Provide implementations of advanced data structures
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a5c-ai/babysitter1.9KUpdated Sep 17, 2026
FreePublished on GitHub
self-optimization
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
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See how to installFiles included (2)
- SKILL.md1.5 KB
- README.md229 B
SKILL.md (original)
Review the instructions the agent will receive before you install.
- 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 tuningagents/adaptive-queen/- Real-time adaptation
Tool Use
Invoke via babysitter process: methodologies/ruflo/ruflo-intelligence
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