AgentSkillsCN

data_science

在建模、探索性数据分析（EDA）及特征工程领域，采用带有强烈主观倾向的数据科学默认配置。当您着手构建机器学习模型、开展探索性数据分析、进行特征工程，或评估模型性能时，此技能将助您事半功倍。但请勿将其用于常规软件工程开发，或非机器学习相关的Python项目。

SKILL.md
--- frontmatter
name: data_science
description: >
  Apply opinionated data science defaults for modeling, EDA, and feature engineering.
  Use when building ML models, doing exploratory data analysis, feature engineering,
  or evaluating model performance. Do NOT use for general software engineering or
  non-ML Python work.

Defaults

  • •Python preferred (R only if explicitly requested)
  • •Libraries: Polars (preferred; fast, typed, pandas-compatible API), NumPy, SciPy, Scikit-Learn, XGBoost, Seaborn, Matplotlib
  • •Environment: uv for deps, ruff/pyright/pytest

Modeling

  • •Default model: XGBoost with tree_method="hist", low learning rate, early stopping
  • •Validation: Nested — 20% holdout + 10-fold CV on training set
  • •Feature importance: Use pred_contribs=True in XGBoost (NOT shap package)

Prohibitions

  • •No shap package — use XGBoost's built-in pred_contribs=True
  • •No SVMs (don't scale)
  • •No neural nets unless explicitly requested
  • •No TensorFlow (use PyTorch if needed)
  • •No Plotly (use Seaborn/Matplotlib)
  • •No SMOTE or upsampling (use scale_pos_weight or sample_weight)

EDA Checklist

Numeric columns: count, missing %, min/max, mean/median, quantiles (5/25/50/75/95), std Categorical columns: cardinality, top 5/bottom 5 values, missing % Text columns: cardinality, length distribution (min/median/max chars) Target: distribution, class balance (if classification)

Feature Engineering

  • •Categoricals: OrdinalEncoder (not one-hot for tree models)
  • •Text: sentence-transformers embeddings preferred, TF-IDF as fallback
  • •Dates: extract year, month, day_of_week, is_weekend, days_since_reference
  • •Missing: let XGBoost handle natively (don't impute for tree models)

Metrics

Regression: RMSE + R-squared + FVE (fraction of variance explained) Classification: log loss + confusion matrix + FVE-binomial Unsupervised: Calinski-Harabasz or Davies-Bouldin for cluster quality

Unsupervised

  • •PCA / TruncatedSVD for dimensionality reduction
  • •K-Means++ for clustering
  • •Always report explained variance ratio for PCA

Artifacts

Save all outputs to report_artifacts/:

  • •Model files, feature importance plots, validation curves, confusion matrices
  • •Produce Quarto or Pweave reports when applicable