How do I install the Vera Stat Binary Testing skill?

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

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Which AI agents work with Vera Stat Binary 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 Binary 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 Binary Testing?

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

Vera Stat Binary Testing by aibot88

Name: Vera Stat Binary Testing
Rating: 4.666666666666667 (3 reviews)
Author: aibot88

Vera Stat Binary Testing

Runs class balance diagnostics and primary association tests for binary outcome variables (0/1, yes/no, survived/died, pass/fail). Produces proportion tables, class balance check with rare-event warning, descriptives by outcome level, chi-square test of independence with Cramer's V, Fisher's exact test when cell counts are small, odds ratio with 95% CI, and a mosaic or grouped bar chart. Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a binary/dichotomous outcome and says "binary outcome," "logistic," "survived or died," "yes or no," "pass or fail," "0/1," "classification," "binary DV," or names a binary variable like survived, admitted, defaulted, churned, diagnosed. Does not handle continuous, count, survival time, ordinal, repeated measures, or SEM.

aibot88 Updated Sep 9, 2026 3 repo stars

File contents

Binary Outcome --- Class Balance Diagnostics & Association 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 CLASS BALANCE
   ├── Balanced (minority ≥ 10%) → standard tests
   └── Imbalanced (minority < 10%) → rare event warning, note power limits

2. PRIMARY ASSOCIATION TEST
   ├── All cells ≥ 5 → Chi-square + Cramer's V + OR
   └── Any cell < 5 → Fisher's exact + OR

Required Inputs

Role	What to collect
Outcome (Y)	Variable name, what 0/1 represents
Primary 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: Class Balance Diagnostics  → plot_01_class_balance.png
PART 2: Primary Association Test   → plot_02_mosaic_[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: Cramer's V (chi-square), odds ratio with 95% CI
Odds ratios: always as "OR = X.XX, 95% CI [X.XX, X.XX]"
Proportions: report as percentages with 1 decimal
Chi-square: chi-sq(df) = X.XX, p = .XXX, Cramer's V = .XXX
Degrees of freedom: always with chi-square statistic
Sample size: final analytic N
Decimal places: 1 for proportions, 2 for OR, 3 for p and effect sizes
Non-significance: "not statistically significant at alpha = .05" --- never "no association"

Association Tests

Scenario	Expected cells all >= 5	Any expected cell < 5
2x2 or 2xK	Chi-square + Cramer's V	Fisher's exact test

Paired/matched binary designs -> vera-stat-repeated-testing (handles within-subject comparisons including paired binary data via McNemar's test).

Example Dataset

R built-in Titanic (convert to data frame): outcome = Survived (0/1), predictors = Class, Sex, Age. Python: sm.datasets.get_rdataset("Titanic").data or reconstruct from counts (offline fallback uses base R Titanic dataset).

Cross-Skill Interface

Output:
├── code_r      → .R script
├── code_python → .py script
├── figures/    → 2 PNGs (class balance + mosaic/grouped bar)
└── recommendations → text block (additional analyses available)

---name: vera-stat-binary-testingdescription: Runs class balance diagnostics and primary association tests for binary outcome variables (0/1, yes/no, survived/died, pass/fail). Produces proportion tables, class balance check with rare-event warning, descriptives by outcome level, chi-square test of independence with Cramer's V, Fisher's exact test when cell counts are small, odds ratio with 95% CI, and a mosaic or grouped bar chart. Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a binary/dichotomous outcome and says "binary outcome," "logistic," "survived or died," "yes or no," "pass or fail," "0/1," "classification," "binary DV," or names a binary variable like survived, admitted, defaulted, churned, diagnosed. Does not handle continuous, count, survival time, ordinal, repeated measures, or SEM.--- # Binary Outcome --- Class Balance Diagnostics & Association 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 CLASS BALANCE ├── Balanced (minority ≥ 10%) → standard tests └── Imbalanced (minority < 10%) → rare event warning, note power limits 2. PRIMARY ASSOCIATION TEST ├── All cells ≥ 5 → Chi-square + Cramer's V + OR └── Any cell < 5 → Fisher's exact + OR ``` ## Required Inputs | Role | What to collect | |---|---| | **Outcome (Y)** | Variable name, what 0/1 represents | | **Primary 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: Class Balance Diagnostics → plot_01_class_balance.png PART 2: Primary Association Test → plot_02_mosaic_[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: Cramer's V (chi-square), odds ratio with 95% CI 3. Odds ratios: always as "OR = X.XX, 95% CI [X.XX, X.XX]" 4. Proportions: report as percentages with 1 decimal 5. Chi-square: chi-sq(df) = X.XX, p = .XXX, Cramer's V = .XXX 6. Degrees of freedom: always with chi-square statistic 7. Sample size: final analytic N 8. Decimal places: 1 for proportions, 2 for OR, 3 for p and effect sizes 9. Non-significance: "not statistically significant at alpha = .05" --- never "no association" ## Association Tests | Scenario | Expected cells all >= 5 | Any expected cell < 5 | |---|---|---| | 2x2 or 2xK | Chi-square + Cramer's V | Fisher's exact test | Paired/matched binary designs -> `vera-stat-repeated-testing` (handles within-subject comparisons including paired binary data via McNemar's test). ## Example Dataset R built-in `Titanic` (convert to data frame): outcome = Survived (0/1), predictors = Class, Sex, Age. Python: `sm.datasets.get_rdataset("Titanic").data` or reconstruct from counts (offline fallback uses base R `Titanic` dataset). ## Cross-Skill Interface ``` Output: ├── code_r → .R script ├── code_python → .py script ├── figures/ → 2 PNGs (class balance + mosaic/grouped bar) └── recommendations → text block (additional analyses available) ```

Frequently asked questions