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diff --git a/.agents/skills/architecture-design/examples/augmentation_example.py b/.agents/skills/architecture-design/examples/augmentation_example.py
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+"""
+Data Augmentation Example
+
+Demonstrates how to create a custom data augmentation function
+following the architecture design pattern.
+"""
+
+import torch
+from typing import Dict
+from src.data_module.augmentation import register_augmentation
+
+
+@register_augmentation("time_shift")
+def time_shift(signal: torch.Tensor, max_shift: int = 10) -> torch.Tensor:
+    """Randomly shift signal in time.
+
+    Args:
+        signal: Input signal tensor of shape (channels, time_steps)
+        max_shift: Maximum number of steps to shift
+
+    Returns:
+        Shifted signal tensor
+    """
+    shift = torch.randint(-max_shift, max_shift + 1, (1,)).item()
+    return torch.roll(signal, shifts=shift, dims=-1)
+
+
+@register_augmentation("amplitude_scale")
+def amplitude_scale(
+    signal: torch.Tensor,
+    min_scale: float = 0.8,
+    max_scale: float = 1.2
+) -> torch.Tensor:
+    """Randomly scale signal amplitude.
+
+    Args:
+        signal: Input signal tensor of shape (channels, time_steps)
+        min_scale: Minimum scaling factor
+        max_scale: Maximum scaling factor
+
+    Returns:
+        Scaled signal tensor
+    """
+    scale = torch.empty(1).uniform_(min_scale, max_scale).item()
+    return signal * scale
+
+
+@register_augmentation("gaussian_noise")
+def add_gaussian_noise(
+    signal: torch.Tensor,
+    mean: float = 0.0,
+    std: float = 0.1
+) -> torch.Tensor:
+    """Add Gaussian noise to signal.
+
+    Args:
+        signal: Input signal tensor of shape (channels, time_steps)
+        mean: Mean of Gaussian noise
+        std: Standard deviation of Gaussian noise
+
+    Returns:
+        Signal with added noise
+    """
+    noise = torch.randn_like(signal) * std + mean
+    return signal + noise
+
+
+# Example: Composed augmentation
+@register_augmentation("composed")
+def composed_augmentation(signal: torch.Tensor, cfg) -> torch.Tensor:
+    """Apply multiple augmentations in sequence.
+
+    Args:
+        signal: Input signal tensor
+        cfg: Configuration object with augmentation parameters
+
+    Returns:
+        Augmented signal tensor
+    """
+    # Apply each augmentation based on config
+    if cfg.augmentation.time_shift:
+        signal = time_shift(signal, cfg.augmentation.max_shift)
+
+    if cfg.augmentation.amplitude_scale:
+        signal = amplitude_scale(
+            signal,
+            cfg.augmentation.min_scale,
+            cfg.augmentation.max_scale
+        )
+
+    if cfg.augmentation.gaussian_noise:
+        signal = add_gaussian_noise(
+            signal,
+            cfg.augmentation.noise_mean,
+            cfg.augmentation.noise_std
+        )
+
+    return signal
+
+
+# Usage in dataset class
+class AugmentedDataset:
+    """Example dataset with augmentation support."""
+
+    def __init__(self, cfg):
+        self.cfg = cfg
+        self.augmentation_fn = AugmentationFactory(cfg.augmentation.name)
+
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
+        # Load signal
+        signal = self.load_signal(idx)
+
+        # Apply augmentation (training mode only)
+        if self.training and self.augmentation_fn:
+            signal = self.augmentation_fn(signal, self.cfg)
+
+        return {"signal": signal, "label": self.labels[idx]}