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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.

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aibot88/sec_skill_store
Last source activity
May 27, 2026 at 03:47
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name
vera-stat-binary-analyzing
description
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.
user-invocable
true
allowed-tools
Read, Bash, Write, Edit, Grep, Glob
# 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`.
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