From air-quality-toolkit
Use when a user has a CSV of air quality measurements and wants to visualize trends, patterns, or create a short-term forecast.
npx claudepluginhub danielrosehill/claude-code-plugins --plugin air-quality-toolkitThis skill uses the workspace's default tool permissions.
Time-series analysis and visualization for pollutant concentrations. Generate wind-dependent polar plots, calendar heatmaps, time-of-day patterns, and optional forecasts.
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Time-series analysis and visualization for pollutant concentrations. Generate wind-dependent polar plots, calendar heatmaps, time-of-day patterns, and optional forecasts.
datetime column (ISO 8601, e.g., 2026-04-20T14:30:00Z) and one or more pollutant columns (e.g., PM2.5, O3, NO2, value)wind_speed, wind_dir (degrees, 0–360), temp, humiditytime-variation, polar-plot, calendar-plot, forecast — or list what the user wants to explore (e.g., "show me hour-of-day patterns and any weekly cycle")Load CSV; validate that datetime column exists and is parseable; stop with helpful error if not.
Parse datetime to UTC; warn if any rows are missing or non-numeric pollutant values (offer to drop or interpolate).
Based on user's requested analysis, execute one or more of:
Time Variation:
timeVariation().Polar Plot:
wind_speed and wind_dir columns; stop with guidance if missing.polarPlot().Calendar Plot:
Simple Forecast:
statsmodels (ARIMA) or prophet installed; print install hint if missing.date, pollutant, forecast, lower_ci, upper_ci (95% CI).Write outputs:
{output_path}/{analysis_type}_{pollutant}.png{output_path}/{analysis_type}_{pollutant}.csv (summary stats, forecast, etc.)Print summary of outputs generated and their file locations.
Example filenames:
time-variation_PM2.5.png
polar-plot_PM2.5.png
calendar-plot_PM2.5.png
forecast_PM2.5.csv
pandas, matplotlib, numpy), print install command and stop gracefully.