원클릭으로 Manus에서 모든 스킬 실행

vera-data-growth-generating

스타1

포크0

업데이트2026년 5월 16일 23:54

Full longitudinal SEM skill for latent growth and change models. Extends the initial fit with nonlinear trajectories, latent-basis and change-score variants, subgroup comparisons, parallel-process models when available, and manuscript-ready output. Trigger after vera-data-growth-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.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

VeraSuperHub

VeraSuperHub/stat-research-pipeline

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Longitudinal Change Analyzing — Full Growth / Change Pipeline

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	Nonlinear/change diagnostics
Subgroup	Analyze Subgroups	Main Agent	`workflow/step05-analyze-subgroups.md`	Prior step output	Multigroup growth/change
Modeling	Fit Models	Main Agent	`workflow/step06-fit-models.md`	Prior step output	Alternative growth/change models
Comparison	Compare Models	Main Agent	`workflow/step07-compare-models.md`	Prior step output	Cross-trajectory synthesis
Manuscript	Generate Manuscript	Main Agent	`workflow/step08-generate-manuscript.md`	Prior step output	methods.md + results.md

이 저장소의 다른 Skills

같은 저장소

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.

2026-05-161

vera-data-binary-generating

VeraSuperHub/stat-research-pipeline

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.

2026-05-161

vera-data-binary-reviewing

VeraSuperHub/stat-research-pipeline

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-generating

VeraSuperHub/stat-research-pipeline

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.

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.

2026-05-161

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).

2026-05-161

name	vera-data-growth-generating
description	Full longitudinal SEM skill for latent growth and change models. Extends the initial fit with nonlinear trajectories, latent-basis and change-score variants, subgroup comparisons, parallel-process models when available, and manuscript-ready output. Trigger after vera-data-growth-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

Longitudinal Change Analyzing — Full Growth / Change Pipeline

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	Nonlinear/change diagnostics
Subgroup	Analyze Subgroups	Main Agent	`workflow/step05-analyze-subgroups.md`	Prior step output	Multigroup growth/change
Modeling	Fit Models	Main Agent	`workflow/step06-fit-models.md`	Prior step output	Alternative growth/change models
Comparison	Compare Models	Main Agent	`workflow/step07-compare-models.md`	Prior step output	Cross-trajectory synthesis
Manuscript	Generate Manuscript	Main Agent	`workflow/step08-generate-manuscript.md`	Prior step output	methods.md + results.md