Cross Tab Analysis Across Segments

I have a survey dataset with a categorical or rating-scale outcome
question and one or more segment/attribute variables (for example,
plan tier, company size, department, or customer segment). I want to
run a cross-tab analysis.

What this prompt does:
- Compares [OUTCOME VARIABLE] across the segments you specify
- Checks whether any segment has enough respondents to draw a real
  conclusion from

What it returns:
- A table of counts and percentages (or means, for rating-scale
  questions), broken out by segment
- A list of any segment comparisons that don't have enough data to trust
- A plain-language summary of the differences worth paying attention to
- A saved image of the table you can download and reuse

Please:
1. Cross-tab [OUTCOME VARIABLE] against [SEGMENT VARIABLE(S)]. These are
   column headers from your data file, exactly as they appear in the
   header row, like `plan_tier` or `nps_score`. They are not spreadsheet
   cell references or column letters, like "C" or "G2". For a
   categorical outcome, show counts and row percentages. For a
   rating-scale or numeric outcome, show the mean and the full
   distribution across scale points.
2. Use code to calculate this, not estimation. Exclude any missing,
   blank, or non-response values from the calculations rather than
   treating them as zero.
3. Flag any cross-tab cell with fewer than [N, e.g., 10] respondents. A
   cell with ZERO respondents also counts as under the threshold, not
   just small nonzero counts. Report the total flagged as a single
   number computed directly from the full table, not calculated in
   parts and combined.
4. Before giving me the final answer, recompute every number you're
   about to report directly from the underlying table one more time,
   using a genuinely different method than you used the first time,
   for example a manual count instead of a formula-based one. Simply
   re-running the same calculation again doesn't count as a check.
   Correct anything that doesn't match rather than keeping your
   first-draft number.
5. Summarize 2-3 patterns, and be explicit about which differences look
   meaningful versus likely noise given sample size. Don't overstate
   findings from a small dataset.
6. Save the table itself as an image file I can download, formatted so
   it's easy to read at a glance.