How do I install the Xgboost Lightgbm skill?

Run npx skillmds@latest add liujiayi3703/xgboost-lightgbm in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Xgboost Lightgbm safe to use?

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Which AI agents work with Xgboost Lightgbm?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Xgboost Lightgbm free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Xgboost Lightgbm?

liujiayi3703 (@liujiayi3703) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Xgboost Lightgbm by liujiayi3703 — Agent Skill

Name: Xgboost Lightgbm
Author: liujiayi3703

Xgboost Lightgbm

Use when the user works with tabular machine learning using XGBoost, LightGBM, gradient boosting decision trees, feature importance, hyperparameter tuning, model validation, leakage checks, or structured-data prediction tasks.

liujiayi3703 Updated Sep 17, 2026

File contents

XGBoost and LightGBM

Purpose

Use this skill for practical tabular machine learning workflows centered on XGBoost and LightGBM. The current folder is a standard skill entry point; add datasets, notebooks, scripts, or references later as needed.

When To Use

The user has CSV, Excel, parquet, or database-exported tabular data and wants a predictive model.
The user asks about XGBoost, LightGBM, GBDT, ranking, classification, regression, feature importance, SHAP, or hyperparameter tuning.
The user wants to compare boosted-tree models against linear models, random forests, neural nets, or baseline heuristics.

Workflow

Identify target column, prediction type, unit of analysis, time split needs, and leakage risks.
Inspect the dataset schema and missingness before modeling.
Establish a simple baseline metric before tuning.
Use cross-validation or a time-aware split that matches the real deployment setting.
Train a conservative first model, then tune only the parameters that matter for the observed failure mode.
Report metrics, feature importance, leakage concerns, and next experiments.

Quick Defaults

Task	Default metric
Binary classification	ROC-AUC plus precision/recall at an operating threshold
Multi-class classification	macro F1 plus confusion matrix
Regression	MAE plus RMSE
Imbalanced labels	PR-AUC plus class-specific recall
Time-dependent data	backtest or forward-chaining validation

Common Mistakes

Random split on time-dependent or user-level repeated data.
Tuning before establishing a baseline.
Reporting feature importance without checking leakage.
Treating high validation score as enough without error analysis.
Using SHAP explanations without confirming the split and preprocessing are valid.

Local Resources

Place reusable notebooks in notebooks/, scripts in scripts/, and modeling references in references/ if this skill grows.

---name: xgboost-lightgbmdescription: Use when the user works with tabular machine learning using XGBoost, LightGBM, gradient boosting decision trees, feature importance, hyperparameter tuning, model validation, leakage checks, or structured-data prediction tasks.---# XGBoost and LightGBM## PurposeUse this skill for practical tabular machine learning workflows centered on XGBoost and LightGBM. The current folder is a standard skill entry point; add datasets, notebooks, scripts, or references later as needed.## When To Use- The user has CSV, Excel, parquet, or database-exported tabular data and wants a predictive model.- The user asks about XGBoost, LightGBM, GBDT, ranking, classification, regression, feature importance, SHAP, or hyperparameter tuning.- The user wants to compare boosted-tree models against linear models, random forests, neural nets, or baseline heuristics.## Workflow1. Identify target column, prediction type, unit of analysis, time split needs, and leakage risks.2. Inspect the dataset schema and missingness before modeling.3. Establish a simple baseline metric before tuning.4. Use cross-validation or a time-aware split that matches the real deployment setting.5. Train a conservative first model, then tune only the parameters that matter for the observed failure mode.6. Report metrics, feature importance, leakage concerns, and next experiments.## Quick Defaults| Task | Default metric || --- | --- || Binary classification | ROC-AUC plus precision/recall at an operating threshold || Multi-class classification | macro F1 plus confusion matrix || Regression | MAE plus RMSE || Imbalanced labels | PR-AUC plus class-specific recall || Time-dependent data | backtest or forward-chaining validation |## Common Mistakes- Random split on time-dependent or user-level repeated data.- Tuning before establishing a baseline.- Reporting feature importance without checking leakage.- Treating high validation score as enough without error analysis.- Using SHAP explanations without confirming the split and preprocessing are valid.## Local ResourcesPlace reusable notebooks in `notebooks/`, scripts in `scripts/`, and modeling references in `references/` if this skill grows.

Frequently asked questions