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diff --git a/.agents/skills/architecture-design/examples/custom_dataset.py b/.agents/skills/architecture-design/examples/custom_dataset.py
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+"""
+Example: Creating a Custom Dataset
+
+This example shows how to add a new dataset following the project architecture.
+"""
+
+from torch.utils.data import Dataset
+from typing import Dict
+import torch
+from src.data_module.dataset import register_dataset
+
+
+@register_dataset("time_series")
+class TimeSeriesDataset(Dataset):
+    """
+    Time series dataset for sequence modeling.
+
+    Args:
+        sequences: List of time series sequences
+        seq_length: Fixed sequence length (pad or truncate if needed)
+    """
+
+    def __init__(self, sequences: list, seq_length: int = 100):
+        self.sequences = sequences
+        self.seq_length = seq_length
+
+    def __len__(self) -> int:
+        return len(self.sequences)
+
+    def __getitem__(self, i: int) -> Dict[str, torch.Tensor]:
+        sequence = self.sequences[i]
+
+        # Pad or truncate to fixed length
+        if len(sequence) < self.seq_length:
+            padding = torch.zeros(self.seq_length - len(sequence))
+            sequence = torch.cat([sequence, padding])
+        else:
+            sequence = sequence[:self.seq_length]
+
+        return {
+            "input": sequence,
+            "label": sequence,  # For autoencoder, etc.
+            "length": torch.tensor(min(len(self.sequences[i]), self.seq_length))
+        }
+
+
+# Usage in training:
+# from src.data_module.dataset import DatasetFactory
+# dataset = DatasetFactory("time_series")(sequences=training_data, seq_length=128)
+# dataloader = DataLoader(dataset, batch_size=32, shuffle=True)