From omer-metin-skills-for-antigravity-2
Reduces model size, improves inference speed, and prepares models for edge deployment using quantization, pruning, knowledge distillation, ONNX export, and TensorRT optimization.
How this skill is triggered — by the user, by Claude, or both
Slash command
/omer-metin-skills-for-antigravity-2:model-optimizationThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
npx claudepluginhub joshuarweaver/cascade-code-general-misc-2 --plugin omer-metin-skills-for-antigravity-2Guides model quantization tasks for ML deployment, including configuration generation and best practices for serving and inference optimization.
Deploys ML models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers quantization (INT8/INT4), on-device LLM inference, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking for mobile/IoT/embedded targets.
Exports TensorFlow models to SavedModel and TensorFlow Lite with quantization, serving signatures for production and edge deployment.