Skill UI

Provides calibration-free weight quantization for LLMs down to 1-bit precision with multiple optimized backends, ideal for fast deployments with vLLM/HuggingFace and LoRA/PEFT fine-tuning without collecting calibration data.

Automates mixed precision ML training workflows by surfacing best practices, generating runnable code/configurations, and validating setups across data prep, model tuning, and experiment tracking.

Optimizes large language model inference on NVIDIA GPUs with TensorRT, delivering 10-100× faster throughput, quantized precision (FP8/INT4), multi-GPU scaling, and serving-ready tooling for production deployments.

Transforms vague or inconsistent prompts into precision-engineered instructions so any LLM delivers structured, hallucination-resistant, token-efficient outputs.

This skill guides users in analyzing complex questions related to criminal or legal incidents (e.g., murder, theft, arson, accident). By first classifying the specific case type and identifying domain-specific keywords, it dramatically improves the precision of subsequent search queries. It teaches users how to combine case type terms with distinctive constraints like amounts, dates, and locations for maximum search effectiveness.