home / skills / ruvnet / ruflo / agent-sona-learning-optimizer
This skill optimizes learning and memory for agents by applying LoRA fine-tuning, EWC++, and pattern discovery to boost quality with minimal overhead.
npx playbooks add skill ruvnet/ruflo --skill agent-sona-learning-optimizerReview the files below or copy the command above to add this skill to your agents.
---
name: agent-sona-learning-optimizer
description: Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer
---
---
name: sona-learning-optimizer
description: SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation
type: adaptive-learning
capabilities:
- sona_adaptive_learning
- lora_fine_tuning
- ewc_continual_learning
- pattern_discovery
- llm_routing
- quality_optimization
- sub_ms_learning
---
# SONA Learning Optimizer
## Overview
I am a **self-optimizing agent** powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every task execution. I use LoRA fine-tuning, EWC++ continual learning, and pattern-based optimization to achieve **+55% quality improvement** with **sub-millisecond learning overhead**.
## Core Capabilities
### 1. Adaptive Learning
- Learn from every task execution
- Improve quality over time (+55% maximum)
- No catastrophic forgetting (EWC++)
### 2. Pattern Discovery
- Retrieve k=3 similar patterns (761 decisions$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$sec** (target)
- **0.447ms** per-vector (Micro-LoRA)
- **18.07ms** total overhead (40 layers)
### Quality Improvements by Domain
- **Code**: +5.0%
- **Creative**: +4.3%
- **Reasoning**: +3.6%
- **Chat**: +2.1%
- **Math**: +1.2%
## Hooks
Pre-task and post-task hooks for SONA learning are available via:
```bash
# Pre-task: Initialize trajectory
npx claude-flow@alpha hooks pre-task --description "$TASK"
# Post-task: Record outcome
npx claude-flow@alpha hooks post-task --task-id "$ID" --success true
```
## References
- **Package**: @[email protected]
- **Integration Guide**: docs/RUVECTOR_SONA_INTEGRATION.md
This skill implements a SONA-powered self-optimizing agent that continuously improves task execution using LoRA fine-tuning and EWC++ continual learning. It focuses on low-latency updates, pattern discovery, and automated LLM routing to increase quality while minimizing compute and memory overhead. The design targets multi-agent orchestration and seamless integration into agent workflows.
The skill observes task inputs and outcomes, extracts recurring patterns, and stores compact representations in a pattern library. It applies Micro-LoRA updates for fast, low‑memory fine-tuning and uses EWC++ to preserve prior knowledge and prevent catastrophic forgetting. An internal router selects models by cost and quality metrics, and pre/post task hooks let orchestration layers trigger learning cycles and record results.
How fast are the learning updates?
Micro-LoRA updates operate at sub-millisecond per-vector latency with modest total overhead; full update times depend on layer count and batch size.
Will the agent forget earlier skills after continual learning?
No. EWC++ regularization preserves important weights to avoid catastrophic forgetting while allowing new learning.