By phdemotions
Gold-standard Claude Code skills for reproducible quantitative research in R and Python. Built for business, marketing, and consumer behavior researchers.
Confirmatory hypothesis testing matched to pre-registration, with full assumption testing, effect sizes, confidence intervals, and APA 7th formatted output. Supports OLS/GLM regression, panel regression (fixest), mixed models (lme4), SEM/CFA (lavaan), meta-analysis (metafor), and delegates PROCESS models to /process-model. Reads pre-registration to align planned analyses, flags deviations, and generates decision log entries for post-hoc choices. Use when the user says "test hypotheses," "run analysis," "confirmatory," "regression," "SEM," "mediation," "mixed model," "meta-analysis," or when /eda completes. Triggers on "analyze," "hypothesis," "regression," "model," "test."
Produce documented data cleaning scripts that log every transformation with N before/after each step, generate a CONSORT-style exclusion flow diagram, create decision log entries for every subjective choice, compute scale reliability and composites, and write cleaned data to data/processed/. Never modifies raw data. Use when the user says "clean data," "prepare data," "apply exclusion criteria," "handle missing data," "create composites," "data preprocessing," or when /data-validate found issues to address. Triggers on "clean," "exclusion," "missing data," "preprocessing," "composites," "reverse code."
Generate a complete, manuscript-ready data profile: demographics summary table, scale identification with reliability (alpha, omega, CFA), comprehensive codebook, sample characteristics, and measurement documentation — all following current best practices at the time of execution. Searches for the latest reporting standards (JARS, TOP, APA) before generating output. Produces everything a Methods section needs to describe the data. Use when the user says "describe my data for the manuscript," "demographics table," "what scales are in here," "codebook," "Methods section data," "sample characteristics," or any time they need data documented for publication. Triggers on "profile," "demographics," "scales," "codebook," "sample description," "Methods section."
Run declarative data quality checks and generate a codebook. Checks completeness, distributions, impossible values, duplicates, outliers, encoding issues, attention check failures, and manipulation check results. Produces a pointblank/pandera validation report and an auto-generated codebook. Use when the user says "validate data," "check data quality," "generate codebook," "what's wrong with my data," "data audit," "check my dataset," or when /research-intake identifies missing validation. Triggers on "validate," "data quality," "codebook," "check my data."
Comprehensive exploratory data analysis with publication-quality descriptive tables, correlation matrices, distribution plots, and assumption testing. Generates a standalone EDA report with Table 1 (gtsummary/great_tables), correlation heatmap, distribution diagnostics, VIF for multicollinearity, and normality/homoscedasticity tests. All figures are APA-formatted and colorblind-safe. Use when the user says "exploratory analysis," "EDA," "descriptive statistics," "explore the data," "Table 1," "correlations," "distributions," or when /data-clean completes successfully. Triggers on "EDA," "descriptive," "Table 1," "explore," "correlations."
Modifies files
Hook triggers on file write and edit operations
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Gold-standard Claude Code skills for reproducible quantitative research in R and Python.
Built for business, marketing, consumer behavior, management, and organizational behavior researchers. Designed so a senior faculty member at a top business school would be proud to put their name on the code.
Plugin (recommended):
/plugin marketplace add phdemotions/research-methods
/plugin install research-methods@phdemotions-research-methods
Skills are available as /research-methods:skill-name in any project. Hooks activate automatically. Auto-updates on version bump.
Quick start (no plugin system):
git clone https://github.com/phdemotions/research-methods ~/tools/research-methods
claude --add-dir ~/tools/research-methods
Skills load automatically. Hooks do not load via --add-dir — use the plugin method for full functionality.
Direct use (contributors):
git clone https://github.com/phdemotions/research-methods
cd research-methods
claude
Everything loads natively when working inside the repo.
| Skill | What it does |
|---|---|
/research-intake | Start here. Reviews your materials, identifies gaps, suggests what to do first |
/research-init | Scaffolds a project with full reproducibility infrastructure (targets/Snakemake, renv/uv, decision log, pre-registration) |
/data-validate | Checks data quality — completeness, ranges, duplicates, attention checks — and generates a codebook |
/data-clean | Produces cleaning scripts that log every transformation with CONSORT-style exclusion flow |
/data-profile | Manuscript-ready data documentation: demographics table, scale reliability (alpha, omega, CFA), comprehensive codebook |
/eda | Table 1, correlation matrix, distributions, assumption pre-checks — all publication-quality |
/analyze | Confirmatory analysis matched to your pre-registration. Supports OLS/GLM, panel FE (fixest), mixed models (lme4), SEM (lavaan), meta-analysis (metafor) |
/process-model | Hayes PROCESS models as transparent lavaan code. Bootstrap CIs, index of moderated mediation, Johnson-Neyman plots |
/visualize | APA 7th figures: interaction plots, path diagrams, forest plots, marginal effects, J-N plots. Colorblind-safe, multi-format export |
| Skill | What it does |
|---|---|
/report | Manuscript-ready APA results paragraphs and tables |
/robustness | Specification curves, alternative estimators, sensitivity analysis |
/reproduce | OSF/repository packaging with FAIR compliance |
/research-review | Methods code review from a senior methodologist's perspective |
/pre-submit | JARS compliance, journal-specific pre-submission checklist |
/research-zeitgeist | Date-aware scan of current best practices — the self-improvement engine |
/method-advisor | "What test should I use?" with citations, assumptions, and code skeletons |
data/raw/. Raw data is sacred.data/processed/.tar_make() (R) or snakemake (Python).R: targets + renv + tidyverse + pointblank + ggplot2 + gtsummary + modelsummary + easystats + fixest + lavaan + bruceR + metafor + Quarto
Python: Snakemake + uv + polars + pandera + plotnine + great_tables + statsmodels + pingouin + Quarto
Every framework choice was verified against current best practices. See docs/FRAMEWORKS.md for the full rationale.
Skills like /data-profile and /research-zeitgeist check current reporting standards (JARS, TOP, journal guidelines) via web search before generating output. Recommendations stay current regardless of when you run them.
Built with Positron. Compatible with VS Code, JetBrains, or any IDE supporting Claude Code.
/research-intake → Review what you have, identify gaps
/research-init → Scaffold project structure
/data-validate → Check data quality, generate codebook
/data-clean → Clean with documented exclusions
/data-profile → Demographics, scales, reliability for Methods section
/eda → Descriptive stats, correlations, assumptions
/analyze → Hypothesis testing matched to pre-registration
/process-model → Mediation/moderation if applicable
/visualize → Publication figures
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