Cross Tab Analysis Across Themes

I have a survey dataset with open-ended text responses and demographic
or segment/attribute variables. I want to run a themed cross-tab
analysis on [OPEN-ENDED QUESTION COLUMN].

What this prompt does:
- Reads open-ended responses and sorts them into a manageable set of
  recurring themes
- Cross-tabs those themes against the segments you specify, and checks
  whether any segment has enough responses to draw a real conclusion
  from

What it returns:
- A list of themes with example quotes, so you can check the coding
  before trusting it
- A table of theme counts and percentages, 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. Read all non-blank responses in [OPEN-ENDED QUESTION COLUMN] and
   identify the 5-8 recurring themes that best capture the range of
   answers. This is a column header from your data file, exactly as it
   appears in the header row, like `biggest_barrier` or
   `open_feedback`, not a spreadsheet cell reference or column letter.
   Use an "Other/unclear" bucket rather than forcing every response
   into a theme.
2. For each theme, give me 2-3 example quotes or paraphrases so I can
   sanity-check the coding, plus the exact count and percentage of
   responses in that theme, excluding blanks.
3. Assign each response to its single best-fitting theme as a new
   column. Use code, not estimation, to do the counting.
4. Cross-tab the theme variable against these segment variables:
   [LIST YOUR SEGMENT VARIABLES, e.g., plan tier, company size, usage
   frequency]. These are also column headers from your data file, not
   spreadsheet cell references. Show counts and row percentages for
   each.
5. 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.
6. 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.
7. 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.
8. Save the table itself as an image file I can download, formatted so
   it's easy to read at a glance.