Agent-Skills.md

Agent Skills: self-optimization

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

UncategorizedID: a5c-ai/babysitter/self-optimization

Repository

a5c-ai/babysitter
a5c-aiLicense: MIT
1,52087

Install this agent skill to your local

pnpm dlx add-skill https://github.com/a5c-ai/babysitter/tree/HEAD/library/methodologies/ruflo/skills/self-optimization

Skill Files

Browse the full folder contents for self-optimization.

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

Skill Metadata

Name
self-optimization
Description
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
  • Improving routing and agent selection over time
  • Adapting to new project patterns without forgetting old ones
  • Building cross-session intelligence

SONA Cycle

  1. Extract Patterns - Mine execution data for recurring patterns
  2. RETRIEVE - Search ReasoningBank for matching trajectories
  3. JUDGE - Evaluate trajectory applicability in current context
  4. DISTILL - Compress and store new entries
  5. 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