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Run npx skillmds@latest add verasuperhub/vera-data-ordinal-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 Ordinal Generating free to use?

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Who published Vera Data Ordinal Generating?

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

Vera Data Ordinal Generating by verasuperhub

Name: Vera Data Ordinal Generating
Author: VeraSuperHub

Vera Data Ordinal Generating

Server-side extension that completes the full analysis pipeline for ordinal outcome variables after vera-data-ordinal-reviewing has run. Adds nonparametric tests, subgroup analysis, and dual-path modeling: Path A ignores ordering (multinomial logistic, CART, RF, LightGBM) while Path B respects ordering (proportional odds with Brant test, adjacent-category logit, continuation-ratio logit, stereotype model, ordinal-aware trees and LightGBM). Cross-path synthesis compares variable importance rankings across both paths. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation, code and code style variation for natural, non-repetitive output. Triggered after vera-data-ordinal-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md).

VeraSuperHub 63ef922 19 files · 421.3 KB Updated Sep 17, 2026

File contents

Ordinal Outcome — 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-ordinal-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, STROBE, etc.)
Research question / hypothesis
Subgroup variable (if subgroup analysis desired)

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-ordinal-reviewing, plus:

Cumulative odds ratios: always with 95% CI, "cumulative OR = X.XX, 95% CI [X.XX, X.XX]"
Proportional odds assumption: Brant test chi-squared(df) = X.XX, p = .XXX; if violated, state which predictors
Coefficients: log-odds with SE always; report cumulative OR for interpretation
If proportional odds violated: report adjacent-category, continuation-ratio, and stereotype models
Multinomial logistic (Path A): class-specific OR with 95% CI per class contrast
Adjacent-category logit: adjacent-category OR with 95% CI
Continuation-ratio logit: continuation-ratio OR with 95% CI
Stereotype model: scaling parameters with coefficients, 95% CI
LightGBM importance: gain-based, normalized to 0-100
Tree-based with small N: frame as "exploratory"; never claim predictive validity
Spearman rho: report with 95% CI when available

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-ordinal-reviewing or orchestrated by vera-data-application-pipelining.

---name: vera-data-ordinal-generatingdescription: Server-side extension that completes the full analysis pipeline for ordinal outcome variables after vera-data-ordinal-reviewing has run. Adds nonparametric tests, subgroup analysis, and dual-path modeling: Path A ignores ordering (multinomial logistic, CART, RF, LightGBM) while Path B respects ordering (proportional odds with Brant test, adjacent-category logit, continuation-ratio logit, stereotype model, ordinal-aware trees and LightGBM). Cross-path synthesis compares variable importance rankings across both paths. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation, code and code style variation for natural, non-repetitive output. Triggered after vera-data-ordinal-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md).---# Ordinal Outcome — 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-ordinal-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, STROBE, etc.)- Research question / hypothesis- Subgroup variable (if subgroup analysis desired)## 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-ordinal-reviewing, plus:- Cumulative odds ratios: always with 95% CI, "cumulative OR = X.XX, 95% CI [X.XX, X.XX]"- Proportional odds assumption: Brant test chi-squared(df) = X.XX, p = .XXX; if violated, state which predictors- Coefficients: log-odds with SE always; report cumulative OR for interpretation- If proportional odds violated: report adjacent-category, continuation-ratio, and stereotype models- Multinomial logistic (Path A): class-specific OR with 95% CI per class contrast- Adjacent-category logit: adjacent-category OR with 95% CI- Continuation-ratio logit: continuation-ratio OR with 95% CI- Stereotype model: scaling parameters with coefficients, 95% CI- LightGBM importance: gain-based, normalized to 0-100- Tree-based with small N: frame as "exploratory"; never claim predictive validity- Spearman rho: report with 95% CI when available## 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-ordinal-reviewing` or orchestrated by `vera-data-application-pipelining`.

Frequently asked questions