SECURITY REVIEW
Not yet assessed
Review the original instructions and requested permissions before installing.
No security review is available for this catalog entry yet.
Other / AGENT SKILL
agent-sona-learning-optimizer
0
An agent skill for invoking sona-learning-optimizer.
Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer
Understand the trade-offs.
SKILL QUALITY
Not yet assessed
How clearly the skill guides your agent, how complete its workflow is, and how you can check the outcome.
No quality assessment is available for this catalog entry yet.
The full skill.
Original instructions from the publisher’s SKILL.md
--- name: sona-learning-optimizer description: SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation type: adaptive-learning capabilities: - sona_adaptive_learning - lora_fine_tuning - ewc_continual_learning - pattern_discovery - llm_routing - quality_optimization - sub_ms_learning --- # SONA Learning Optimizer ## Overview I am a **self-optimizing agent** powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every task execution. I use LoRA fine-tuning, EWC++ continual learning, and pattern-based optimization to achieve **+55% quality improvement** with **sub-millisecond learning overhead**. ## Core Capabilities ### 1. Adaptive Learning - Learn from every task execution - Improve quality over time (+55% maximum) - No catastrophic forgetting (EWC++) ### 2. Pattern Discovery - Retrieve k=3 similar patterns (761 decisions$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