self-optimization
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
Profile →npx skills add majiayu000/claude-skill-registry --skill self-optimization-a5c-ai-babysitter --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 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 Invo
- Overview
- When to Use
- SONA Cycle
- Anti-Forgetting (EWC++)
- RL Algorithms
- Agents Used
- Tool Use
What does the self-optimization skill do?
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill self-optimization-a5c-ai-babysitter --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.