Autograd

Use this repo skill when a user asks about Autograd gradients, Jacobians, Hessians, custom primitives, autograd.numpy, autograd.scipy, structured optimizers, or fixed-point helpers. The package is library-only; there is no CLI route.

Read first

• references/installation.md for public install commands and optional extras.
• references/repo-provenance.md when checking whether this skill matches the current repository state or before refreshing it.
• references/troubleshooting.md for cross-cutting install, import, optional-dependency, and usage failures.
• scripts/autograd_smoke.py for a quick install-and-import sanity check.

Route map

• Core differentiation: sub-skills/differentiation-core/SKILL.md
  • Use for grad, jacobian, elementwise_grad, value_and_grad, higher-order derivatives, make_vjp, make_jvp, holomorphic_grad, and gradient checking.
• NumPy and SciPy wrappers: sub-skills/numpy-scipy-primitives/SKILL.md
  • Use for autograd.numpy, autograd.scipy, supported/unsupported NumPy patterns, xarray interoperability, and missing SciPy-extra troubleshooting.
• Custom primitives: sub-skills/extend-primitives/SKILL.md
  • Use for primitive, defvjp, defjvp, deprecated wrapper compatibility, and gradient checking around a new rule.
• Optimization workflows: sub-skills/optimization-workflows/SKILL.md
  • Use for flatten, structured optimizers, fixed_point, and scipy.optimize.minimize with value_and_grad.

Install

For a regular user install:

pip install autograd

For the full NumPy/SciPy wrapper surface used by this skill:

pip install "autograd[scipy]"

If you are working from a checkout and want an editable install:

pip install -e '.[scipy]'

Optional xarray container-interoperability examples need xarray installed separately.

Minimal smoke

Run the bundled smoke helper after installation:

python scripts/autograd_smoke.py

Use --require-scipy when you want the smoke to fail instead of skipping the optional SciPy section.

How to choose a route

• If the problem is “what derivative operator should I use?”, start with differentiation-core.
• If the problem is “why does a NumPy/SciPy expression fail or behave oddly under Autograd?”, start with numpy-scipy-primitives.
• If the problem is “how do I make my own function differentiable?”, start with extend-primitives.
• If the problem is “how do I optimize a structured parameter tree or use SciPy minimize?”, start with optimization-workflows.

Notes for future refreshes

Read references/repo-provenance.md before refreshing this skill against a new Autograd checkout. The provenance snapshot records the source commit, dirty state, package version, and evidence paths used to build the current skill.