Identify Architecture

Analyze and document machine learning model architectures including layers, connections, and information flow.

When to Use

• Understanding paper model designs
• Planning model implementation
• Comparing architecture variations
• Documenting neural network structure

Quick Reference

# Extract architecture from paper
# Look for: "Figure X: Architecture of [Model]"
# Check for: Table with layer specifications
# Find: Layer descriptions (Conv2D, FC, BatchNorm, etc.)

# Visualize model structure (Mojo)
# var model: SimpleNet = ...
# print(model)  # Should show layer information

Workflow

1. Locate architecture diagram: Find visual architecture representation in paper
2. List layers: Enumerate all layers with type and parameters
3. Document connections: Map data flow between layers (skip connections, merges)
4. Extract layer parameters: For each layer record size, activation, normalization
5. Create implementation plan: Translate to Mojo struct/function definitions

Output Format

Architecture documentation:

• Model name and source
• Layer-by-layer breakdown
• Layer type (Conv2D, Dense, etc.)
• Parameters (kernel size, stride, padding, activation)
• Input/output shapes
• Data flow diagram (text or ASCII)
• Special components (skip connections, attention)

References

• See extract-hyperparameters skill for model configuration
• See CLAUDE.md > Mojo Syntax Standards for implementation patterns
• See /notes/review/mojo-ml-patterns.md for architecture patterns