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vera-data-cfa-generating

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更新日2026年5月16日 23:54

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.

インストール

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VeraSuperHub

VeraSuperHub/stat-research-pipeline

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CFA Analyzing — Full Measurement Model & Manuscript

Open-source skill.

Read reference/specs/output-variation-protocol.md before every generation.

Workflow

Step	Responsibility	Executor	Document	Input	Output
Additional tests	Run Additional Tests	Main Agent	`workflow/step04-run-additional-tests.md`	Prior step output	Reliability + validity code/prose
Subgroup	Analyze Subgroups	Main Agent	`workflow/step05-analyze-subgroups.md`	Prior step output	Measurement invariance code/prose
Modeling	Fit Models	Main Agent	`workflow/step06-fit-models.md`	Prior step output	Alternative CFA models
Comparison	Compare Models	Main Agent	`workflow/step07-compare-models.md`	Prior step output	Cross-model synthesis
Manuscript	Generate Manuscript	Main Agent	`workflow/step08-generate-manuscript.md`	Prior step output	methods.md + results.md

Additional Inputs

Collect if missing:

target discipline / journal style
whether theory allows correlated residuals or cross-loadings
planned grouping variable for invariance
whether ordinal indicators should force a categorical estimator

Output Structure

output/
├── methods.md
├── results.md
├── tables/
├── figures/
├── references.bib
├── code.R
└── code.py

Key References

File	Purpose
`reference/specs/output-variation-protocol.md`	Output quality variation
`reference/specs/code-style-variation.md`	Code style diversity
`reference/patterns/sentence-bank.md`	SEM wording variants
`reference/rules/reporting-standards.md`	CFA reporting rules

このリポジトリの他の Skills

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vera-data-application-pipelining

VeraSuperHub/stat-research-pipeline

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.

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vera-data-binary-generating

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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.

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vera-data-binary-reviewing

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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.

2026-05-161

vera-data-cfa-pipelining

VeraSuperHub/stat-research-pipeline

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.

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vera-data-cfa-reviewing

VeraSuperHub/stat-research-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).

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vera-data-continuous-generating

VeraSuperHub/stat-research-pipeline

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.

2026-05-161

name	vera-data-cfa-generating
description	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.
allowed-tools	Read, Bash, Write, Edit, Grep, Glob

CFA Analyzing — Full Measurement Model & Manuscript

Open-source skill.

Read reference/specs/output-variation-protocol.md before every generation.

Workflow

Step	Responsibility	Executor	Document	Input	Output
Additional tests	Run Additional Tests	Main Agent	`workflow/step04-run-additional-tests.md`	Prior step output	Reliability + validity code/prose
Subgroup	Analyze Subgroups	Main Agent	`workflow/step05-analyze-subgroups.md`	Prior step output	Measurement invariance code/prose
Modeling	Fit Models	Main Agent	`workflow/step06-fit-models.md`	Prior step output	Alternative CFA models
Comparison	Compare Models	Main Agent	`workflow/step07-compare-models.md`	Prior step output	Cross-model synthesis
Manuscript	Generate Manuscript	Main Agent	`workflow/step08-generate-manuscript.md`	Prior step output	methods.md + results.md

Additional Inputs

Collect if missing:

target discipline / journal style
whether theory allows correlated residuals or cross-loadings
planned grouping variable for invariance
whether ordinal indicators should force a categorical estimator

Output Structure

output/
├── methods.md
├── results.md
├── tables/
├── figures/
├── references.bib
├── code.R
└── code.py

Key References

File	Purpose
`reference/specs/output-variation-protocol.md`	Output quality variation
`reference/specs/code-style-variation.md`	Code style diversity
`reference/patterns/sentence-bank.md`	SEM wording variants
`reference/rules/reporting-standards.md`	CFA reporting rules