super-analysis

Super Analyze

Super Analyze is a human-in-the-loop assistant for statistical analysis of experimental datasets.

It turns raw data into a reproducible analysis workflow with automatic detection, explicit review gates, and traceable output artifacts.

English | 中文

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Table of contents

• What is Super Analyze?
• Feature cards
• Install
• How to use
• Workflow
• Method mapping
• Outputs
• Supported questionnaires
• About rcode
• Contributing
• License

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What is Super Analyze?

Super Analyze helps research teams move from raw study files to reproducible analysis with better control.

• Detect questionnaire type and design structure from a dataset.
• Keep humans in the loop with required confirmation checkpoints.
• Recommend methods with rationale and alternatives.
• Generate rerunnable scripts and a clean artifact set.

The core value is simple: fewer manual steps, clear decision traces, and lower reproducibility risk.

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Feature cards

Feature	What you get
Smart intake	Detects IPQ, SSQ, SUS, NASA-TLX, or generic experimental datasets, plus subject/condition/DV columns.
Two confirmation gates	Mandatory user confirmation on detection and method choice for every analysis path.
Method suggestions	Recommends parametric and non-parametric alternatives per dependent variable.
Traceable script generation	Produces a readable analyze_<dataset>.py with source labels (rcode vs fallback).
Claude-first command flow	Integrated slash-command flow for conversational execution.
One command output pack	Exports cleaned data, summary, and figure files together with the script.

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Install

python -m venv myenv
myenv\Scripts\activate
pip install -r requirements.txt
pip install -e .

Register plugin in Claude Code

/plugin marketplace add <YOUR_REPO_PATH>
/plugin install super-analysis@vibe-example-local

Example:

/plugin marketplace add C:/Users/adminroot/Documents/GitHub/vibe_example
/plugin install super-analysis@vibe-example-local

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How to use

Recommended (Claude command)

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

Direct CLI usage

python .\scripts\super_analyze.py scan path/to/dataset.csv
python .\scripts\super_analyze.py recommend path/to/dataset.csv

Use the virtual environment interpreter when available:

.\myenv\Scripts\python.exe .\scripts\super_analyze.py scan path/to/dataset.csv

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Workflow

Data file → Detect → Confirm → Preprocess → Assumption checks → Confirm → Generate analysis → Export outputs

Phase 1 — Detection (automatic)

• Detect questionnaire type and detected fields.
• Infer design pattern (within/between, single-factor or multi-factor).
• Produce an initial structured report.

Phase 2 — Confirmation 1

• User confirms or corrects the detection report before moving forward.

Phase 3 — Preprocessing

• Apply questionnaire scoring and data cleanup when supported.
• Keep intermediate files deterministic and auditable.

Phase 4 — Assumption checks + recommendation (automatic)

• Compute condition-wise summaries and assumption checks.
• Suggest an analysis method and a fallback for each dependent variable.

Phase 5 — Confirmation 2

• User accepts or replaces the suggested method per dependent variable.

Phase 6 — Script and artifacts

• Generate analyze_<dataset>.py and result files after confirmations pass.

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Method mapping

Design	Parametric option	Non-parametric option
2 conditions, within-subject	Paired t-test	Wilcoxon signed-rank
2 conditions, between-subject	Independent t-test	Mann–Whitney U
>2 conditions, within-subject	Repeated-measures ANOVA	Friedman
>2 conditions, between-subject	One-way ANOVA	Kruskal-Wallis
Multi-factor	Two-way ANOVA (or equivalent)	ART or non-parametric alternative

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Outputs

File	Purpose
analyze_<dataset>.py	Traceable analysis script, ready to rerun
<dataset_stem>_cleaned_scored.csv	Cleaned/scored data table
<dataset_stem>_analysis_summary.txt	Condensed run summary
figures/*.png	Auto-generated figures

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Supported questionnaires