use when: reviewing finances, answering questions about net worth, spending patterns, debt paydown progress, Bitcoin holdings, asset allocation. Keywords: financial report, net worth, spending, cash flow, debt, liabilities, bitcoin, how am I doing financially, finances summary, show me my finances, portfolio.
Set up the Python environment for OpenAlgo indicator analysis. Installs openalgo, plotly, dash, streamlit, numba, yfinance, matplotlib, seaborn, and creates the project folder structure.