Exekiel179
UserSkill plugin for Claude Code, Codex & other AI agents: small, safety-first Pingouin workflows for psychology statistics — assumption checks, S0–S5 supervision gates, reproducible Python, and APA-style reporting.
Categories
Indexed Skills (16)
pg-analysis-approval
Review and approve Pingouin psychology analyses before final reporting. Use after data screening or statistical output and before interpretation to catch common problems: wrong test, ignored repeated measures, missing assumption checks, multiple-comparison errors, unsupported causal claims, incomplete reporting, or output-format mismatches.
pg-anova
Run or generate Pingouin code for one-way, factorial, repeated-measures, mixed, Welch ANOVA, ANCOVA, and follow-up pairwise tests.
pg-bayesian
Compute or generate Pingouin Bayes factors for t tests, correlations, and proportions (bayesfactor_ttest, bayesfactor_pearson, bayesfactor_binom), and read the BF10 already returned by pg.ttest and pg.corr.
pg-categorical
Run or generate Pingouin code for categorical / contingency-table analyses — chi-square test of independence, McNemar's paired test, 2x2 crosstabs, and chi-square power — for psychology data with nominal variables.
pg-correlations
Run or generate Pingouin code for Pearson, Spearman, Kendall, partial, repeated-measures, distance, and pairwise correlations with APA-style reporting.
pg-data-screening
Screen psychology datasets before Pingouin analysis: variable types, missingness, duplicates, long/wide shape, assumption checks, and safer preprocessing.
pg-mean-tests
Run or generate Pingouin code for one-sample, independent, paired, Welch, and corrected pairwise mean comparisons in psychology studies.
pg-multivariate
Run or generate Pingouin code for multivariate comparisons — Hotelling's T-squared test (multivariate_ttest) — plus the multivariate assumption checks box_m (equal covariance) and multivariate_normality, for designs with several dependent variables.
pg-nonparametric
Run or generate Pingouin code for rank-based non-parametric tests — Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, Friedman, and Cochran Q — when outcomes are ordinal or parametric assumptions fail.
pg-regression-mediation
Run or generate Pingouin code for linear regression, binary logistic regression, and mediation analysis with reproducible reporting.
pg-reporting
Convert Pingouin results into compact, polished APA-style or Chinese psychology result sections with tables, assumption notes, and uncertainty caveats.
pingouin-stat-router
Route psychology statistics requests to the smallest Pingouin workflow. Use when the user asks what analysis to run, gives a psychology design, asks to analyze data with Pingouin, or needs lower-token guidance before code generation.
pingouin-stat
Main Pingouin statistics workflow for psychology research. Use when the user says pingouin-stat or asks to analyze psychology data with Pingouin end-to-end: first clarify research question, variables, assumptions, output format such as Word/PDF/LaTeX, table/figure requirements, then route to analysis skills, run approval checks, and organize final results.
pg-power
Plan psychology study sample sizes or compute achieved power with Pingouin power functions for t tests, ANOVA, repeated-measures ANOVA, and correlations.
pg-reliability
Run or generate Pingouin code for Cronbach alpha, item-scale reliability checks, and intraclass correlation for psychological ratings.
pg-workflow
Run and resume the stateful Pingouin psychology statistics workflow. Use when the user wants an end-to-end, auditable analysis from intake through screening, analysis, approval, reporting, and archive, or asks to continue an existing analysis run.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.