docstring

PyTorch docstrings require Sphinx/reStructuredText format with raw strings, specific section ordering (signature, description, args, returns, examples), and Sphinx roles like `:func:` and `:class:`. This Skill generates properly structured docstrings enforcing these conventions, including mathematical formulas and cross-references, so your documentation stays consistent with PyTorch standards.

PyTorch docstrings must contain function signature, brief description, mathematical formulas, cross-references, notes or warnings, Args, Keyword Args, Returns, and Examples sections. This Skill ensures all required sections are present and correctly formatted with proper indentation, Sphinx roles, and LaTeX notation for tensor shapes.

Yes. This Skill generates complete, convention-compliant docstrings for PyTorch functions and methods automatically, handling raw-string formatting, section structure, and Sphinx markup. You provide the function signature and purpose; it produces a fully formatted docstring ready for torch/_tensor_docs.py, torch/nn/functional.py, and similar PyTorch codebases.

This Skill generates example code within PyTorch docstrings using Sphinx code-block directives and proper tensor shape notation in LaTeX. It structures examples to demonstrate function usage clearly, ensuring they integrate seamlessly with PyTorch's Sphinx documentation build and cross-reference system.

PyTorch docstrings use reStructuredText inline math notation (`:math:` role) for formulas and Sphinx cross-reference roles (`:func:`, `:class:`, `:meth:`) for links. This Skill automatically applies correct syntax and indentation for both mathematical expressions and references within docstrings.

No. This Skill handles raw-string prefixes, Sphinx role syntax (`:func:`, `:class:`), section formatting, and indentation automatically. You describe what the function does; it produces a fully formatted, Sphinx-compliant docstring following PyTorch conventions exactly.