CSV analyse

When to use

The user has a CSV file (uploaded, written to the filesystem, or inline) and wants a structured summary or statistical analysis.

Examples:

• "Summarise sales.csv"
• "What columns are in this file and what do they contain?"
• "Give me descriptive stats for the numeric columns"
• "Count missing values per column"

Steps

1. Locate the CSV. If the user mentioned a filename, call fslist and fsread under uploads/ or their chosen folder to get the content as a string. If the content is in a previous tool result or an attachment, pass it directly as stdin.

1. Run the bundled analyser. Call:

`` runskillscript({ name: "csv-analyse", path: "scripts/analyse.py", stdin: <the CSV content as a string> }) `` The script reads stdin, uses pandas to profile the data, and prints a JSON report to stdout.

1. Parse the JSON. The stdout is a single JSON object with keys: shape, columns, dtypes, missing, numericsummary, categoricalsummary, head. Show the user the bits they asked for — don't dump the whole thing unless they asked for "everything".

1. Offer follow-ups. Use offer_choices with 3-5 relevant next steps, e.g.:

- "Plot the distribution of [numeric column]" - "Filter rows where [condition]" - "Export the summary to a Word document" - "Correlate these columns"

Style

• Present the shape (rows × cols) first — it's the most useful single fact.
• Show missing-value percentages only where > 0.
• Round numeric summaries to 3 significant figures.
• If the file is big (>10k rows), mention that analysis used the full file, not a sample.

What not to do

• Don't guess column meanings. If a column name is ambiguous, ask the user.
• Don't dump the full CSV back in the chat. The user already has it.
• Don't run the analysis if the input looks like it's not a CSV (check for delimiters in the first line). Ask for clarification instead.