a5c-ai/self-optimization
library/methodologies/ruflo/skills/self-optimization/SKILL.md
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
$ install --global
skillsauthnpx skillsauth add a5c-ai/babysitter self-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 1, 2026, 12:20 PM67.2s2 files scanned
SKILL.md
- name:
- self-optimization
- description:
- SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
- allowed-tools:
- Read, Write, Edit, Bash, Grep, Glob, WebFetch, WebSearch, Agent, AskUserQuestion
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
- 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
Related Skills
a5c-ai/model-card-generator
development
VerifiedTrustedCommunity
Model documentation skill for generating model cards following Google's model card framework.
680SKILL.mdUpdated Apr 28, 2026
a5c-ai/mlflow-experiment-tracker
development
VerifiedTrustedCommunity
MLflow integration skill for experiment tracking, model registry, and artifact management. Enables LLMs to log experiments, compare runs, manage model lifecycle, and retrieve artifacts through the MLflow API.
680SKILL.mdUpdated Apr 28, 2026
a5c-ai/lime-explainer
data-ai
VerifiedTrustedCommunity
LIME-based local explanation skill for individual predictions across tabular, text, and image data.
680SKILL.mdUpdated Apr 28, 2026
a5c-ai/kubeflow-pipeline-executor
devops
VerifiedTrustedCommunity
Kubeflow Pipelines skill for ML workflow orchestration, component management, and Kubernetes-native ML.
680SKILL.mdUpdated Apr 28, 2026