Babysitter self-optimization

SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/methodologies/ruflo/skills/self-optimization" ~/.claude/skills/a5c-ai-babysitter-self-optimization && rm -rf "$T"

manifest: library/methodologies/ruflo/skills/self-optimization/SKILL.md

Self-Optimization

Overview

Implements the SONA (Self-Optimizing Neural Architecture) adaptation cycle with sub-millisecond weight updates, EWC++ to prevent catastrophic forgetting, and a ReasoningBank for trajectory-based learning.

When to Use

After task completion to extract and persist learnings
Improving routing and agent selection over time
Adapting to new project patterns without forgetting old ones
Building cross-session intelligence

SONA Cycle

Extract Patterns - Mine execution data for recurring patterns
RETRIEVE - Search ReasoningBank for matching trajectories
JUDGE - Evaluate trajectory applicability in current context
DISTILL - Compress and store new entries
Adapt - Update weights with EWC++ regularization

Anti-Forgetting (EWC++)

Elastic Weight Consolidation prevents overwriting previously learned patterns
Fisher information matrix tracks parameter importance
Configurable regularization penalty for new adaptations

RL Algorithms

Q-Learning, SARSA, PPO, DQN, A2C, TD3, SAC, DDPG, Rainbow

Agents Used

```
agents/optimizer/
```
- Performance tuning
```
agents/adaptive-queen/
```
- Real-time adaptation

Tool Use

Invoke via babysitter process:

methodologies/ruflo/ruflo-intelligence