numpy

NumPy numerical computing with arrays. Use for numerical operations.

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

numpy is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

NumPy numerical computing with arrays. Use for numerical operations.

Teams using numpy should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/numpy/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/numpy/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/numpy/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How numpy Compares

Feature / Agent	numpy	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

NumPy numerical computing with arrays. Use for numerical operations.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# NumPy

NumPy is the bedrock of the Python ecosystem. v2.0 (2024) brought the first major ABI change in 15 years, improving performance and API consistency.

## When to Use

- **Linear Algebra**: Matrix multiplication, eigenvalues.
- **Array Manipulation**: Reshaping, broadcasting.
- **Foundation**: When building libraries (like PyTorch or Pandas).

## Core Concepts

### Broadcasting

The magic rule that allows `array(3x1) + array(3)` to work.

### Dtypes

Precision matters. `float32` vs `float64`.

### Stride Tricks

Efficient memory views without copying data.

## Best Practices (2025)

**Do**:

- **Check v2.0 compat**: Many old libraries broke with NumPy 2.0.
- **Use `numpy.strings`**: New string kernels in v2.0 are much faster.

**Don't**:

- **Don't write `for` loops**: Always vectorize operations.

## References

- [NumPy Documentation](https://numpy.org/)