"Use Hummingbird with optional source model ecosystems: LightGBM,
Scanned 9/8/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill optional-source-models --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: optional-source-models
description: "Use Hummingbird with optional source model ecosystems: LightGBM,
XGBoost, SparkML, Prophet, and optional ONNX-ML tooling dependency checks."
disable-model-invocation: true
metadata:
disco-role: operating
license: MIT
---
# Optional Source Models
Load this sub-skill when a Hummingbird task involves optional source-model ecosystems or optional tooling dependencies, especially:
- LightGBM to PyTorch/TorchScript/ONNX.
- XGBoost `XGBClassifier`, `XGBRegressor`, or `XGBRanker`, particularly when `test_input` or feature-count inference is failing.
- SparkML models, transformers, or pipelines that use Spark DataFrames as conversion input.
- Prophet conversion to PyTorch or ONNX.
- Dependency questions around `hummingbird-ml[extra]`, `hummingbird-ml[sparkml]`, `hummingbird-ml[onnx]`, or `hummingbird-ml[benchmark]`.
- Optional-library failures such as OpenMP/libgomp/libomp/cmake errors.
## Start here
1. Check dependency availability with the bundled probe:
[`scripts/check_optional_sources.py`](scripts/check_optional_sources.py).
2. Use the source-family workflows and caveats in
[`references/optional-source-models.md`](references/optional-source-models.md).
3. Use the extras and import-gate matrix in
[`references/dependency-matrix.md`](references/dependency-matrix.md).
4. Use OS-specific and source-family troubleshooting in
[`references/troubleshooting.md`](references/troubleshooting.md).
## Route elsewhere
- For core `hummingbird.ml.convert` / `convert_batch` syntax on ordinary scikit-learn-style models, load [`../core-conversion/SKILL.md`](../core-conversion/SKILL.md).
- For ONNX backend output, ONNX-ML source-model details, model save/load, and container I/O, load [`../onnx-and-model-io/SKILL.md`](../onnx-and-model-io/SKILL.md).
- For CUDA, TVM, TorchScript performance tuning, threading, and batch-shape performance issues, load [`../advanced-backends-and-performance/SKILL.md`](../advanced-backends-and-performance/SKILL.md).
## Important constraints
- Optional source packages are not part of the minimal Hummingbird runtime. Treat missing LightGBM, XGBoost, Prophet, SparkML, and TVM as expected until the user requests those workflows.
- Hummingbird builds optional supported-operator lists at Python import time. If optional packages were installed after importing Hummingbird, restart the Python process before diagnosing missing converters.
- Do not claim optional source-family native verification unless the user's environment actually has the relevant optional packages and the conversion has been exercised.

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