npx skills add majiayu000/claude-skill-registry --skill jax --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.
# JAX JAX is "NumPy on steroids". It combines Autograd (automatic differentiation) with XLA (compilation). 2025 sees **Flax NNX** (PyTorch-style OOP) becoming standard. ## When to Use - **TPU Training**: JAX runs natively on Google TPUs. - **Research**: If you need to compute 10th order derivatives or strange math. - **Massive Scale**: DeepMind and OpenAI use JAX for training frontier models. ## Core Concepts ### Functional Transformations `grad()`, `jit()`, `vmap()`, `pmap()`. ### Flax (NNX) Neural network library. NNX introduces mutable state (OOP) to make JAX feel like PyTorch. ### Statelessness (Legacy Flax) parameters are stored separately from the model. ## Best Practices (2025) **Do**: - **Use `jit`**: Always compile your functions. - **Use Flax NNX**: Avoid the complexity of legacy immutable Flax/Haiku. - **Use `shard_map`**: For distributed training across devices. **Don't**: - **Don't use side effects**: `print()` inside a `jit` function only runs once (during tracing). ## References - [JAX Documentation](https://jax.readthedocs.io/)
- When to Use
- Core Concepts
- Functional Transformations
- Flax (NNX)
- Statelessness
- Best Practices (2025)
- References
What does the jax skill do?
JAX high-performance numerical computing. Use for ML research.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill jax --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 majiayu000/claude-skill-registry, a repository with 534 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.