self-optimization
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
Best use case
self-optimization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
Teams using self-optimization should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/self-optimization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How self-optimization Compares
| Feature / Agent | self-optimization | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 Invoke via babysitter process: `methodologies/ruflo/ruflo-intelligence`
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