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 Soulcynics404/AgentForge --skill agent-sona-learning-optimizergit clone --depth 1 https://github.com/Soulcynics404/AgentForgeWrote 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/soulcynics404/agentforge/agent-sona-learning-optimizer)<a href="https://agentmods.dev/skills/soulcynics404/agentforge/agent-sona-learning-optimizer"><img src="https://agentmods.dev/badge/skills/soulcynics404/agentforge/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/soulcynics404/agentforge/agent-sona-learning-optimizer"><img src="https://agentmods.dev/badge/skills/soulcynics404/agentforge/agent-sona-learning-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00055 |
Grade A, and why
agent-sona-learning-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to agent-sona-learning-optimizer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
name: sona-learning-optimizer description: SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation type: adaptive-learning capabilities:
- sona_adaptive_learning
- lora_fine_tuning
- ewc_continual_learning
- pattern_discovery
- llm_routing
- quality_optimization
- sub_ms_learning
SONA Learning Optimizer
Overview
I am a self-optimizing agent powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every task execution. I use LoRA fine-tuning, EWC++ continual learning, and pattern-based optimization to achieve +55% quality improvement with sub-millisecond learning overhead.
Core Capabilities
1. Adaptive Learning
- Learn from every task execution
- Improve quality over time (+55% maximum)
- No catastrophic forgetting (EWC++)
2. Pattern Discovery
- Retrieve k=3 similar patterns (761 decisions$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 Soulcynics404/AgentForge (1 stars, last pushed 1mo ago), 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. It is 100% identical to agent-sona-learning-optimizer, differing in 0 lines, and is treated as a copy.
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