Agent-Skills.md

Agent Skills: Q-EDA

Run exploratory data analysis on tabular datasets with measurement-appropriate statistics. Use for EDA, descriptive statistics, data exploration, or preparing data summaries for reports and manuscripts.

UncategorizedID: tyrealq/q-skills/q-eda

Repository

tyrealq/q-skills
TyrealQLicense: MIT
20

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pnpm dlx add-skill https://github.com/TyrealQ/q-skills/tree/HEAD/skills/q-scholar/q-eda

Skill Files

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skills/q-scholar/q-eda/SKILL.md

Skill Metadata

Name
q-eda
Description
Run exploratory data analysis on tabular datasets with measurement-appropriate statistics. Use for EDA, descriptive statistics, data exploration, or preparing data summaries for reports and manuscripts.

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
  • 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
  • [ ] 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
Q-EDA Skill | Agent Skills