Installs into .claude/skills of the current project.
Are you the author of Augmentation And Sampling?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-augmentation-and-sampling)
---
name: augmentation-and-sampling
description: "Guides braindecode signal transforms, composed augmentation
loaders, and sequence, relative-positioning, and self-supervised samplers with
shape-safe validation."
disable-model-invocation: true
metadata:
disco-role: operating
license: BSD 3-Clause
---
# Augmentation and sampling
Use this route when augmentations, `Transform` composition,
`AugmentedDataLoader`, `n_augmentation`, sequence sampling, relative
positioning, or self-supervised sampling is the main task.
## Workflow
1. Confirm batches are `(X, y)` with `X` shaped `(batch, channels, time)` and
that labels remain aligned after each transform.
2. Build a `Transform` or `Compose` with an explicit probability and random
state. Test it on one batch and verify shape, dtype, device, and label
invariants.
3. Use `AugmentedDataLoader` for training batches. Remember that
`n_augmentation=0` preserves the original batch; positive values append
transformed copies after clean originals and tile labels accordingly.
4. Choose sequence/relative-positioning/self-supervised samplers only when the
dataset contains the required trial/session/window metadata. Verify indices
and split boundaries before training.
5. Use CPU first. Move both model and batches to CUDA only after a bounded
allocation succeeds; a CUDA availability flag is not a memory guarantee.
Read [API reference](references/api-reference.md), [sampling workflows](references/sampling-workflows.md),
and [troubleshooting](references/troubleshooting.md). Run the deterministic
[augmentation smoke](scripts/smoke_augmentation.py), which uses a local
`TensorDataset` and no network or credentials.
