How do I install the Vera Stat Binary Analyzing skill?

Run npx skillmds@latest add aibot88/vera-stat-binary-analyzing 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 Analyzing 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 Binary Analyzing?

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 Analyzing 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 Analyzing?

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

Vera Stat Binary Analyzing by aibot88

Name: Vera Stat Binary Analyzing
Rating: 4.2 (5 reviews)
Author: aibot88

Vera Stat Binary Analyzing

Server-side extension that completes the full analysis pipeline for binary outcome variables after vera-stat-binary-testing has run. Adds remaining association tests (chi-square/Fisher's for additional predictors, point-biserial for continuous predictors), stratified odds ratio analysis with Breslow-Day test and forest plot, full modeling (logistic regression with OR and 95% CI, Hosmer-Lemeshow GOF, pseudo-R2, ROC curve with AUC, classification table, tree-based classification with CART/RF/GBM), and cross-method variable importance comparison on a 0-100 unified scale. 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-stat-binary-testing completes, or direct request with a binary outcome variable.

aibot88 Updated Sep 9, 2026 3 repo stars

File contents

Binary 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-stat-binary-testing stopped (PART 0-2 done).

Step	File	Executor	Output
Additional tests	`workflow/04-run-additional-tests.md`	Main Agent	PART 3 code + prose
Subgroup	`workflow/05-analyze-subgroups.md`	Main Agent	PART 4 code + prose
Modeling	`workflow/06-fit-models.md`	Main Agent	PART 5 code + prose
Comparison	`workflow/07-compare-models.md`	Main Agent	PART 6 code + prose
Manuscript	`workflow/08-generate-manuscript.md`	Main Agent	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-stat-binary-testing, plus:

Logistic coefficients: report OR (not raw B) in results text; raw B with SE in supplementary table
Pseudo-R2: report McFadden and Nagelkerke; say "the model accounted for" --- never "explained"
AUC: report with 95% CI; note "in-sample" if no cross-validation performed
Classification: report sensitivity, specificity, and threshold used
Tree-based with small N: frame as "exploratory"; never claim predictive validity
Hosmer-Lemeshow: report chi-sq, df, p; non-significant = adequate fit

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-stat-binary-testing or orchestrated by vera-stat-application-pipeline.

---name: vera-stat-binary-analyzingdescription: Server-side extension that completes the full analysis pipeline for binary outcome variables after vera-stat-binary-testing has run. Adds remaining association tests (chi-square/Fisher's for additional predictors, point-biserial for continuous predictors), stratified odds ratio analysis with Breslow-Day test and forest plot, full modeling (logistic regression with OR and 95% CI, Hosmer-Lemeshow GOF, pseudo-R2, ROC curve with AUC, classification table, tree-based classification with CART/RF/GBM), and cross-method variable importance comparison on a 0-100 unified scale. 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-stat-binary-testing completes, or direct request with a binary outcome variable.--- # Binary 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-stat-binary-testing stopped (PART 0-2 done). | Step | File | Executor | Output | |---|---|---|---| | Additional tests | `workflow/04-run-additional-tests.md` | Main Agent | PART 3 code + prose | | Subgroup | `workflow/05-analyze-subgroups.md` | Main Agent | PART 4 code + prose | | Modeling | `workflow/06-fit-models.md` | Main Agent | PART 5 code + prose | | Comparison | `workflow/07-compare-models.md` | Main Agent | PART 6 code + prose | | Manuscript | `workflow/08-generate-manuscript.md` | Main Agent | 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-stat-binary-testing, plus: - Logistic coefficients: report OR (not raw B) in results text; raw B with SE in supplementary table - Pseudo-R2: report McFadden and Nagelkerke; say "the model accounted for" --- never "explained" - AUC: report with 95% CI; note "in-sample" if no cross-validation performed - Classification: report sensitivity, specificity, and threshold used - Tree-based with small N: frame as "exploratory"; never claim predictive validity - Hosmer-Lemeshow: report chi-sq, df, p; non-significant = adequate fit ## 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-stat-binary-testing` or orchestrated by `vera-stat-application-pipeline`.

Frequently asked questions