# 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.

- Skill: `verasuperhub/vera-data-doe-generating` (Agent Skill, multi-file: 23 files)
- Install (CLI): `npx skillmds@latest add verasuperhub/vera-data-doe-generating`
- Raw SKILL.md: https://api.skillmd.com/api/skills/verasuperhub/vera-data-doe-generating/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: VeraSuperHub (https://skillmd.com/u/verasuperhub)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/verasuperhub/vera-data-doe-generating

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# 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`.