numpy Skill
Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorization. Published by microsoft in debugpy.
Decision snapshot
Is this a fit?
Data analysis, Includes SKILL.md, Reusable instructions
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Copy skill directory
3 months ago · NOASSERTION license
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What is numpy Skill?
Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorization. Published by microsoft in debugpy. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Data analysis use cases
Declared skill metadata
- Source file: .claude/skills/numpy/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use numpy Skill
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/debugpy
- Skill file: .claude/skills/numpy/SKILL.md
What it does
Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorization.
Skill instructions
Skill: NumPy Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorization. When to Use Apply this skill when doing numerical computing with NumPy — arrays, broadcasting, linear algebra, random sampling. Arrays - Use explicit dtypes (np.float64, np.int32) when creating arrays. - Prefer np.zeros, np.ones, np.empty, np.arange, np.linspace over list-based construction. - Use structured arrays or separate arrays instead of object arrays. Vectorization - Replace Python loops with vectorized NumPy operations wherever possible. - Use broadcasting rules to operate on arrays of different shapes without explicit expansion. - Use np.where() for conditional element-wise operations. Memory - Use np.float32 instead of np.float64 when precision is not critical to halve memory. - Use views (reshape, slicing) instead of copies when data doesn't need mutation. - Use np.memmap for arrays too large to fit in RAM. Random - Use np.random.defaultrng(seed) (new Generator
Verified compatibility and discovery
Frequently asked questions
What is numpy?
numpy is a open-source AI agent skill with Copy skill directory. Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorization.
Who is numpy best for?
numpy is best for reusing agent instructions, scripts, and references, data analysis workflows.
How do I install numpy?
Install or run numpy using Copy skill directory. Check numpy for the latest setup command.
Is numpy actively maintained?
numpy may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
