csv-analyse

# 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 `fs_list` and `fs_read` 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`. 2. **Run the bundled analyser.** Call: ``` run_skill_script({ 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. 3. **Parse the JSON.** The stdout is a single JSON object with keys: `shape`, `columns`, `dtypes`, `missing`, `numeric_summary`, `categorical_summary`, `head`. Show the user the bits they asked for — don't dump the whole thing unless they asked for "everything". 4. **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.