VeraSuperHub

End-to-end applied statistical research pipeline. Takes a research question and dataset, runs literature review, statistical analysis with parallel method tracks, and produces a complete manuscript (Markdown + LaTeX/PDF). Use when user says "application pipeline", "applied analysis", "analyze my data and write paper", "end-to-end analysis", or wants to go from raw data to manuscript. Covers all outcome types: continuous, binary, ordinal, nominal, count, survival, repeated measures, time series, multivariate, DOE, meta-analysis, and SEM. Human-in-the-loop by design: the skill handles the standardized workflow, while the user owns study framing, high-stakes judgment, and final sign-off.

Server-side extension that completes the full analysis pipeline for binary outcome variables after vera-data-binary-reviewing has run. Adds remaining association tests (chi-square/Fisher's for additional predictors, point- biserial for continuous predictors), stratified odds ratio analysis with Breslow-Day test and forest plot, full modeling (logistic regression with OR and 95% CI, Hosmer-Lemeshow GOF, pseudo-R2, ROC curve with AUC, classification table, tree-based classification with CART/RF/GBM), and cross-method variable importance comparison on a 0-100 unified scale. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation and code style variation for natural, non-repetitive output. Triggered after vera-data-binary-reviewing completes and its PART 0–2 artifacts (code.R, code.

Runs class balance diagnostics and primary association tests for binary outcome variables (0/1, yes/no, survived/died, pass/fail). Produces proportion tables, class balance check with rare-event warning, descriptives by outcome level, chi-square test of independence with Cramer's V, Fisher's exact test when cell counts are small, odds ratio with 95% CI, and a mosaic or grouped bar chart. Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a binary/dichotomous outcome and says "binary outcome," "survived or died," "yes or no," "pass or fail," "0/1," "classification," "binary DV," or names a binary variable like survived, admitted, defaulted, churned, diagnosed. Does not handle continuous, count, survival time, ordinal, repeated measures, or SEM.

Full CFA skill for measurement-model evaluation. Extends the initial CFA fit with reliability, convergent/discriminant validity, alternative-model comparison, modification-index review, subgroup invariance testing, and manuscript-ready methods/results. Trigger after vera-data-cfa-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.

Complete skill family for confirmatory factor analysis (CFA) and measurement model evaluation. Use when the user asks for CFA, latent factor validation, measurement models, factor loadings, convergent/discriminant validity, or measurement invariance. Uses a two-step structure: testing for initial diagnostics, analyzing for full pipeline.

Runs the CFA pipeline: collect indicator structure, check basic identification and estimator requirements, fit a primary CFA model, and report fit indices plus standardized loadings with a recommendation block for full SEM analysis. Trigger when the user asks for CFA, confirmatory factor analysis, measurement model testing, latent factor validation, factor loading assessment, construct validity, scale validation, measurement invariance check, or psychometric evaluation. Does not cover full structural path modeling (use vera-data-path-reviewing) or longitudinal change modeling (use vera-data-growth-reviewing).

Server-side extension that completes the full analysis pipeline for continuous outcome variables after vera-data-continuous-reviewing has run. Adds remaining hypothesis tests (ANOVA, post-hoc, nonparametric), subgroup analysis with interaction tests and forest plots, full modeling (OLS with diagnostics, quantile regression, tree-based exploratory), and model comparison. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation and code style variation for natural, non-repetitive output. Triggered after vera-data-continuous-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.

Runs distribution diagnostics and primary hypothesis tests for continuous outcome variables. Produces Shapiro-Wilk normality check, skewness, kurtosis, Q-Q plot, and one fully interpreted group comparison (Welch's t for 2 groups or ANOVA with Tukey HSD for 3+ groups) with effect sizes and nonparametric confirmation. Ends with a recommendation block listing Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a continuous/numeric outcome and says "analyze continuous outcome," "my DV is numeric," "compare group means," or names a continuous variable like weight, score, income, time, cost, mpg, blood pressure. Does not handle binary, count, survival, ordinal, repeated measures, or SEM outcomes.

End-to-end applied AI/ML research pipeline. Takes a research question and dataset, runs literature review, multi-method classification/regression analysis with parallel model tracks, and produces a complete manuscript (Markdown + LaTeX/PDF). Use when user says "application pipeline", "applied analysis", "analyze my data and write paper", "end-to-end analysis", or wants to go from raw data to manuscript. Covers all data modalities: NLP text, structured/tabular, and image data.

Server-side extension that completes the full analysis pipeline for image classification after vera-ai-image-reviewing has run. Adds ResNet50, EfficientNet-B0, VGG16, DenseNet121 CNN classifiers with transfer learning (feature extraction and full fine-tuning), subgroup analysis by metadata or image properties, advanced models (ViT transformer with optional tabular fusion and hyperparameter search), ensemble methods, cross-method comparison with unified feature attribution (GradCAM, attention maps) on a 0-100 scale, and manuscript-ready methods.md and results.md. Applies output variation and code style diversity for natural, non-repetitive output. Open-source skill. Triggered after vera-ai-image-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.

Runs data quality diagnostics and baseline image classification. Produces class distribution tables, sample image grids, image size and channel statistics, a baseline CNN or pre-trained feature extractor with Logistic Regression, weighted F1 and macro AUC (bootstrapped 95% CIs), confusion matrix, and ROC curves. Ends with a recommendation block listing additional models available in the analysis workflow. Outputs Python scripts with 2 publication-quality plots. Triggered when user has image data and says "image classification," "computer vision," "CNN," "object recognition," "medical imaging," "image detection," "visual recognition," "photo classification," "X-ray classification," or describes a task involving classifying images into categories. Does not handle free-text NLP or tabular-only data.

End-to-end AI/ML methodology research pipeline. From research direction to publication-ready manuscript with novel architectures, training strategies, or evaluation methods. Includes idea discovery, implementation (model code, ablation studies, benchmark experiments), external review through the configured reviewer bridge, and paper writing (LaTeX + PDF). Use when user says "methodology pipeline", "develop new method", "research pipeline", "full pipeline", "run everything", or wants the complete autonomous AI/ML methodology research workflow. Designed for overnight autonomous execution.

Server-side extension that completes the full analysis pipeline for NLP text classification after vera-ai-nlp-reviewing has run. Adds SVM, Random Forest, LightGBM classifiers with TF-IDF and optional extra features, subgroup analysis by metadata or text properties, deep learning models (GRU, TextCNN, ALBERT with optional tabular fusion and hyperparameter search), cross-method comparison with unified feature importance on a 0-100 scale, and manuscript-ready methods.md and results.md. Applies output variation and code style diversity for natural, non-repetitive output. Open-source skill. Triggered after vera-ai-nlp-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.

Runs class balance diagnostics and baseline text classification for NLP tasks. Produces class distribution tables, text length statistics, vocabulary analysis, TF-IDF feature inspection, a baseline Logistic Regression classifier with weighted F1 and macro AUC (bootstrapped 95% CIs), confusion matrix, and ROC curves. Ends with a recommendation block listing additional models and analyses available in the analysis workflow. Outputs Python scripts with 2 publication-quality plots. Triggered when user has text data and says "text classification," "NLP," "sentiment analysis," "AI detection," "language model detection," "document classification," "spam detection," "text mining," "topic classification," or describes a task involving classifying text into categories. Does not handle structured/tabular-only data or image data.

Server-side extension that completes the full analysis pipeline for structured/tabular data after vera-ai-structured-reviewing has run. Adds SVM, Random Forest, XGBoost, LightGBM, CatBoost classifiers/regressors with missing value imputation, encoding, and scaling, subgroup analysis by metadata variables, deep learning models (MLP, TabNet with optional hyperparameter search), stacking ensemble with meta-learner, cross-method comparison with unified feature importance on a 0-100 scale, and manuscript-ready methods.md and results.md. Supports BOTH classification (F1/AUC) AND regression (RMSE/R2/MAE). Applies output variation and code style diversity for natural, non-repetitive output. Open-source skill. Triggered after vera-ai-structured-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.

Runs data quality diagnostics and baseline classification/regression for structured (tabular) data. Produces missing value analysis, feature distributions, correlation matrix, class balance check, outlier detection, a baseline LightGBM classifier with weighted F1 and macro AUC (bootstrapped 95% CIs), feature importance, confusion matrix, and ROC curves. Ends with a recommendation block listing additional models available in the analysis workflow. Outputs Python scripts with 2 publication-quality plots. Triggered when user has tabular/structured data and says "tabular data," "structured data," "classification," "regression," "feature engineering," "predict from columns," "CSV classification," "spreadsheet," "predict outcome," or describes a task involving predicting from numeric/categorical columns. Does not handle free-text NLP or image data.

Verified birth-chart interpretation workflow for simplified Akashic-record-style and life-lesson readings. Use when the user wants a symbolic soul map, Akashic record, past life reading, life purpose reading, soul blueprint, karmic reading, this-life lesson, soul mission, destiny pattern, BaZi, Zi Wei Dou Shu, Western astrology, traditional Western astrology, Hellenistic astrology, Vedic or Jyotish astrology, numerology, or a reflective spiritual narrative from provided birth data or verified charts. Produces one symbolic record and one mission by default, separates deterministic chart calculation from LLM interpretation, and refuses to invent unverified charts.