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self-optimization

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

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Repository
a5c-ai/babysitter
Letzte Quellaktivität
1. Juni 2026 um 07:46
Erkannte Sprache von SKILL.md
Englisch
Sterne
1.832
Forks
113

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
self-optimization
description
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
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Read, Write, Edit, Bash, Grep, Glob, WebFetch, WebSearch, Agent, AskUserQuestion
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{"domains":["domain:software-engineering"],"skillAreas":["skill-area:agentic-loops","skill-area:orchestration-loop"],"workflows":["workflow:feature-development"],"topics":["topic:developer-experience"],"roles":["role:tech-lead","role:backend-engineer"]}
- 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`
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