How do I install the Vera Stat Continuous Testing skill?

Run npx skillmds@latest add aibot88/vera-stat-continuous-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 Continuous Testing safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Vera Stat Continuous Testing?

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 Stat Continuous 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 Continuous Testing?

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

Vera Stat Continuous Testing by aibot88

Name: Vera Stat Continuous Testing
Rating: 4.5 (4 reviews)
Author: aibot88

Vera Stat Continuous Testing

Runs distribution diagnostics and primary hypothesis tests for continuous outcome variables. Produces Shapiro-Wilk normality check, skewness, kurtosis, Q-Q plot, and one fully interpreted group comparison (Welch's t for 2 groups or ANOVA with Tukey HSD for 3+ groups) with effect sizes and nonparametric confirmation. Ends with a recommendation block listing Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a continuous/numeric outcome and says "analyze continuous outcome," "my DV is numeric," "compare group means," or names a continuous variable like weight, score, income, time, cost, mpg, blood pressure. Does not handle binary, count, survival, ordinal, repeated measures, or SEM outcomes.

aibot88 Updated Sep 9, 2026 3 repo stars

File contents

Continuous 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
   ├── Normal (Shapiro-Wilk p ≥ .05, |skewness| < 1) → parametric primary
   └── Non-normal → nonparametric primary + recommend QR/trees

2. GROUP COMPARISON
   ├── 2 groups → Welch's t + Cohen's d + Mann-Whitney U
   └── 3+ groups → ANOVA + η² + Tukey HSD + Kruskal-Wallis

Required Inputs

Role	What to collect
Outcome (Y)	Variable name, units, what it measures
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_distribution.png
PART 2: Primary Hypothesis Test  → plot_02_boxplot_[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: Cohen's d (t-test), η² (ANOVA) — always alongside p
95% CIs: always for mean differences
Degrees of freedom: always with t and F statistics
Sample size: final analytic N
Decimal places: 2 for M/SD, 3 for p and effect sizes
Non-significance: "not statistically significant at α = .05" — never "no effect"

Hypothesis Tests

Scenario	Normal	Non-Normal
2 independent groups	Welch's t	Mann-Whitney U
3+ independent groups	ANOVA + Tukey HSD	Kruskal-Wallis + Dunn's

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

Example Dataset

R built-in mtcars: outcome = mpg, 2-group = am, 3+ group = cyl. Python: sm.datasets.get_rdataset("mtcars").data (with offline fallback to bundled examples/mtcars.csv).

Cross-Skill Interface

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

---name: vera-stat-continuous-testingdescription: Runs distribution diagnostics and primary hypothesis tests for continuous outcome variables. Produces Shapiro-Wilk normality check, skewness, kurtosis, Q-Q plot, and one fully interpreted group comparison (Welch's t for 2 groups or ANOVA with Tukey HSD for 3+ groups) with effect sizes and nonparametric confirmation. Ends with a recommendation block listing Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a continuous/numeric outcome and says "analyze continuous outcome," "my DV is numeric," "compare group means," or names a continuous variable like weight, score, income, time, cost, mpg, blood pressure. Does not handle binary, count, survival, ordinal, repeated measures, or SEM outcomes.--- # Continuous 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 ├── Normal (Shapiro-Wilk p ≥ .05, |skewness| < 1) → parametric primary └── Non-normal → nonparametric primary + recommend QR/trees 2. GROUP COMPARISON ├── 2 groups → Welch's t + Cohen's d + Mann-Whitney U └── 3+ groups → ANOVA + η² + Tukey HSD + Kruskal-Wallis ``` ## Required Inputs | Role | What to collect | |---|---| | **Outcome (Y)** | Variable name, units, what it measures | | **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_distribution.png PART 2: Primary Hypothesis Test → plot_02_boxplot_[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: Cohen's d (t-test), η² (ANOVA) — always alongside p 3. 95% CIs: always for mean differences 4. Degrees of freedom: always with t and F statistics 5. Sample size: final analytic N 6. Decimal places: 2 for M/SD, 3 for p and effect sizes 7. Non-significance: "not statistically significant at α = .05" — never "no effect" ## Hypothesis Tests | Scenario | Normal | Non-Normal | |---|---|---| | 2 independent groups | Welch's t | Mann-Whitney U | | 3+ independent groups | ANOVA + Tukey HSD | Kruskal-Wallis + Dunn's | Paired/repeated designs → `vera-stat-repeated-testing`. ## Example Dataset R built-in `mtcars`: outcome = mpg, 2-group = am, 3+ group = cyl. Python: `sm.datasets.get_rdataset("mtcars").data` (with offline fallback to bundled `examples/mtcars.csv`). ## Cross-Skill Interface ``` Output: ├── code_r → .R script ├── code_python → .py script ├── figures/ → 2 PNGs (distribution + boxplot) └── recommendations → text block (additional analyses available) ```

Frequently asked questions