Skill UI

Utilize the idc-index Python package to access and query massive public datasets from the NCI Imaging Data Commons (IDC). This skill allows users to search, download, and visualize large-scale radiology (CT, MR, PET) and pathology (slide) images. It is essential for AI model training, medical research, and complex medical data analysis.

A powerful command-line interface (CLI) tool, modeled after kubectl/docker, designed for comprehensive management of GPU compute jobs on the Qizhi platform. It supports job submission (interactive/non-interactive), resource discovery, node availability checking, and batch processing for distributed training and HPC workloads. Ideal for machine learning engineers and data scientists running large-scale scientific computations.

This skill simulates a Chief Data Officer's deep interrogation, pressure-testing any plan touching core data strategy. Use it before approving ML model training runs, selecting data infrastructure (warehouse vs. mesh), productizing data assets, or making major team hires. It ensures that all data decisions are grounded in clear business decisions, compliance, and structural necessity.

Dispatches computationally intensive quantitative financial jobs (such as multi-year walk-forward backtesting, large Monte-Carlo simulations, parameter sweeps, and deep model training using neural-trader) to a managed cloud agent. This is necessary for workloads that exceed local computational limits, providing robust, scalable infrastructure for professional quantitative research.

This skill provides a comprehensive workflow for training advanced neural network models, including LSTM, Transformer, and N-BEATS, using historical market data. It facilitates model training, prediction generation with confidence intervals, performance comparison across different model types, and the structured storage of results for further analysis and SONA training.