Entry-point router for the HABIT (Habitat Analysis Biomedical Imaging Toolkit) package. Use this FIRST whenever a user mentions HABIT, habitat analysis, tumor sub-region clustering, intra-tumor heterogeneity, "生境分析", "亚区聚类", "habitat segmentation", or wants a multi-step radiomics workflow. Verifies environment, validates data layout, then hands off to the right specialist skill.
Diagnose and fix HABIT runtime errors — covers preprocess, habitat clustering, feature extraction, and ML/comparison failures. Use when the user pastes a Python traceback, when a `habit ...` CLI command fails, when an output file is missing or corrupt, or when results look biologically wrong (degenerate clusters, all-NaN features, AUC=0.5). Triggers on phrases like "报错", "出错", "不工作", "为什么", "error", "failed", "traceback", "crash", "AUC 太低", "没有生成结果".
Extract quantitative features from HABIT habitat maps — traditional radiomics, whole-habitat radiomics, per-habitat radiomics, MSI (Most Significant Intensity), and ITH (Intra-Tumor Heterogeneity) scores. Use when the user has habitat .nrrd maps and wants per-subject feature CSVs ready for ML. Triggers on "提取生境特征", "MSI 特征", "ITH 异质性", "habitat features", "extract radiomics from habitats". Runs `habit extract`.
Generate tumor habitat (sub-region) maps from medical images using voxel-level clustering. Use when the user wants to identify intra-tumor heterogeneity zones, perform supervoxel clustering, generate habitat .nrrd maps, run kinetic DCE habitat, or use voxel-radiomics texture clustering. Triggers on "生境分析", "亚区聚类", "habitat", "supervoxel", "kinetic", "DCE 分期生境", "intra-tumor heterogeneity". Runs `habit get-habitat`.
Train, predict, or k-fold cross-validate ML classifiers on habitat / radiomics feature CSVs using HABIT. Supports LogisticRegression, RandomForest, XGBoost, SVM, MLP, AutoGluon, and 12+ feature selection methods (LASSO, mRMR, RFECV, ICC, correlation, ANOVA, ...). Use when the user wants to build a prediction model from feature CSVs. Triggers on "训练模型", "建模", "K折交叉验证", "k-fold", "LASSO", "feature selection", "predict mode". Runs `habit model` or `habit cv`.
Compare multiple trained classification models with publication-quality plots — ROC, DCA, calibration, precision-recall, DeLong's AUC test. Use when the user has prediction CSVs from 2+ models and wants side-by-side comparison. Triggers on "模型比较", "ROC 对比", "DeLong 检验", "决策曲线", "校准曲线", "model comparison", "compare AUC", "DCA". Runs `habit compare`.
Extract traditional PyRadiomics features (firstorder, shape, GLCM, GLRLM, GLSZM, NGTDM, GLDM) from medical images at the whole-tumor ROI level — no habitat segmentation. Use when the user wants classical radiomics features without doing habitat analysis. Triggers on "传统影像组学", "PyRadiomics", "radiomics features", "shape features", "GLCM". Runs `habit radiomics`.
End-to-end HABIT workflow recipes for common research scenarios (multi-modal MRI habitat, DCE-MRI kinetic, CT-only radiomics, test-retest reproducibility, demo dataset walkthrough). Use when the user wants to run the entire pipeline (preprocess -> habitat -> features -> ML -> comparison) instead of one isolated step. Triggers on phrases like "全流程", "端到端", "整个流程跑一遍", "demo 走一遍", "full pipeline", "end-to-end", "complete workflow".