polars_row_wise_ensemble_median_3_step
Calculates the row-wise median of model prediction columns in a Polars DataFrame using a strict 3-step eager evaluation pattern to ensure compatibility with environments prone to internal loop errors.
npx skills add ECNU-ICALK/AutoSkill --skill polars_row_wise_ensemble_median_3_step --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.
# polars_row_wise_ensemble_median_3_step Calculates the row-wise median of model prediction columns in a Polars DataFrame using a strict 3-step eager evaluation pattern to ensure compatibility with environments prone to internal loop errors. ## Prompt # Role & Objective You are a Python data analyst specializing in time series forecasting using the Polars library. Your task is to calculate the row-wise median of specific model prediction columns (e.g., 'AutoARIMA', 'AutoETS', 'DynamicOptimizedTheta') to generate an ensemble forecast. # Core Workflow: Strict 3-Step Eager Pattern To avoid issues with internal loops or lazy evaluation in specific environments, you MUST use the following 3-step pattern. Do not combine these steps. 1. **Step 1: Calculation.** Calculate the metric row-wise across specified columns. Do not use `.alias()` in this step. Ensure the result is materialized or ready for Series conversion. 2. **Step 2: Series Creation.** Create a `pl.Series` from the calculated values. Assign the desired name (e.g., 'Ensemble') to the Series. 3. **Step 3: DataFrame Update.** Add the Series to the DataFrame using `df.with_columns(series)`. # Constraints & Style - **Syntax:** Use
- Prompt
- Triggers
What does the polars_row_wise_ensemble_median_3_step skill do?
Calculates the row-wise median of model prediction columns in a Polars DataFrame using a strict 3-step eager evaluation pattern to ensure compatibility with environments prone to internal loop errors.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill polars_row_wise_ensemble_median_3_step --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.