id	xlsx
name	xlsx
description	Read, summarize, calculate from, and analyze Excel/CSV/TSV spreadsheet attachments saved by Kivio Chat.
recommended-tools	["read","run_python"]

XLSX Skill

Use this skill when the user attaches or references a spreadsheet (.xls, .xlsx, .xlsm, .csv, or .tsv) and asks to inspect, summarize, calculate, clean, compare, chart, or answer questions from it.

Inputs

Kivio stores each uploaded spreadsheet as a safe local copy and includes its absolute path in the user message under Kivio 安全副本路径. Pass that safe copy path to run_python via files; Kivio mounts it inside the Pyodide filesystem for the run. Python code must use the mounted virtual paths in KIVIO_INPUT_FILES, not the host absolute path directly.

You can also process any local spreadsheet, not just uploaded safe copies: pass its absolute path (e.g. one discovered via glob/list_dir) directly to run_python via files.

Workflow

Identify the safe copy path from the attachment note.
For .csv / .tsv, use read for a small text preview or run_python with files=["Kivio 安全副本路径"] and pandas for analysis.
For .xlsx / .xlsm, use run_python with files=["Kivio 安全副本路径"] and pandas.read_excel when available.
For legacy .xls, try pandas.read_excel; if the engine is unavailable, explain the limitation and ask for .xlsx or .csv.
Inspect sheet names, columns, row counts, missing values, and representative rows before answering.
Do not invent numbers. Run calculations explicitly.

Python Patterns

Preview workbook:

from pathlib import Path
import pandas as pd

path = Path(KIVIO_INPUT_FILES[0])
book = pd.ExcelFile(path)
print(book.sheet_names)
for sheet in book.sheet_names:
    df = pd.read_excel(path, sheet_name=sheet)
    print("\\nSHEET:", sheet)
    print("shape:", df.shape)
    print(df.head().to_string())

Preview CSV/TSV:

from pathlib import Path
import pandas as pd

path = Path(KIVIO_INPUT_FILES[0])
sep = "\\t" if path.suffix.lower() == ".tsv" else ","
df = pd.read_csv(path, sep=sep)
print(df.shape)
print(df.head().to_string())

Output

For analysis: include calculation assumptions and key columns used.
For summaries: mention sheet names and row/column counts.
For charts: save generated images to relative filenames such as chart.png so Kivio can capture the artifact.

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XLSX Skill

Inputs

You can also process any local spreadsheet, not just uploaded safe copies: pass its absolute path (e.g. one discovered via glob/list_dir) directly to run_python via files.

Workflow

Identify the safe copy path from the attachment note.

For .csv / .tsv, use read for a small text preview or run_python with files=["Kivio 安全副本路径"] and pandas for analysis.

For .xlsx / .xlsm, use run_python with files=["Kivio 安全副本路径"] and pandas.read_excel when available.

For legacy .xls, try pandas.read_excel; if the engine is unavailable, explain the limitation and ask for .xlsx or .csv.

Inspect sheet names, columns, row counts, missing values, and representative rows before answering.

Do not invent numbers. Run calculations explicitly.

Python Patterns

Preview workbook:

from pathlib import Path import pandas as pd path = Path(KIVIO_INPUT_FILES[0]) book = pd.ExcelFile(path) print(book.sheet_names) for sheet in book.sheet_names: df = pd.read_excel(path, sheet_name=sheet) print("\\nSHEET:", sheet) print("shape:", df.shape) print(df.head().to_string())

Preview CSV/TSV:

from pathlib import Path import pandas as pd path = Path(KIVIO_INPUT_FILES[0]) sep = "\\t" if path.suffix.lower() == ".tsv" else "," df = pd.read_csv(path, sep=sep) print(df.shape) print(df.head().to_string())

Output

For analysis: include calculation assumptions and key columns used.

For summaries: mention sheet names and row/column counts.

For charts: save generated images to relative filenames such as chart.png so Kivio can capture the artifact.