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

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Who published Vera Data Count Generating?

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

Vera Data Count Generating by verasuperhub

Name: Vera Data Count Generating
Author: VeraSuperHub

Vera Data Count Generating

Server-side extension that completes the full analysis pipeline for count outcome variables after vera-data-count-reviewing has run. Adds remaining hypothesis tests (additional group comparisons, continuous predictor correlations), subgroup analysis with stratified rate ratios and interaction tests, full modeling (Poisson, Negative Binomial, Zero-Inflated Poisson, Zero-Inflated Negative Binomial, Hurdle, tree-based exploratory), and model comparison with unified variable importance. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation and code style variation for natural, non-repetitive output. Triggered after vera-data-count-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 90647b3 27 files · 710.2 KB Updated Sep 17, 2026

File contents

Count 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-count-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-count-reviewing, plus:

IRR: always with 95% CI, "IRR = X.XX, 95% CI [X.XX, X.XX]"
Rate: "X.XX events per [unit] of exposure"
Overdispersion: report deviance/df ratio
Poisson vs NB: LR test chi-sq(1), p
ZIP/ZINB: Vuong test statistic, p
Hurdle: AIC comparison + conceptual justification
Count means: report with SD (not SE unless explicitly comparing)
Coefficients: log-scale B with SE always; exponentiate to IRR for interpretation
Tree-based with small N: frame as "exploratory"; never claim predictive validity
Model selection: frame as "which distributional assumption fits the data" not "which model wins"

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

---name: vera-data-count-generatingdescription: Server-side extension that completes the full analysis pipeline for count outcome variables after vera-data-count-reviewing has run. Adds remaining hypothesis tests (additional group comparisons, continuous predictor correlations), subgroup analysis with stratified rate ratios and interaction tests, full modeling (Poisson, Negative Binomial, Zero-Inflated Poisson, Zero-Inflated Negative Binomial, Hurdle, tree-based exploratory), and model comparison with unified variable importance. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation and code style variation for natural, non-repetitive output. Triggered after vera-data-count-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.---# Count 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-count-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-count-reviewing, plus:- IRR: always with 95% CI, "IRR = X.XX, 95% CI [X.XX, X.XX]"- Rate: "X.XX events per [unit] of exposure"- Overdispersion: report deviance/df ratio- Poisson vs NB: LR test chi-sq(1), p- ZIP/ZINB: Vuong test statistic, p- Hurdle: AIC comparison + conceptual justification- Count means: report with SD (not SE unless explicitly comparing)- Coefficients: log-scale B with SE always; exponentiate to IRR for interpretation- Tree-based with small N: frame as "exploratory"; never claim predictive validity- Model selection: frame as "which distributional assumption fits the data" not "which model wins"## 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-count-reviewing` or orchestrated by `vera-data-application-pipelining`.

Frequently asked questions