| name | Programmatic EDA |
| description | Systematic exploratory data analysis on a dataset — structure, nulls, outliers, distributions, correlations — and produce a shareable profiling report |
| var | |
| tags | ["data","analysis"] |
${var} — Path to the dataset to profile (CSV or Parquet — Excel is auto-converted to CSV). Relative to repo root. Required. Example: data/orders_2026q2.csv.
Read memory/MEMORY.md for context.
If ${var} is empty, notify and exit:
./notify "programmatic-eda skipped: var empty — pass dataset path e.g. var=data/foo.csv"
exit 0
If the file at ${var} does not exist, notify and exit:
[ -f "${var}" ] || { ./notify "programmatic-eda aborted: dataset not found at ${var}"; exit 0; }
Goal
Run a fixed profiling pipeline on ${var}, classify findings, and ship a Markdown report + a short findings summary. Same checks every run so reports are comparable across datasets.
Steps
1. Prepare environment
Ensure Python deps are available. The bundled scripts use only pandas and numpy (no scipy / matplotlib — histograms are ASCII, descriptive stats are pandas-native). If ${var} is a Parquet file, pyarrow is also required — pd.read_parquet will fail without an engine even though pandas itself imports fine. If an import fails, run pip install --quiet pandas numpy pyarrow and retry once.
Inputs supported by the scripts: .csv and .parquet. If ${var} ends in .xlsx / .xls, convert to CSV first (python -c "import pandas as pd; pd.read_excel('${var}').to_csv('/tmp/eda-input.csv', index=False)") and use the converted path for the rest of the run.
All bundled scripts use --input <path> (CSV or Parquet). Set RUN_OUT=out/programmatic-eda/$(basename ${var} | sed 's/\.[^.]*$//')-${today} and mkdir -p "$RUN_OUT" before step 2.
2. Load and overview
Run python skills/programmatic-eda/scripts/data_overview.py --input ${var} --sample 10 | tee "$RUN_OUT/overview.txt". Capture row count, dtypes, memory, sample. Confirm the grain (what one row represents) — if ambiguous, note it as an open question in the findings summary rather than guessing.
3. Null profile
Read warn/fail thresholds from skills/programmatic-eda/references/quality_thresholds.md (defaults: warn 5%, fail 30%). Run python skills/programmatic-eda/scripts/null_profiler.py --input ${var} --warn-pct <W> --fail-pct <F> --output "$RUN_OUT/nulls.csv" | tee "$RUN_OUT/nulls.txt". Flag every column marked FAIL (and any WARN that crosses a column-specific override in the thresholds doc).
4. Outlier detection
Run python skills/programmatic-eda/scripts/outlier_detector.py --input ${var} --method both --output "$RUN_OUT/outliers.csv" | tee "$RUN_OUT/outliers.txt" (IQR + z-score on numerics). For each flagged column, classify the spike: real signal (keep) vs data error (recommend fix). Don't drop anything — this skill profiles, it doesn't mutate.
If the dataset has a categorical regime column (e.g. mode, cohort, segment) whose levels differ by ≥10× in scale, pooled outlier detection misleads — higher-regime rows get flagged as outliers when they're just routine for their level. Re-run with --groupby <col> to stratify thresholds within each level, and treat the grouped output as the authoritative one.
5. Distribution summary
Run python skills/programmatic-eda/scripts/distribution_summary.py --input ${var} --bins 20 --output "$RUN_OUT/distributions.csv" | tee "$RUN_OUT/distributions.txt". The script emits descriptive stats and ASCII histograms — both land in the tee'd file.
If a --groupby was warranted in step 4, pass the same column here (--groupby <col>) so stats and histograms emit per group — pooled descriptives over a multi-regime column hide the structure.
6. Correlation exploration
Run python skills/programmatic-eda/scripts/correlation_explorer.py --input ${var} --threshold 0.8 --output "$RUN_OUT/correlations.csv" | tee "$RUN_OUT/correlations.txt". Flag pairs with |r| ≥ 0.8 as potential multicollinearity or redundancy. Note any surprising zero-correlations between columns that should be related.
If a --groupby was warranted in step 4, pass the same column here (--groupby <col>) — pooled correlation can be dominated by between-group level differences, so a strong pooled r may vanish or flip sign within each regime. The stacked per-group output lets you tell structural collinearity (consistent within every group) from regime-driven artifacts (only present pooled or in one group).
7. Checklist sign-off
Walk skills/programmatic-eda/references/eda_checklist.md. Confirm each item before declaring profiling complete; any unchecked item becomes a line in the findings summary.
8. Write deliverables
- Fill
skills/programmatic-eda/assets/eda_report_template.md into the run output directory as eda_report.md — full per-column profiling output.
- Fill
skills/programmatic-eda/assets/findings_summary.md into the run output directory as findings_summary.md — top 3–5 issues with severity (CRITICAL / HIGH / MEDIUM / LOW) and a recommended next step for each.
9. Notify
Send via ./notify:
*Programmatic EDA — $(basename ${var})*
Rows: <N> Cols: <M> Grain: <one row = ...>
Top issues: <issue 1> · <issue 2> · <issue 3>
Report: out/programmatic-eda/<run-dir>/eda_report.md
Keep it one paragraph (per Aeon notification rules).
10. Log
Append to memory/logs/${today}.md:
### programmatic-eda
- Dataset: ${var}
- Rows / Cols: <N> / <M>
- Top findings: <bullet list>
- Output: out/programmatic-eda/<run-dir>/
Sandbox note
This skill is fully local — it reads a file from the repo and runs Python scripts. No outbound network is required, so the WebFetch fallback pattern doesn't apply. If a script needs a package the runner doesn't have, install via pip install --quiet (pandas/numpy/scipy/matplotlib are the only deps the bundled scripts use).
If ${var} points at a remote URL instead of a local path: fetch it once via curl -L -o /tmp/eda-input.<ext> ${var} (with WebFetch fallback), then run the pipeline against the local copy.
Constraints
- Never mutate the input dataset. This skill profiles; it does not clean.
- Bundled scripts have no skip-list arg, so PII filtering happens at the reporting layer: when summarizing distribution/correlation output, exclude columns whose name matches obvious PII patterns (email, ssn, phone, address) — these are noise for EDA and a leak risk in the report.
- Don't crash on a single failing step — log it as a finding ("step X failed: "), continue with the remaining steps, and surface the failure in the notification.
- Don't downgrade thresholds to make a dataset look clean. If
quality_thresholds.md flags something, it flags something.
Provenance
Scripts, references, and asset templates are vendored from nimrodfisher/data-analytics-skills (01-data-quality-validation/programmatic-eda/). Upstream license applies. To pull upstream updates, re-copy the three subfolders; SKILL.md is Aeon-specific and should not be overwritten.
