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.