Q eda

Q-EDA

Universal exploratory data analysis for tabular datasets. Interviews the user to confirm column measurement levels, runs statistically appropriate analysis per variable type, and produces structured CSVs with a narrative summary.

IMPORTANT: This skill requires Bash execution. Use the pre-built scripts/run_eda.py from ${SKILL_DIR}/scripts/ — do NOT write a new script or inline Python.

If in plan mode: write a brief plan — "Run q-eda skill: interview user for context and column types, execute run_eda.py, write EXPLORATORY_SUMMARY.md from generated CSVs." — then exit plan mode immediately. Do NOT attempt interview stages, script execution, or any analysis while plan mode is active.

Script Directory

Agent execution instructions:

1. Determine this SKILL.md file's directory path as SKILL_DIR.
2. Script path = ${SKILL_DIR}/scripts/run_eda.py.
3. Reference path = ${SKILL_DIR}/references/<ref-name>.

Dependencies

pandas
numpy
scipy
openpyxl   # required for .xlsx input and Phase 6 Excel report

Install: pip install pandas numpy scipy openpyxl

References

• references/interview_protocol.md — two-stage interview, column classification table, and detection rules
• references/invocation_guide.md — script arguments, examples, and behavioral defaults
• references/summary_instructions.md — post-script summary instructions, table formatting rules, flagging thresholds
• references/summary_template.md — structural blueprint for EXPLORATORY_SUMMARY.md
• ../references/apa_style_guide.md — APA formatting, numbers, notation

Core Principles

• Exploratory-first: no confirmatory statistics; build the picture before hypothesis testing
• User-confirmed classification: suggest measurement levels; user confirms before analysis runs
• Whole-artifact re-presentation on revision: when the user requests corrections, re-present the complete updated classification table alongside the confirmed Stage A answers as a single consolidated proof, and obtain explicit final approval before invoking run_eda.py
• Measurement-appropriate methods: median/IQR for ordinal, Pearson for continuous, Spearman for ordinal, cross-tabs for nominal
• Insight-flagging: report patterns and warnings, not just numbers
• Dual output: CSVs for validation and import; markdown for interpretation
• APA-compatible statistics: full metric set (M, Mdn, SD, SE, 95% CI, skewness, kurtosis) ready for reporting

Workflow

Step	Action	Reference
1	Interview: context questions, then column classification with user confirmation	references/interview_protocol.md
2	Execute: run run_eda.py with confirmed types (see Pipeline below)	references/invocation_guide.md
3	Summarize: write tables-eda/EXPLORATORY_SUMMARY.md from generated CSVs	references/summary_template.md, references/summary_instructions.md

Pipeline (Step 2 output)

Phase	Output	Content
0	(console)	Data loading, column classification, schema summary
1	01_dataset_profile.csv	Shape, column types, missing%, uniqueness
2	02_data_quality.csv	Missing counts/%, duplicates, constant columns, outliers (IQR)
3	03-08_*.csv	Univariate: nominal frequencies, binary summary, ordinal/discrete/continuous descriptives
4	09-12_*.csv	Bivariate: Pearson/Spearman correlations, grouped descriptives, cross-tabs
5	13-14_*.csv	Specialized: text analysis, temporal trends
6	EXPLORATORY_REPORT.xlsx	APA-7th formatted workbook (B&W, one sheet per CSV)

Files are omitted when no columns of that type exist. Output directory: tables-eda/.

Scope

Include: Any .xlsx/.csv dataset — academic, business, or general. Outputs feed directly into q-methods and q-results.

Exclude: Confirmatory statistics, visualization, hypothesis testing, data cleaning beyond script internals.

Checklist

• Column classification table presented and confirmed by user
• If corrections were requested, full updated classification table re-presented alongside Stage A context and explicitly approved before script invocation
• Confirmed types passed via --col_types; grouping columns via --group
• Each detected column type has at least one output file
• EXPLORATORY_SUMMARY.md follows references/summary_template.md structure
• Descriptive tables use core + detail split-table format
• Every content section cites its source CSV in the heading
• Numbers in the summary match the source CSVs exactly
• No ad-hoc Python used to derive findings outside the script pipeline