Machine learning with JAX: functional patterns, JIT, and core practices

Prerequisites: a JAX-based ML project to work on — the skill is a reference the agent consults, not software to install Install "Machine learning with JAX: functional patterns, JIT, and core practices" for me. It gives my agent @mindrally's JAX reference: jax.numpy operations, grad/jit/vmap transformations, lax control flow, functional RNG key handling, pytrees, sharding, checkpointing, and Flax/Haiku model pointers. Apache-2.0 licensed. Repository: https://github.com/mindrally/skills/blob/main/machine-learning/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code, credentials only via the secure vault, allowed hosts declared in the SKILL.md. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "machine-learning". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. install JAX and Flax/Haiku in my Python environment; nothing else — it's a reference). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.