How do I install the Vera Stat Ordinal Testing skill?

Run npx skillmds@latest add aibot88/vera-stat-ordinal-testing 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 Stat Ordinal Testing safe to use?

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Which AI agents work with Vera Stat Ordinal Testing?

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Is Vera Stat Ordinal Testing free to use?

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

Who published Vera Stat Ordinal Testing?

aibot88 (@aibot88) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Vera Stat Ordinal Testing by aibot88

Name: Vera Stat Ordinal Testing
Rating: 4.2 (5 reviews)
Author: aibot88

Vera Stat Ordinal Testing

Runs distribution diagnostics and primary hypothesis tests for ordinal outcome variables. Produces frequency tables, cumulative proportions, ordinal bar chart, and one fully interpreted nonparametric group comparison (Mann-Whitney U for 2 groups or Kruskal-Wallis for 3+ groups) with effect sizes (rank-biserial r or Cliff's delta) and Jonckheere-Terpstra trend test. Ends with a recommendation block listing additional analyses available. Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has an ordinal outcome and says "ordinal outcome," "Likert scale," "ordered categories," "rating scale," "severity levels," "none/mild/moderate/ severe," "low/medium/high," "improvement levels," "ranked outcome," "ordered factor," or names an ordinal variable like satisfaction, severity, agreement, stage, grade, rating. Does not handle continuous, binary, count, survival, repeated measures, or SEM outcomes.

aibot88 Updated Sep 9, 2026 3 repo stars

File contents

Ordinal Outcome — Distribution Diagnostics & Hypothesis Testing

Open-source skill.

Workflow

Read each step file in workflow/ before executing that step.

Step	File	Executor	Output
Collect	`workflow/01-collect-inputs.md`	Main Agent	Structured input summary
Diagnose	`workflow/02-check-distribution.md`	Main Agent	PART 1 code block
Test	`workflow/03-run-primary-test.md`	Main Agent	PART 2-3 code blocks

Decision Tree

1. CHECK DISTRIBUTION
   ├── Balanced levels (no cell < 5) → standard nonparametric tests
   └── Sparse levels (any cell < 5) → warn, consider collapsing levels

2. GROUP COMPARISON
   ├── 2 groups → Mann-Whitney U + rank-biserial r
   └── 3+ groups → Kruskal-Wallis + pairwise Dunn's + Cliff's delta

3. TREND TEST (if predictor is ordered)
   └── Jonckheere-Terpstra trend test

Required Inputs

Role	What to collect
Outcome (Y)	Variable name, ordered levels (confirm ordering with user)
Group variable	What defines groups, how many levels
Predictors	For recommendation block (not executed)
Covariates	For recommendation block (not executed)

Code Structure

PART 0: Setup & Data Loading
PART 1: Distribution Diagnostics → plot_01_ordinal_distribution.png
PART 2: Primary Hypothesis Test  → plot_02_stacked_bar_[var].png
PART 3: Recommendation Block     → text listing additional analyses available

Reporting Standards

p-values: "< .001" not "0.000"; exact to 3 decimals otherwise
Effect sizes: rank-biserial r (Mann-Whitney), Cliff's delta (2 groups), epsilon-squared (Kruskal-Wallis)
Always report medians and IQRs for ordinal data, not means/SDs
Mann-Whitney U: U = X, p = .XXX, rank-biserial r = .XXX
Kruskal-Wallis: H(df) = X.XX, p = .XXX
Proportions per level: report as percentages with 1 decimal
Non-significance: "not statistically significant at α = .05" — never "no effect"

Hypothesis Tests

Scenario	Test	Effect Size
2 independent groups	Mann-Whitney U	Rank-biserial r
3+ independent groups	Kruskal-Wallis + Dunn's	Cliff's delta (pairwise)
Ordered groups (trend)	Jonckheere-Terpstra	—

Paired/repeated designs → vera-stat-repeated-testing.

Example Dataset

R: vcd::Arthritis — Outcome = Improvement (None/Some/Marked), Predictors: Treatment, Sex, Age. Python: reconstruct via statsmodels.datasets or manual construction from R dataset.

Cross-Skill Interface

Output:
├── code_r      → .R script
├── code_python → .py script
├── figures/    → 2 PNGs (ordinal distribution + stacked bar)
└── recommendations → text block (additional analyses available)

---name: vera-stat-ordinal-testingdescription: Runs distribution diagnostics and primary hypothesis tests for ordinal outcome variables. Produces frequency tables, cumulative proportions, ordinal bar chart, and one fully interpreted nonparametric group comparison (Mann-Whitney U for 2 groups or Kruskal-Wallis for 3+ groups) with effect sizes (rank-biserial r or Cliff's delta) and Jonckheere-Terpstra trend test. Ends with a recommendation block listing additional analyses available. Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has an ordinal outcome and says "ordinal outcome," "Likert scale," "ordered categories," "rating scale," "severity levels," "none/mild/moderate/ severe," "low/medium/high," "improvement levels," "ranked outcome," "ordered factor," or names an ordinal variable like satisfaction, severity, agreement, stage, grade, rating. Does not handle continuous, binary, count, survival, repeated measures, or SEM outcomes.--- # Ordinal Outcome — Distribution Diagnostics & Hypothesis Testing Open-source skill. ## Workflow Read each step file in `workflow/` before executing that step. | Step | File | Executor | Output | |---|---|---|---| | Collect | `workflow/01-collect-inputs.md` | Main Agent | Structured input summary | | Diagnose | `workflow/02-check-distribution.md` | Main Agent | PART 1 code block | | Test | `workflow/03-run-primary-test.md` | Main Agent | PART 2-3 code blocks | ## Decision Tree ``` 1. CHECK DISTRIBUTION ├── Balanced levels (no cell < 5) → standard nonparametric tests └── Sparse levels (any cell < 5) → warn, consider collapsing levels 2. GROUP COMPARISON ├── 2 groups → Mann-Whitney U + rank-biserial r └── 3+ groups → Kruskal-Wallis + pairwise Dunn's + Cliff's delta 3. TREND TEST (if predictor is ordered) └── Jonckheere-Terpstra trend test ``` ## Required Inputs | Role | What to collect | |---|---| | **Outcome (Y)** | Variable name, ordered levels (confirm ordering with user) | | **Group variable** | What defines groups, how many levels | | **Predictors** | For recommendation block (not executed) | | **Covariates** | For recommendation block (not executed) | ## Code Structure ``` PART 0: Setup & Data Loading PART 1: Distribution Diagnostics → plot_01_ordinal_distribution.png PART 2: Primary Hypothesis Test → plot_02_stacked_bar_[var].png PART 3: Recommendation Block → text listing additional analyses available ``` ## Reporting Standards 1. p-values: "< .001" not "0.000"; exact to 3 decimals otherwise 2. Effect sizes: rank-biserial r (Mann-Whitney), Cliff's delta (2 groups), epsilon-squared (Kruskal-Wallis) 3. Always report medians and IQRs for ordinal data, not means/SDs 4. Mann-Whitney U: U = X, p = .XXX, rank-biserial r = .XXX 5. Kruskal-Wallis: H(df) = X.XX, p = .XXX 6. Proportions per level: report as percentages with 1 decimal 7. Non-significance: "not statistically significant at α = .05" — never "no effect" ## Hypothesis Tests | Scenario | Test | Effect Size | |---|---|---| | 2 independent groups | Mann-Whitney U | Rank-biserial r | | 3+ independent groups | Kruskal-Wallis + Dunn's | Cliff's delta (pairwise) | | Ordered groups (trend) | Jonckheere-Terpstra | — | Paired/repeated designs → `vera-stat-repeated-testing`. ## Example Dataset R: `vcd::Arthritis` — Outcome = Improvement (None/Some/Marked), Predictors: Treatment, Sex, Age. Python: reconstruct via `statsmodels.datasets` or manual construction from R dataset. ## Cross-Skill Interface ``` Output: ├── code_r → .R script ├── code_python → .py script ├── figures/ → 2 PNGs (ordinal distribution + stacked bar) └── recommendations → text block (additional analyses available) ```

Frequently asked questions