self-optimization
npx machina-cli add skill a5c-ai/babysitter/self-optimization --openclawSelf-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
- 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
Source
git clone https://github.com/a5c-ai/babysitter/blob/main/plugins/babysitter/skills/babysit/process/methodologies/ruflo/skills/self-optimization/SKILL.mdView on GitHub Overview
SONA implements a Self-Optimizing Neural Architecture cycle featuring ultra-fast weight updates, EWC++ anti-forgetting, and ReasoningBank trajectory learning. This setup enables cross-session intelligence and smarter routing/agent selection by reusing and refining learned trajectories.
How This Skill Works
SONA mines execution data to extract recurring patterns, retrieves matching trajectories from ReasoningBank, and judges their applicability to the current context. It then distills new entries and adapts weights with EWC++ regularization to preserve prior knowledge while incorporating new patterns, guided by RL feedback loops.
When to Use It
- After task completion to extract and persist learnings
- To improve routing and agent selection over time
- To adapt to new project patterns without forgetting old ones
- To build cross-session intelligence
- When integrating new environments or tasks that should not erase prior skills
Quick Start
- Step 1: Initialize the SONA cycle in your environment and ensure ReasoningBank and EWC++ modules are loaded
- Step 2: Run the SONA cycle: Extract Patterns -> RETRIEVE -> JUDGE -> DISTILL -> Adapt with EWC++; monitor for sub-millisecond updates
- Step 3: Persist updated trajectories and validate cross-session improvements; iterate as needed; Invoke via babysitter: methodologies/ruflo/ruflo-intelligence
Best Practices
- Capture high-fidelity execution traces and clearly label recurring patterns
- Index ReasoningBank trajectories for fast and accurate RETRIEVE
- Tune the EWC++ regularization strength to balance learning new and retaining old
- Incorporate RL feedback loops (Q-Learning, SARSA, PPO, DQN, etc.) to guide updates
- Validate updates with cross-session tests before deployment
Example Use Cases
- An optimizer agent (agents/optimizer/) continuously tunes performance by reusing past trajectories while adapting to new workloads
- An adaptive-queen agent (agents/adaptive-queen/) improves real-time decision strategies without forgetting prior behavior
- Cross-project pattern retention where new patterns are learned without erasing legacy workflows
- Robust trajectory-based planning in robotics by leveraging ReasoningBank to generalize across tasks
- A multi-RL agent ensemble using EWC++ to stabilize learning while expanding policy capabilities
