Local Model Quantization Router

Use this skill to choose between local quantized models and cloud fallback before running OpenClaw workloads.

Workflow

1. Describe available hardware and task requirements.
2. Run scripts/local_model_quantization_router.py with CLI flags or JSON input.
3. Review the recommended model family, quantization level, endpoint, fallback, and risk notes.
4. Use the output as routing evidence for local-first or privacy-first deployments.

Parameters

• --task TEXT: Task summary.
• --complexity {simple,standard,complex,critical}.
• --privacy {low,normal,high,regulated}.
• --vram-gb FLOAT: GPU memory available.
• --ram-gb FLOAT: System memory available.
• --context-tokens INT: Required context window.
• --hardware PATH: Optional JSON with vram_gb, ram_gb, cpu_only.
• --output PATH: Optional JSON output path.

Outputs

• route: local-only, local-first, hybrid, or cloud-required.
• model: Recommended model family.
• quantization: Suggested quant level.
• endpoint: Suggested local endpoint type.
• fallback: Safer fallback when quality or context is insufficient.
• reasons: Evidence for the decision.

No model is downloaded and no config file is changed.