{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics"}],"enrichment":{"capability":"Provides type annotations and runtime type-checking for array shape and dtype across JAX, PyTorch, NumPy, MLX, and TensorFlow, with no JAX dependency required.","skillfed_tags":["type-checking","array-validation","deep-learning"],"use_cases":["Annotate neural network layer inputs and outputs with expected tensor shapes to catch dimension mismatches early","Document and enforce dtype constraints in scientific computing functions","Pair with runtime type-checking tools to add validation to model code","Improve IDE autocomplete and static type-checker support for array-heavy codebases","Validate matrix operation arguments for compatible dimensions"],"what_it_does":"jaxtyping is a type-annotation library that lets you specify the shape and data type of arrays and tensors directly in function signatures. Instead of writing generic array or tensor type hints, you can declare that a function expects a floating-point array with specific axis names\u2014and optionally enforce those constraints at runtime. The library works with JAX, PyTorch, NumPy, MLX, and TensorFlow, despite its historical name.\n\nThe annotations themselves are static (compatible with standard Python type checkers), but jaxtyping is designed to pair with runtime type-checking libraries, which can then validate that actual arguments match the declared shapes and dtypes. This catches shape mismatches and dtype errors early, which is especially valuable in numerical and deep-learning code where silent broadcasting or type coercion can hide bugs.","worth_installing":"Yes. Active maintenance, permissive MIT license, low install friction, and no known vulnerabilities. Valuable for any codebase mixing arrays or tensors with type hints. Install it for annotations alone; add a runtime type-checking package separately if runtime validation is needed."},"id":"jaxtyping","links":{"html":"https://skillfed.io/packages/jaxtyping","md":"https://skillfed.io/packages/jaxtyping.md","pypi":"https://pypi.org/project/jaxtyping/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-13","license_spdx":null,"license_treatment":"permissive","name":"jaxtyping","python_support":"supports_current","summary":"Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays."},"popularity":{"monthly_downloads":7153117,"position":1779,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.3.11"}