Scikit-learn Pipeline with NER and VADER Feature Engineering
Constructs a scikit-learn text classification pipeline that integrates custom feature engineering steps: one-hot encoding of spaCy NER labels for a predefined set of 18 classes and VADER sentiment analysis.
npx skills add ECNU-ICALK/AutoSkill --skill scikit-learn-pipeline-with-ner-and-vader-feature-engineering --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.
# Scikit-learn Pipeline with NER and VADER Feature Engineering Constructs a scikit-learn text classification pipeline that integrates custom feature engineering steps: one-hot encoding of spaCy NER labels for a predefined set of 18 classes and VADER sentiment analysis. ## Prompt # Role & Objective You are a Machine Learning Engineer specializing in Python and scikit-learn. Your task is to construct a text classification pipeline that includes specific custom feature engineering steps for Named Entity Recognition (NER) and sentiment analysis. # Operational Rules & Constraints 1. **Pipeline Construction**: Use `sklearn.pipeline.make_pipeline` to assemble the components. 2. **Custom Transformers**: Use `sklearn.preprocessing.FunctionTransformer` with `validate=False` to wrap custom feature extraction functions. 3. **NER Feature Engineering**: * Assume a spaCy model is loaded as `nlp`. * Create a function (e.g., `perform_ner_label`) that accepts a text string. * The function must generate a binary feature vector (list of 0s and 1s) for the following specific 18 NER labels: `['PERSON', 'NORP', 'FAC', 'ORG', 'GPE', 'LOC', 'PRODUCT', 'EVENT', 'WORK_OF_ART', 'LAW', 'LANGUAGE', 'DATE', 'TIM
- Prompt
- Triggers
What does the Scikit-learn Pipeline with NER and VADER Feature Engineering skill do?
Constructs a scikit-learn text classification pipeline that integrates custom feature engineering steps: one-hot encoding of spaCy NER labels for a predefined set of 18 classes and VADER sentiment analysis.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill scikit-learn-pipeline-with-ner-and-vader-feature-engineering --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.