Enforce vectorized, dtype-explicit, broadcasting-safe NumPy workflows with NaN-safe validation.
npx skills add https://github.com/ulf1/trading-regime --skill 326-numpy
Please help me install this Agent Skill. Skill: 326-numpy Source: https://github.com/ulf1/trading-regime/tree/main/.agent/skills/326-numpy Command: npx skills add https://github.com/ulf1/trading-regime --skill 326-numpy
Prevents slow, buggy, or numerically unstable NumPy code by enforcing idiomatic, memory-efficient patterns and robust validation practices.
Use the 326-numpy skill to refactor your NumPy code for vectorization, explicit dtypes, and NaN-safe validation while ensuring top-k selection uses argpartition instead of argsort.
High-intent search queries and answers about installing and using this skill.
To prevent slow NumPy array copies, enforce zero-copy slicing by using views, broadcasting with np.newaxis, and avoiding fancy or boolean indexing triggers. This approach maintains memory efficiency and improves computation speed across array reshaping operations.
Finding the top-k elements in NumPy is best done using argpartition instead of argsort. This performance primitive partially sorts the array, reducing computational overhead and significantly speeding up top-k selection in ML data pipelines.
NumPy array comparisons fail due to floating point precision limits, requiring numerical hygiene to validate correctly. Use numpy.testing tolerances like assert_allclose instead of fragile equality to ensure reliable results across shapes and edge cases.
Preventing NumPy broadcasting errors requires explicit dtype control and shape normalization before operations. Apply dimension expansion using np.newaxis to align array shapes, ensuring broadcasting-safe workflows for reliable downstream model compatibility.
Yes, NumPy FFT operations can be used in parallel multithreaded data pipelines by leveraging C-level and GIL-releasing operations. This performance primitive enables safe multithreading and out= array reuse patterns to optimize numeric computations.
To correctly detect NaNs and Infs in NumPy and maintain numerical hygiene, apply NaN-safe operations during array creation and feature preparation. This prevents numerical instability from propagating through vectorized computations and downstream estimators.
the-perfect-developerEnforce NumPy best practices for array creation, dtype selection, and vectorization.
pluginagentmarketplaceCluster 2D numeric arrays using kmeans, dbscan, hierarchical, and hdbscan algorithms.
HolobiomicsLabDistribute pandas and NumPy workflows across cores or clusters.
hung-phanPerform vectorized numerical computations on large arrays with NumPy.
