Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ruvnet/ruflo --skill agent-sona-learning-optimizergit clone --depth 1 https://github.com/ruvnet/rufloWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ruvnet/ruflo/agent-sona-learning-optimizer)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-sona-learning-optimizer"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-sona-learning-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-sona-learning-optimizer"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-sona-learning-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 70 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 73 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.00549 |
| Opus 5.5 | $0.00010 | $0.00220 |
| Sonnet 5.5 | $0.00005 | $0.00110 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
agent-sona-learning-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- agent-sona-learning-optimizer — 100% identical, 4 lines differ
- agent-sona-learning-optimizer — 100% identical, 0 lines differ
- agent-sona-learning-optimizer — 100% identical, 0 lines differ
- agent-sona-learning-optimizer — 100% identical, 0 lines differ
What it actually says
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: @[email protected]
- Integration Guide: docs/RUVECTOR_SONA_INTEGRATION.md
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 80 lines · 24 tokens per session scan A c4d85b3e8b54
agent-sona-learning-optimizer is a skill published in the GitHub repository ruvnet/ruflo (78,778 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 549 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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