How do I install the Vera Data Meta Generating skill?

Run npx skillmds@latest add verasuperhub/vera-data-meta-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.

Is Vera Data Meta Generating safe to use?

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Which AI agents work with Vera Data Meta 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 Meta 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 Meta Generating?

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

Vera Data Meta Generating by verasuperhub

Name: Vera Data Meta Generating
Author: VeraSuperHub

Vera Data Meta Generating

Server-side extension that completes the full analysis pipeline for meta-analysis after vera-data-meta-reviewing has run. Adds publication bias assessment (funnel plot, Egger's test, trim-and-fill), sensitivity analysis (leave-one-out, influence diagnostics, cumulative meta-analysis), subgroup analysis with Q_between, meta-regression with bubble plots, advanced modeling (REML, Knapp-Hartung, Bayesian, three-level), 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-meta-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 6404896 28 files · 960.1 KB Updated Sep 17, 2026

File contents

Meta-Analysis — 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-meta-reviewing stopped (PART 0-2 done).

Step	Responsibility	Executor	Document	Input	Output
Publication bias	Run Additional Tests	Main Agent	`workflow/step04-run-additional-tests.md`	Prior step output	PART 3 code + prose
Subgroup & regression	Analyze Subgroups	Main Agent	`workflow/step05-analyze-subgroups.md`	Prior step output	PART 4 code + prose
Advanced models	Fit Models	Main Agent	`workflow/step06-fit-models.md`	Prior step output	PART 5 code + prose
Model 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, PRISMA, Cochrane)
Research question / hypothesis
Subgroup variable (categorical moderator)
Continuous moderator (for meta-regression)

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-meta-reviewing, plus:

Egger's test: "intercept = X.XX, SE = X.XX, p = .XXX"
Begg's rank test: "Kendall's tau = X.XX, p = .XXX"
Trim-and-fill: "k imputed = X, adjusted ES = X.XX, 95% CI [X.XX, X.XX]"
Meta-regression: "B = X.XX, SE = X.XX, p = .XXX, R² = .XX"
Leave-one-out: "pooled ES ranged from X.XX to X.XX when individual studies removed"
Subgroup differences: "Q_between(df) = X.XX, p = .XXX"
Bayesian: "posterior mean = X.XX, 95% CrI [X.XX, X.XX]"
Three-level: "sigma²_within = X.XX, sigma²_between = X.XX"

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

---name: vera-data-meta-generatingdescription: Server-side extension that completes the full analysis pipeline for meta-analysis after vera-data-meta-reviewing has run. Adds publication bias assessment (funnel plot, Egger's test, trim-and-fill), sensitivity analysis (leave-one-out, influence diagnostics, cumulative meta-analysis), subgroup analysis with Q_between, meta-regression with bubble plots, advanced modeling (REML, Knapp-Hartung, Bayesian, three-level), 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-meta-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.---# Meta-Analysis — 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-meta-reviewing stopped (PART 0-2 done).| Step | Responsibility | Executor | Document | Input | Output ||---|---|---|---|---|---|| Publication bias | Run Additional Tests | Main Agent | `workflow/step04-run-additional-tests.md` | Prior step output | PART 3 code + prose || Subgroup & regression | Analyze Subgroups | Main Agent | `workflow/step05-analyze-subgroups.md` | Prior step output | PART 4 code + prose || Advanced models | Fit Models | Main Agent | `workflow/step06-fit-models.md` | Prior step output | PART 5 code + prose || Model 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, PRISMA, Cochrane)- Research question / hypothesis- Subgroup variable (categorical moderator)- Continuous moderator (for meta-regression)## 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-meta-reviewing, plus:- Egger's test: "intercept = X.XX, SE = X.XX, p = .XXX"- Begg's rank test: "Kendall's tau = X.XX, p = .XXX"- Trim-and-fill: "k imputed = X, adjusted ES = X.XX, 95% CI [X.XX, X.XX]"- Meta-regression: "B = X.XX, SE = X.XX, p = .XXX, R² = .XX"- Leave-one-out: "pooled ES ranged from X.XX to X.XX when individual studies removed"- Subgroup differences: "Q_between(df) = X.XX, p = .XXX"- Bayesian: "posterior mean = X.XX, 95% CrI [X.XX, X.XX]"- Three-level: "sigma²_within = X.XX, sigma²_between = X.XX"## 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-meta-reviewing` or orchestrated by `vera-data-application-pipelining`.

Frequently asked questions