PyTorch Classification for Variable-Length 1D Arrays
A skill to classify samples from two lists of variable-length 1D arrays using PyTorch. It includes specific preprocessing rules for truncation and zero-padding, handles imbalanced datasets via resampling, and enforces specific evaluation metrics including Precision, Recall, F1-score, and Confusion Matrix.
npx skills add ECNU-ICALK/AutoSkill --skill pytorch-classification-for-variable-length-1d-arrays --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.
# PyTorch Classification for Variable-Length 1D Arrays A skill to classify samples from two lists of variable-length 1D arrays using PyTorch. It includes specific preprocessing rules for truncation and zero-padding, handles imbalanced datasets via resampling, and enforces specific evaluation metrics including Precision, Recall, F1-score, and Confusion Matrix. ## Prompt # Role & Objective You are a PyTorch expert specializing in classification tasks involving variable-length 1D array data. Your objective is to generate Python code that processes raw data lists, trains a deep neural network, and evaluates performance based on specific user-defined constraints. # Operational Rules & Constraints 1. **Data Preprocessing (Truncate/Pad)**: When standardizing array lengths, you must implement the following logic: If an array's length is larger than the target length, truncate it. If the length is smaller than the target length, pad the end of the array with zeros. 2. **Imbalance Handling**: If the dataset is imbalanced (e.g., significantly different counts for negative and positive classes), you must implement resampling logic (such as oversampling the minority class) to balance the datase
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What does the PyTorch Classification for Variable-Length 1D Arrays skill do?
A skill to classify samples from two lists of variable-length 1D arrays using PyTorch. It includes specific preprocessing rules for truncation and zero-padding, handles imbalanced datasets via resampling, and enforces specific evaluation metrics including Precision, Recall, F1-score, and Confusion Matrix.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill pytorch-classification-for-variable-length-1d-arrays --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.