using-dynamic-architectures

# Dynamic Architectures Meta-Skill

## When to Use This Skill

Invoke this meta-skill when you encounter:

- **Growing Networks**: Adding capacity during training (new layers, neurons, modules)
- **Pruning Networks**: Removing capacity that isn't contributing
- **Continual Learning**: Training on new tasks without forgetting old ones
- **Gradient Isolation**: Training new modules without destabilizing existing weights
- **Modular Composition**: Building networks from graftable, composable components
- **Lifecycle Management**: State machines controlling when to grow, train, integrate, prune
- **Progressive Training**: Staged capability expansion with warmup and cooldown

This is the **entry point** for dynamic/morphogenetic neural network patterns. It routes to 7 specialized reference sheets.

## How to Access Reference Sheets

**IMPORTANT**: All reference sheets are located in the SAME DIRECTORY as this SKILL.md file.

When this skill is loaded from:
  `skills/using-dynamic-architectures/SKILL.md`

Reference sheets like `continual-learning-foundations.md` are at:
  `skills/using-dynamic-architectures/continual-learning-foundations.md`

NOT at:
  `skills/continual-learning-foundations.md` (WRONG PATH)

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## Core Principle

**Dynamic architectures grow capability, not just tune weights.**

Static networks are a guess about capacity. Dynamic networks let training signal drive structure. The challenge is growing without forgetting, integrating without destabilizing, and knowing when to act.

Key tensions:
- **Stability vs. Plasticity**: Preserve existing knowledge while adding new capacity
- **Isolation vs. Integration**: Train new modules separately, then merge carefully
- **Exploration vs. Exploitation**: When to add capacity vs. when to stabilize

## The 7 Dynamic Architecture Skills

1. **continual-learning-foundations** - EWC, PackNet, rehearsal strategies, catastrophic forgetting theory
2. **gradient-isolation-techniques** - Freezing, gradient masking, stop_gr