| name | csv-summary |
| description | Use when the user hands over a CSV or spreadsheet export and wants to know what is in it — column types, missing values, ranges, and the most common values. |
| tags | ["data"] |
Summarize a CSV
Profiles a CSV so you can describe it before analysing it: per column, how many
rows are filled, whether the values are numeric or text, and either min/max/mean
or the most frequent values.
Standard library only — nothing to install.
Steps
-
Confirm the file path with the user if you are not certain of it.
-
Run the companion script:
python /workspace/skills/csv-summary/scripts/summarize.py <path-to-csv>
Useful flags: --delimiter ';' for European exports, --json when you want to
post-process the result, --max-rows N to profile only the first N rows of a
very large file.
-
Report the findings in prose, not as a dump of the script output. Lead with
what the file is (one row per what?), then call out anything that will matter
for the next step: columns with missing values, a column that looks numeric but
is stored as text, a categorical column with only a handful of distinct values.
-
If the script reports a column as text that the user expects to be numeric,
say so explicitly — it usually means stray units, thousands separators, or a
placeholder like N/A.
Notes
- The script never modifies the input file.
- A column counts as numeric only when every non-empty value parses as a number,
so a single
N/A flips a column to text. That is deliberate: it surfaces dirty
data instead of silently ignoring it.
