Skeights
Skeights serializes fitted scikit-learn models to safetensors and JSON, replacing insecure pickle with a safe, inspectable format.
Skeights
What is Skeights?
Skeights vs Similar AI Tools
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How to use Skeights?
- 1Install skeights via pip: pip install skeights
- 2Import skeights and your sklearn estimator
- 3Fit your model as usual
- 4Save using skeights.save(estimator, "model.safetensors", "model.json")
- 5Load using skeights.load("model.safetensors", "model.json")
Skeights Key Features
- Human-readable JSON configuration for hyperparameters
- Safe safetensors format for numeric weights
- Supports many sklearn estimators including pipelines, trees, and boosting models
- No pickle or joblib dependency
- Forward-compatible loading across sklearn versions on a best-effort basis
- Recursive hyperparameter extraction and setting
Skeights Use Cases
- Saving ML models securely without arbitrary code execution risk
- Version control for model hyperparameters via diffable JSON
- Inspecting model configurations without loading full weights
- Deploying models to production with safe serialization
Skeights Pricing & Free Credits
Skeights currently operates on a Free model.
This tool is completely free to use
Skeights Pros & Cons
Pros
- Secure: no pickle, no code execution risk
- Inspectable: hyperparameters in human-readable JSON
- Compatible with many sklearn estimators and pipelines
- Easy to use with simple save/load API
- Open-source with MIT license
Cons
- Requires scikit-learn >= 1.5
- Limited to supported estimators (not all sklearn models)
- Cross-version compatibility not guaranteed
What is Skeights best for?
- Developers needing secure sklearn model serialization
- ML engineers wanting diffable model configurations
- Teams using safetensors for weight storage
Skeights FAQ
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