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Run npx skillmds@latest add verasuperhub/vera-data-doe-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.

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Is Vera Data Doe Generating free to use?

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Who published Vera Data Doe Generating?

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

Vera Data Doe Generating by verasuperhub

Name: Vera Data Doe Generating
Author: VeraSuperHub

Vera Data Doe Generating

Server-side extension that completes the full analysis pipeline for designed experiments (DOE) after vera-data-doe-reviewing has run. Adds simple effects analysis, contrast analysis, effect magnitude ranking, response surface methodology (RSM) with contour plots and canonical analysis, fractional factorial alias structure, split-plot error terms, residual diagnostics, optimal factor settings via desirability function, and tree-based variable importance (RF + LightGBM). Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation, code style variation for natural, non-repetitive output. Triggered after vera-data-doe-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 b207f22 23 files · 2.0 MB Updated Sep 17, 2026

File contents

Design of Experiments -- Full Analysis & Manuscript Generation

Open-source skill. Read reference/specs/output-variation-protocol.md before every generation -- apply all variation layers.

Workflow

Continues from where vera-data-doe-reviewing stopped (PART 0-2 done).

Step	Responsibility	Executor	Document	Input	Output
Additional tests	Run Additional Tests	Main Agent	`workflow/step04-run-additional-tests.md`	Prior step output	PART 3 code + prose
Subgroup	Analyze Subgroups	Main Agent	`workflow/step05-analyze-subgroups.md`	Prior step output	PART 4 code + prose
Modeling	Fit Models	Main Agent	`workflow/step06-fit-models.md`	Prior step output	PART 5 code + prose
Comparison	Compare Models	Main Agent	`workflow/step07-compare-models.md`	Prior step output	PART 6 code + prose
Manuscript	Generate Manuscript	Main Agent	`workflow/step08-generate-manuscript.md`	Prior step output	methods.md + results.md

Additional Inputs

Collect if not already provided:

Target discipline (for reporting conventions)
Target journal or style (APA 7th, CONSORT for experiments, etc.)
Research question / hypothesis
Whether optimization of response is desired (maximize, minimize, target)

Output Structure

output/
├── methods.md
├── results.md
├── tables/             <- Markdown + CSV per table
├── figures/            <- PNGs, 300 DPI
├── references.bib
├── code.R              <- Style-varied
└── code.py             <- Style-varied

Key References (read before generation)

File	Purpose
`reference/specs/output-variation-protocol.md`	Output quality variation layers
`reference/specs/code-style-variation.md`	Seven-dimension code style diversity
`reference/patterns/sentence-bank.md`	4-6 phrasings per result type
`reference/rules/reporting-standards.md`	Hard rules for statistical reporting

Reporting Standards

Same as vera-data-doe-reviewing, plus:

SS Type III always for unbalanced designs
F(df1, df2) = X.XX, p, partial eta-squared for every effect
Effect estimates with SE for all contrasts
Design resolution for fractional factorial
R-squared for RSM models (these ARE experiments, so "explained" is appropriate)
Contour plot interpretation: saddle point, maximum, minimum, or ridge
Canonical analysis: eigenvalues and eigenvectors for second-order RSM
Tree-based with small N: frame as "exploratory"; never claim predictive validity

Cross-Skill Interface

Method Unit Contract:
├── code_r           -> .R script (style-varied)
├── code_python      -> .py script (style-varied)
├── methods_md       -> methods.md (varied structure)
├── results_md       -> results.md (varied phrasing)
├── tables/          -> Markdown + CSV
├── figures/         -> PNGs 300 DPI (varied layout)
├── references_bib   -> .bib with cited references
└── comparison       -> cross-method narrative (in results.md)

Invoked directly after vera-data-doe-reviewing or orchestrated by vera-data-application-pipelining.

---name: vera-data-doe-generatingdescription: Server-side extension that completes the full analysis pipeline for designed experiments (DOE) after vera-data-doe-reviewing has run. Adds simple effects analysis, contrast analysis, effect magnitude ranking, response surface methodology (RSM) with contour plots and canonical analysis, fractional factorial alias structure, split-plot error terms, residual diagnostics, optimal factor settings via desirability function, and tree-based variable importance (RF + LightGBM). Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation, code style variation for natural, non-repetitive output. Triggered after vera-data-doe-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.---# Design of Experiments -- Full Analysis & Manuscript GenerationOpen-source skill. Read `reference/specs/output-variation-protocol.md`before every generation -- apply all variation layers.## WorkflowContinues from where vera-data-doe-reviewing stopped (PART 0-2 done).| Step | Responsibility | Executor | Document | Input | Output ||---|---|---|---|---|---|| Additional tests | Run Additional Tests | Main Agent | `workflow/step04-run-additional-tests.md` | Prior step output | PART 3 code + prose || Subgroup | Analyze Subgroups | Main Agent | `workflow/step05-analyze-subgroups.md` | Prior step output | PART 4 code + prose || Modeling | Fit Models | Main Agent | `workflow/step06-fit-models.md` | Prior step output | PART 5 code + prose || Comparison | Compare Models | Main Agent | `workflow/step07-compare-models.md` | Prior step output | PART 6 code + prose || Manuscript | Generate Manuscript | Main Agent | `workflow/step08-generate-manuscript.md` | Prior step output | methods.md + results.md |## Additional InputsCollect if not already provided:- Target discipline (for reporting conventions)- Target journal or style (APA 7th, CONSORT for experiments, etc.)- Research question / hypothesis- Whether optimization of response is desired (maximize, minimize, target)## Output Structure```output/├── methods.md├── results.md├── tables/ <- Markdown + CSV per table├── figures/ <- PNGs, 300 DPI├── references.bib├── code.R <- Style-varied└── code.py <- Style-varied```## Key References (read before generation)| File | Purpose ||---|---|| `reference/specs/output-variation-protocol.md` | Output quality variation layers || `reference/specs/code-style-variation.md` | Seven-dimension code style diversity || `reference/patterns/sentence-bank.md` | 4-6 phrasings per result type || `reference/rules/reporting-standards.md` | Hard rules for statistical reporting |## Reporting StandardsSame as vera-data-doe-reviewing, plus:- SS Type III always for unbalanced designs- F(df1, df2) = X.XX, p, partial eta-squared for every effect- Effect estimates with SE for all contrasts- Design resolution for fractional factorial- R-squared for RSM models (these ARE experiments, so "explained" is appropriate)- Contour plot interpretation: saddle point, maximum, minimum, or ridge- Canonical analysis: eigenvalues and eigenvectors for second-order RSM- Tree-based with small N: frame as "exploratory"; never claim predictive validity## Cross-Skill Interface```Method Unit Contract:├── code_r -> .R script (style-varied)├── code_python -> .py script (style-varied)├── methods_md -> methods.md (varied structure)├── results_md -> results.md (varied phrasing)├── tables/ -> Markdown + CSV├── figures/ -> PNGs 300 DPI (varied layout)├── references_bib -> .bib with cited references└── comparison -> cross-method narrative (in results.md)```Invoked directly after `vera-data-doe-reviewing` or orchestrated by `vera-data-application-pipelining`.

Frequently asked questions