Agent Sona Learning Optimizer
Wire a self-optimizing agent loop that learns from each run with LoRA fine-tuning, EWC++ memory, and pattern retrieval instead of static prompts.
Overview
SONA Learning Optimizer is an agent skill most often used in Build (also Operate, Ship) that continuously adapts agents with LoRA, EWC++, and pattern-based routing for higher quality per task.
Install
npx skills add https://github.com/ruvnet/ruflo --skill agent-sona-learning-optimizerWhat is this skill?
- SONA adaptive learning with EWC++ to limit catastrophic forgetting across tasks
- Micro-LoRA fine-tuning cited at 99% parameter reduction and faster training vs full weights
- Pattern discovery with k=3 similar patterns and high-throughput retrieval for reuse on new tasks
- Quality-aware LLM routing positioned for roughly 60% cost savings on model calls
- Benchmark-oriented targets: +55% max quality gain and sub-millisecond learning overhead
- +55% maximum quality improvement cited
- k=3 similar pattern retrieval
- 99% parameter reduction via LoRA positioning
Adoption & trust: 644 installs on skills.sh; 58.5k GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your agent repeats the same mistakes because nothing systematic learns from past runs or picks cheaper models without hurting quality.
Who is it for?
Solo builders operating custom multi-step agents who log task outcomes and want incremental learning without full model retraining.
Skip if: One-off chat sessions with no telemetry, teams that cannot store execution traces, or builders who only need a single static prompt.
When should I use this skill?
Invoke when you need SONA-powered self-optimization with LoRA, EWC++, pattern discovery, and quality-aware LLM routing on recurring agent workloads.
What do I get? / Deliverables
Each execution can update lightweight adapters, recall similar patterns, and route to the right LLM so quality trends up with bounded forgetting and measurable overhead.
- Adaptive routing and learning configuration for agent runs
- Growing pattern library applied to new tasks
- Documented quality and latency targets aligned to benchmarks
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Canonical shelf is Build because solo builders adopt this while shaping agent stacks, routing, and fine-tuning—not after the product is frozen. Agent-tooling is the right subphase: SONA, LoRA, pattern libraries, and LLM routing are capabilities you bolt onto coding agents and custom orchestrators.
Where it fits
Attach SONA learning hooks after you define tool loops so codegen agents improve on repeated repo patterns.
Benchmark latency and throughput targets before launch so learning overhead stays sub-millisecond per step.
Feed production task outcomes into pattern discovery without wiping older skills thanks to EWC++.
How it compares
Use as an adaptive layer on top of static skills— not a replacement for brainstorming or plan-writing workflows.
Common Questions / FAQ
Who is agent-sona-learning-optimizer for?
Indie and solo developers building persistent agents—coding assistants, ops bots, or research runners—who want quality to compound from real executions.
When should I use agent-sona-learning-optimizer?
During Build when designing agent pipelines; during Ship when tuning quality and cost before release; and during Operate when iterating on production agent behavior with continual learning.
Is agent-sona-learning-optimizer safe to install?
Review the Security Audits panel on this Prism page and treat fine-tuning and routing hooks like any code that touches models, data, and API keys.
SKILL.md
READMESKILL.md - 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**: @ruvector$sona@0.1.1 - **Integration Guide**: docs/RUVECTOR_SONA_INTEGRATION.md