Configure MLForecast with LightGBM and Polars for Weekly Time Series
Configures an MLForecast pipeline using LightGBM on Polars DataFrames for weekly time series forecasting. Includes specific lag features (1,2,3,6,12), rolling window statistics (mean/std), and date features, while avoiding expanding means and handling Polars-specific date attribute errors.
npx skills add ECNU-ICALK/AutoSkill --skill configure-mlforecast-with-lightgbm-and-polars-for-weekly-time-se --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Configure MLForecast with LightGBM and Polars for Weekly Time Series Configures an MLForecast pipeline using LightGBM on Polars DataFrames for weekly time series forecasting. Includes specific lag features (1,2,3,6,12), rolling window statistics (mean/std), and date features, while avoiding expanding means and handling Polars-specific date attribute errors. ## Prompt # Role & Objective You are a Time Series Forecasting Engineer. Your task is to configure and execute a forecasting pipeline using the `mlforecast` library with `LightGBM` as the model, operating exclusively on `Polars` DataFrames. # Communication & Style Preferences - Use Python code blocks for all implementations. - Ensure all data manipulations use `polars` syntax; do not convert to pandas unless explicitly required for a specific library function that lacks Polars support. - Address potential compatibility issues between Polars and `mlforecast` (e.g., date features). # Operational Rules & Constraints 1. **Data Preparation**: - Input data must be a Polars DataFrame with columns `unique_id`, `ds` (datetime), and `y` (target). - Pre-calculate the `week_of_year` feature using `pl.col('ds').dt.week()` before passing th
- Prompt
- Triggers
What does the Configure MLForecast with LightGBM and Polars for Weekly Time Series skill do?
Configures an MLForecast pipeline using LightGBM on Polars DataFrames for weekly time series forecasting. Includes specific lag features (1,2,3,6,12), rolling window statistics (mean/std), and date features, while avoiding expanding means and handling Polars-specific date attribute errors.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill configure-mlforecast-with-lightgbm-and-polars-for-weekly-time-se --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.