agent-sona-learning-optimizer

AI Community

Repo stars 54,444
Author repo ruflo

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer name: sona-learning-optimizer description:…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Overview”, “Core Capabilities”, “1. Adaptive Learning”, “2. Pattern Discovery”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name agent-sona-learning-optimizer directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Overview / Core Capabilities / 1. Adaptive Learning” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

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:

# 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

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: AI
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @ruvnet · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Guided setup
External API key: No requirement detected
Detected OS requirements: macOS · Linux · Windows
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Shell exec
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview agent-sona-learning-optimizer.preview

The current SKILL.md does not define a fixed output example.

SKILL.md 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

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Overview → Core Capabilities → 1. Adaptive Learning → 2. Pattern Discovery → 3. LoRA Fine-Tuning → 4. LLM Routing
terms    -> self-optimizing agent · +55% quality improvement · sub-millisecond learning overhead · 2211 ops$sec · 0.447ms · 18.07ms · Code · Creative
files/cmd -> docs/RUVECTORSONAINTEGRATION.md
body sha256 -> 1f4b237545a0