How do I install the Vera Data Continuous Generating skill?

Run npx skillmds@latest add verasuperhub/vera-data-continuous-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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Which AI agents work with Vera Data Continuous Generating?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Vera Data Continuous Generating free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Vera Data Continuous Generating?

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

Vera Data Continuous Generating by verasuperhub

Name: Vera Data Continuous Generating
Author: VeraSuperHub

Vera Data Continuous Generating

Server-side extension that completes the full analysis pipeline for continuous outcome variables after vera-data-continuous-reviewing has run. Adds remaining hypothesis tests (ANOVA, post-hoc, nonparametric), subgroup analysis with interaction tests and forest plots, full modeling (OLS with diagnostics, quantile regression, tree-based exploratory), and model comparison. 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-continuous-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 95464b8 29 files · 1.0 MB Updated Sep 17, 2026

File contents

Continuous 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-continuous-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-continuous-reviewing, plus:

Coefficients: unstandardized B with SE always; add β when predictors on different scales
R²: "accounted for X% of variance" — never "explained" unless true experiment
Quantile regression: report specific quantile(s) and SE method (bootstrap preferred)
Tree-based with small N: frame as "exploratory"; never claim predictive validity
Transformations: report on both transformed and original scales

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

---name: vera-data-continuous-generatingdescription: Server-side extension that completes the full analysis pipeline for continuous outcome variables after vera-data-continuous-reviewing has run. Adds remaining hypothesis tests (ANOVA, post-hoc, nonparametric), subgroup analysis with interaction tests and forest plots, full modeling (OLS with diagnostics, quantile regression, tree-based exploratory), and model comparison. 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-continuous-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.---# Continuous 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-continuous-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-continuous-reviewing, plus:- Coefficients: unstandardized B with SE always; add β when predictors on different scales- R²: "accounted for X% of variance" — never "explained" unless true experiment- Quantile regression: report specific quantile(s) and SE method (bootstrap preferred)- Tree-based with small N: frame as "exploratory"; never claim predictive validity- Transformations: report on both transformed and original scales## 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-continuous-reviewing` or orchestrated by `vera-data-application-pipelining`.

Frequently asked questions