How do I install the Vera Data Cfa Generating skill?

Run npx skillmds@latest add verasuperhub/vera-data-cfa-generating in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Vera Data Cfa Generating safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Vera Data Cfa Generating?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Vera Data Cfa Generating free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Vera Data Cfa Generating?

VeraSuperHub (@verasuperhub) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Vera Data Cfa Generating by verasuperhub

Name: Vera Data Cfa Generating
Author: VeraSuperHub

Vera Data Cfa Generating

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.

VeraSuperHub 29b6cad 20 files · 100.0 KB Updated Sep 17, 2026

File contents

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

---name: vera-data-cfa-generatingdescription: 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.---# CFA Analyzing — Full Measurement Model & ManuscriptOpen-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 InputsCollect 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 |

Frequently asked questions