agent-sona-learning-optimizer
- Repo stars 54,444
- Author updated Live
- Author repo ruflo
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @ruvnet · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- macOS · Linux · Windows
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Shell exec
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
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。
---
name: agent-sona-learning-optimizer
description: Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer name: sona-…
category: ai
runtime: no special runtime
---
# agent-sona-learning-optimizer output preview
## PART A: Task fit
- Use case: 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 I am a self-optimizing agent powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / Core Capabilities / 1. Adaptive Learning” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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 I am a self-optimizing agent powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every…”.
- **02** When the source has headings, the agent prioritizes “Overview / Core Capabilities / 1. Adaptive Learning” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, run shell commands, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, run shell commands, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, run shell commands, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “Overview / Core Capabilities / 1. Adaptive Learning”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: agent-sona-learning-optimizer
description: Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer name: sona-…
category: ai
source: ruvnet/ruflo
---
# agent-sona-learning-optimizer
## When to use
- Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer name: sona-learning-optimizer des…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / Core Capabilities / 1. Adaptive Learning” and keep inference separate from source facts.
- read files, run shell commands, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "agent-sona-learning-optimizer" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / Core Capabilities / 1. Adaptive Learning
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, run shell commands, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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 "$ID" --success trueReferences
- Package: @ruvector$sona@0.1.1
- Integration Guide: docs/RUVECTOR_SONA_INTEGRATION.md
Decide Fit First
Design Intent
How To Use It
Boundaries And Review