Edge Model Compression
Enables efficient deployment of large ML models on edge devices using techniques like quantization and pruning.
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Security score
The Edge Model Compression skill was audited on Feb 9, 2026 and we found 12 security issues across 2 threat categories, including 4 high-severity. Review the findings below before installing.
Categories Tested
Security Issues
high line 213
Eval function call - arbitrary code execution
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| 213 | model.eval() |
high line 250
Eval function call - arbitrary code execution
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| 250 | original.eval() |
high line 258
Eval function call - arbitrary code execution
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| 258 | quantized.eval() |
high line 488
Eval function call - arbitrary code execution
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| 488 | teacher.eval() |
low line 1335
External URL reference
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| 1335 | - [TensorFlow Model Optimization](https://www.tensorflow.org/model_optimization) |
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External URL reference
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| 1336 | - [PyTorch Quantization](https://pytorch.org/docs/stable/quantization.html) |
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External URL reference
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| 1337 | - [ONNX Runtime](https://onnxruntime.ai/) |
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External URL reference
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| 1341 | - [AutoKeras](https://autokeras.com/) |
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External URL reference
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| 1347 | - [Quantization and Training of Neural Networks](https://arxiv.org/abs/1712.05877) |
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External URL reference
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| 1348 | - [Pruning Filters for Efficient ConvNets](https://arxiv.org/abs/1608.08710) |
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External URL reference
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| 1349 | - [Distilling Knowledge in a Neural Network](https://arxiv.org/abs/1503.02531) |
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External URL reference
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| 1350 | - [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055) |
Scanned on Feb 9, 2026
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Categorydevelopment
UpdatedMay 21, 2026
majiayu000/claude-skill-registry