tensorflow-physics-ml
TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models
Profile →npx skills add a5c-ai/babysitter --skill tensorflow-physics-ml --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.
# TensorFlow Physics ML ## Purpose Provides expert guidance on TensorFlow for physics applications, including physics-informed neural networks and neural network potentials. ## Capabilities - Physics-informed neural networks (PINNs) - Neural network potentials (NNP) - Normalizing flows for density estimation - Graph neural networks for molecular systems - Automatic differentiation for physics - TensorBoard experiment tracking ## Usage Guidelines 1. **Architecture Design**: Build appropriate neural network architectures 2. **PINNs**: Incorporate physical constraints in loss functions 3. **Potentials**: Train neural network interatomic potentials 4. **GNNs**: Use graph networks for molecular systems 5. **Training**: Monitor and optimize training with TensorBoard ## Tools/Libraries - TensorFlow - DeepMD-kit - SchNet
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the tensorflow-physics-ml skill do?
TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill tensorflow-physics-ml --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 a5c-ai/babysitter, a repository with 1,642 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.