self-optimization
SkillDev toolsLets your agent learn from its own past task performance and improve its behavior over time.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the self-optimization skill
About this capability
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/methodologies/ruflo/skills/self-optimization/SKILL.md and read by ahel’s review.
- 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
Signals
- GitHub stars
- 2k
- Forks
- 106
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
self-optimization- Source
- github.com/a5c-ai/babysitter