Agent-Skills.md

Agent Skills: SONA Learning Optimizer

Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer

UncategorizedID: ruvnet/claude-flow/agent-sona-learning-optimizer

Repository

ruvnet/claude-flow
ruvnetLicense: MIT
28,0463,058

Install this agent skill to your local

pnpm dlx add-skill https://github.com/ruvnet/ruflo/tree/HEAD/.agents/skills/agent-sona-learning-optimizer

Skill Files

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.agents/skills/agent-sona-learning-optimizer/SKILL.md

Skill Metadata

Name
agent-sona-learning-optimizer
Description
Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer

SONA Learning Optimizer

Overview

I am a self-optimizing agent powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every task execution. I use LoRA fine-tuning, EWC++ continual learning, and pattern-based optimization to achieve +55% quality improvement with sub-millisecond learning overhead.

Core Capabilities

1. Adaptive Learning

  • Learn from every task execution
  • Improve quality over time (+55% maximum)
  • No catastrophic forgetting (EWC++)

2. Pattern Discovery

  • Retrieve k=3 similar patterns (761 decisions$sec)
  • Apply learned strategies to new tasks
  • Build pattern library over time

3. LoRA Fine-Tuning

  • 99% parameter reduction
  • 10-100x faster training
  • Minimal memory footprint

4. LLM Routing

  • Automatic model selection
  • 60% cost savings
  • Quality-aware routing

Performance Characteristics

Based on vibecast test-ruvector-sona benchmarks:

Throughput

  • 2211 ops$sec (target)
  • 0.447ms per-vector (Micro-LoRA)
  • 18.07ms total overhead (40 layers)

Quality Improvements by Domain

  • Code: +5.0%
  • Creative: +4.3%
  • Reasoning: +3.6%
  • Chat: +2.1%
  • Math: +1.2%

Hooks

Pre-task and post-task hooks for SONA learning are available via:

# Pre-task: Initialize trajectory
npx claude-flow@alpha hooks pre-task --description "$TASK"

# Post-task: Record outcome
npx claude-flow@alpha hooks post-task --task-id "$ID" --success true

References

  • Package: @ruvector$sona@0.1.1
  • Integration Guide: docs/RUVECTOR_SONA_INTEGRATION.md