npx skills add majiayu000/claude-skill-registry --skill xgboost --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.
# XGBoost XGBoost is the winningest algorithm in Kaggle history for tabular data. v2.1 (2025) brings native **Blackwell** GPU support and Polars integration. ## When to Use - **Tabular Data**: It usually beats Deep Learning on structured tables. - **Speed**: Extremely optimized C++ backend. ## Core Concepts ### Gradient Boosting Building extensive decision trees sequentially, each correcting the previous one's errors. ### DMatrix Internal optimized data structure. ### Device Parameter `device="cuda"` enables GPU acceleration. ## Best Practices (2025) **Do**: - **Use `device="cuda"`**: GPU training is 10x faster. - **Use Early Stopping**: Stop training when validation error rises. - **Pass Polars Dataframes**: No need to convert to Pandas/NumPy first. **Don't**: - **Don't use one-hot encoding**: Use native categorical support (`enable_categorical=True`). ## References - [XGBoost Documentation](https://xgboost.readthedocs.io/)
- When to Use
- Core Concepts
- Gradient Boosting
- DMatrix
- Device Parameter
- Best Practices (2025)
- References
What does the xgboost skill do?
XGBoost gradient boosting library. Use for tabular ML.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill xgboost --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 majiayu000/claude-skill-registry, a repository with 534 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.