numpy
N-dimensional array computing library providing array types, vectorized operations, linear algebra, FFT, random sampling, and testing utilities.
Profile →npx skills add majiayu000/claude-skill-registry --skill skills-skilldoai-skilldo-16 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Imports ```python import numpy as np from numpy import array, asarray, arange, zeros, ones, empty, linspace from numpy import dtype, reshape, concatenate, stack, where from numpy import sum, mean, std, min, max from numpy import dot from numpy.linalg import norm, solve ``` ## Core Patterns ### Create arrays and control dtype/shape ✅ Current ```python import numpy as np def main() -> None: a: np.ndarray = np.array([1, 2, 3], dtype=np.int64) b: np.ndarray = np.zeros((2, 3), dtype=np.float64) c: np.ndarray = np.arange(0, 10, 2, dtype=np.int32) d: np.dtype = np.dtype([("x", np.int32), ("y", np.float64)]) rec: np.ndarray = np.zeros(3, dtype=d) # Print in a way that reliably includes dtype names and field names in stdout. print("a dtype:", a.dtype) print("b dtype:", b.dtype) print("c dtype:", c.dtype) print("rec dtype names:", rec.dtype.names) if __name__ == "__main__": main() ``` * Use `np.array`/`np.asarray` for explicit conversion, `np.zeros`/`np.ones`/`np.empty` for allocation, and `np.dtype(...)` to define dtypes (including structured/record dtypes). ### Vectorized computation, masking, and selection ✅ Fixed ```python import numpy as np def main() -> None: x: np.ndarray = np.lins
- Imports
- Core Patterns
- Create arrays and control dtype/shape ✅ Current
- Vectorized computation, masking, and selection ✅ Fixed
- Reshape, stack, and concatenate ✅ Fixed
- Linear algebra with numpy.linalg ✅ Current
- Run NumPy’s test suite from Python ✅ Current
- Configuration
- Pitfalls
- Wrong: Assuming list-based structured dtypes create custom field names
- Right: Specify names explicitly for structured dtypes
- Wrong: Using numpy.core (private) instead of public top-level APIs
- Right: Use public numpy APIs (top-level) and documented submodules
- Wrong: Expecting np.asarray to copy input data
What does the numpy skill do?
N-dimensional array computing library providing array types, vectorized operations, linear algebra, FFT, random sampling, and testing utilities.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill skills-skilldoai-skilldo-16 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.