llm-config — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited llm-config (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Configure RuVLLM for local inference and fine-tuning.
When you need to configure local LLM inference, create MicroLoRA adapters for task-specific fine-tuning, or set up SONA for real-time adaptation.
mcp__claude-flow__ruvllm_status to see current model and adapter statemcp__claude-flow__ruvllm_generate_config with model parametersmcp__claude-flow__ruvllm_microlora_create for task-specific adaptersmcp__claude-flow__ruvllm_microlora_adapt with training datamcp__claude-flow__ruvllm_sona_create for real-time neural adaptationmcp__claude-flow__ruvllm_sona_adapt with feedback signals| Feature | MicroLoRA | SONA |
|---|---|---|
| Speed | Minutes to train | <0.05ms adaptation |
| Scope | Task-specific fine-tuning | Real-time micro-adjustments |
| Persistence | Saved as adapter weights | Session-scoped |
| Use case | Specialized domain tasks | Continuous feedback loops |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.