NumPy best practices: vectorization, dtypes, and memory-efficient array programming
Vectorized ufuncs and broadcasting over loops, views over copies, dtype discipline, memory-layout awareness, and NaN-safe error handling for idiomatic high-performance NumPy
- What
- Vectorized ufuncs and broadcasting over loops, views over copies, dtype discipline, memory-layout awareness, and NaN-safe error handling for idiomatic high-performance NumPy
- Cost
- Free
- Needs
- a Python project using NumPy that the agent can read and refactor — the skill is guidance the agent follows, not software to install
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @mindrally's NumPy best-practices skill for writing idiomatic, fast numerical Python: always prefer vectorized ufuncs and broadcasting over explicit loops, pick array-creation and combination routines deliberately (`np.zeros`/`np.empty` pre-allocation, `vstack`/`hstack`/`concatenate`), use boolean advanced indexing and `np.where()`, understand views vs fancy-index copies, declare dtypes explicitly (`float32` when precision allows, guard against integer overflow), check memory layout with `ndarray.flags` and prefer in-place `out=` operations, handle NaN/Inf with `isnan`/`isinf` under `np.errstate()`, use the modern `np.random.default_rng()` generator API, solve linear systems with `linalg.solve` instead of inverting, and test with `pytest` + `np.testing` assertions in NumPy docstring style. Honest caveats: methodology only, no tooling — the agent needs a NumPy project to apply it to; NumPy-version-specific behaviors (e.g. RNG API) evolve, pin the version in your project. Apache-2.0 licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: a Python project using NumPy that the agent can read and refactor — the skill is guidance the agent follows, not software to install Install "NumPy best practices: vectorization, dtypes, and memory-efficient array programming" for me. It gives my agent @mindrally's NumPy playbook: vectorized ufuncs and broadcasting over loops, array-creation discipline, boolean indexing, views vs copies, explicit dtypes, memory-layout awareness with in-place operations, NaN/Inf-safe error handling, the modern default_rng API, linear-algebra habits (solve instead of invert), and pytest + np.testing testing conventions. Apache-2.0 licensed. Repository: https://github.com/mindrally/skills/blob/main/numpy-best-practices/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 "numpy-best-practices". 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. point the agent at my NumPy code or file to review and the NumPy version pinned in the project; nothing else — it's a methodology). 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.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.