numpy
- Repo stars 2,412
- License NOASSERTION
- Author updated Live
- Author repo debugpy
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 94 / 100 · audit passed
- Author / version / license
- @microsoft · NOASSERTION
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: numpy
description: Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorizat…
category: other
runtime: Python
---
# numpy output preview
## PART A: Task fit
- Use case: Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorization. Apply this skill when doing numerical computing with NumPy — arrays, broadcasting, linear algebra, random sampling. runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Use / Arrays / Vectorization” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorization. Apply this skill when doing numerical computing with NumPy — arrays, broadcasting, linear algebra, random sampling. runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “When to Use / Arrays / Vectorization” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “When to Use / Arrays / Vectorization”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: numpy
description: Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorizat…
category: other
source: microsoft/debugpy
---
# numpy
## When to use
- Best practices for numerical computing with NumPy including arrays, broadcasting, and vectorization. Apply this skill…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to Use / Arrays / Vectorization” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "numpy" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to Use / Arrays / Vectorization
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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.linspaceover 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.float32instead ofnp.float64when precision is not critical to halve memory. - Use views (
reshape, slicing) instead of copies when data doesn't need mutation. - Use
np.memmapfor arrays too large to fit in RAM.
Random
- Use
np.random.default_rng(seed)(new Generator API) instead ofnp.random.seed(). - Always seed random generators in tests for reproducibility.
Pitfalls
- Don't compare floats with
==; usenp.allclose()ornp.isclose(). - Beware of silent integer overflow in integer arrays.
- Avoid
np.matrix— it's deprecated; use 2Dnp.ndarray.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review