vibe-example-local

Super Analyze

A Claude Code plugin for statistical analysis of experimental research data. It auto-detects questionnaire types and experimental designs, asks for your confirmation at key decision points, and generates a traceable analysis script.

Built on top of rcode, a statistical helper library in the same repository (see README-rcode.md).

Quick Start

# 1. Install dependencies
python -m venv myenv
myenv\Scripts\activate
pip install -r requirements.txt
pip install -e .

# 2. Register as a local plugin in Claude Code
/plugin marketplace add C:/Users/adminroot/Documents/GitHub/vibe_example
/plugin install super-analysis@vibe-example-local

# 3. Run
/super-analysis:run text_dataset/ipq.csv
# or shorthand
/super-analysis text_dataset/ipq.csv

Workflow

Data file ──▶ Detect ──▶ Confirm ──▶ Preprocess ──▶ Assumptions ──▶ Confirm ──▶ Analyze ──▶ Output
                │          ▲                            │              ▲
                │          │                            │              │
            automatic   you decide                 auto-recommend   you decide

Phase 1 — Detection (automatic) Scan the file and detect questionnaire type (IPQ / SSQ / SUS / NASA-TLX / generic), independent variables, dependent variables, subject ID, and experimental design (within/between-subjects, single/multi-factor).

Confirmation 1 — You confirm or correct the detection results

Phase 2 — Preprocessing (automatic) Score questionnaires via rcode functions when available, clean data, and export cleaned_scored.csv.

Phase 3 — Descriptive Statistics & Assumption Checks (automatic) Summarize by condition; run Shapiro-Wilk, Mauchly, Levene, etc. and recommend a statistical method per dependent variable.

Confirmation 2 — You confirm or override the recommended methods

Phase 4 — Main Analysis (automatic)

Design	Parametric	Non-parametric
2 conditions, within	Paired t-test	Wilcoxon
2 conditions, between	Independent t-test	Mann-Whitney U
≥3 conditions, within	Repeated-measures ANOVA	Friedman
≥3 conditions, between	One-way ANOVA	Kruskal-Wallis
Multi-factor	Two-way ANOVA	ART

Phase 5 — Output

File	Description
analyze_xxx.py	Traceable analysis script (the core artifact — each block is labeled with its source)
cleaned_scored.csv	Cleaned and scored data
summary.txt	Results summary
figures/*.png	Figures

Every code block in the generated script is labeled as either rcode-backed or local fallback (using pingouin / scipy / statsmodels).

Supported Questionnaires

• IPQ / SSQ / SUS — reuses scoring functions from rcode
• NASA-TLX — local fallback scoring (rcode does not yet expose a dedicated wrapper)
• Generic data — skips questionnaire scoring and goes straight to analysis

The rcode Library

Super Analyze is the orchestration layer; rcode is the statistical library underneath it, handling questionnaire scoring, assumption checks, reporting, and plotting.

For the full list of available functions, see README-rcode.md.