xlsx

name

xlsx

description

Excel spreadsheet (.xlsx) creation, editing, reading, and manipulation skill. Use when the user wants to create, read, edit, or manipulate Excel spreadsheets (.xlsx, .xlsm, .csv, .tsv files). Triggers include: any mention of 'spreadsheet', 'Excel', '.xlsx', or requests for tabular data deliverables. Also for adding columns, computing formulas, formatting, charting, cleaning messy data, or converting between tabular file formats. When an S3 URI with .xlsx extension is provided. Do NOT use for PDFs, Word documents, or Google Sheets.

XLSX creation, editing, and analysis

Execution Rules

• ALL code execution MUST use the code_interpreter tool. Do NOT use the shell tool.
• NEVER call !pip install. openpyxl, pandas, boto3, numpy, Pillow, matplotlib, lxml are pre-installed in the AgentCore Code Interpreter sandbox. Import directly. If an import fails, stop and report the error to the user — do not attempt to install anything.
• Generate the COMPLETE spreadsheet and upload to S3 in a SINGLE code_interpreter call. Do NOT split into multiple calls.
• Before calling code_interpreter, call artifact_path(filename="spreadsheet.xlsx") to get the S3 bucket and key.
• After completion, report the artifact_ref to the user.
• If code_interpreter fails with an error, do NOT retry automatically. Report the error to the user and ask for clarification or guidance. Do not make multiple retry attempts without user input.

Workflow

1. Call artifact_path(filename="spreadsheet.xlsx") — returns { s3_uri, bucket, key, artifact_ref }
2. Copy the actual s3_uri string value from the artifact_path result and hardcode it as a string literal in your code_interpreter script. Do NOT use variable references — the code_interpreter runs in an isolated sandbox and cannot access the agent's tool results.
3. Call code_interpreter ONCE with a single script that does everything: create the spreadsheet, save it, and upload to S3.

from openpyxl import Workbook
import boto3

# IMPORTANT: Replace with the ACTUAL s3_uri value returned by artifact_path
S3_URI = "s3://my-bucket/user123/proj456/artifacts/art_abc123/spreadsheet.xlsx"  # ← paste the actual s3_uri here

# Parse S3 URI into bucket and key
BUCKET, KEY = S3_URI.replace("s3://", "").split("/", 1)

# Build entire spreadsheet
wb = Workbook()
# ... all spreadsheet content ...
wb.save('./output.xlsx')

# Upload to S3
s3 = boto3.client('s3')
with open('./output.xlsx', 'rb') as f:
    s3.upload_fileobj(
        f, BUCKET, KEY,
        ExtraArgs={'ContentType': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'}
    )

4. Report the artifact_ref to the user

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Quick Reference

Task	Approach
Read/analyze content	Download from S3 → pandas or openpyxl in code_interpreter
Create new spreadsheet	Use openpyxl in code_interpreter
Edit existing spreadsheet	Download from S3 → openpyxl → edit → upload in code_interpreter

Charts

When the user requests charts or visualizations, always attempt to embed charts directly using openpyxl first. Only use the chart skill if direct embedding is not possible or the chart type is unsupported by openpyxl.

from openpyxl.chart import BarChart, Reference

chart = BarChart()
data = Reference(sheet, min_col=2, min_row=1, max_row=5)
chart.add_data(data, titles_from_data=True)
sheet.add_chart(chart, "E2")

Chart QA — Overlap Detection

After generating the file, run this check to catch overlapping charts before uploading:

from openpyxl import load_workbook
from openpyxl.drawing.spreadsheet_drawing import TwoCellAnchor, OneCellAnchor

# Approximate EMU per default cell (column width ~8.43 chars, row height ~15pt)
EMU_PER_COL = 600000
EMU_PER_ROW = 190500

def _bounds(anchor):
    if isinstance(anchor, TwoCellAnchor):
        return (anchor._from.col, anchor._from.row, anchor.to.col, anchor.to.row)
    if isinstance(anchor, OneCellAnchor):
        c, r = anchor._from.col, anchor._from.row
        return (c, r, c + round(anchor.ext.cx / EMU_PER_COL), r + round(anchor.ext.cy / EMU_PER_ROW))
    return None

def _overlaps(a, b):
    return not (a[2] <= b[0] or b[2] <= a[0] or a[3] <= b[1] or b[3] <= a[1])

wb = load_workbook('./output.xlsx')
issues = []
for ws in wb.worksheets:
    bounds = [(c, _bounds(c.anchor)) for c in ws._charts]
    bounds = [(c, b) for c, b in bounds if b]
     i, (_, b1)  (bounds):
         _, b2  bounds[i+:]:
             _overlaps(b1, b2):
                issues.append()

 issues:
     issue  issues:
        ()
     ValueError()
:
    ()

If overlaps are detected, adjust the anchor cell or set an explicit size:

from openpyxl.drawing.spreadsheet_drawing import TwoCellAnchor
from openpyxl.drawing.xdr import XDRPoint2D, XDRPositiveSize2D
from openpyxl.utils.units import pixels_to_EMU

# Place chart at E2, spanning to O17 (no overlap with next chart starting at E19)
chart.anchor = "E2:O17"

---

Requirements for Outputs

All Excel files

Professional Font

• Use a consistent, professional font (e.g., Arial, Times New Roman) for all deliverables unless otherwise instructed by the user

Zero Formula Errors

• Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)

Preserve Existing Templates (when updating templates)

• Study and EXACTLY match existing format, style, and conventions when modifying files
• Never impose standardized formatting on files with established patterns
• Existing template conventions ALWAYS override these guidelines

Financial models

Color Coding Standards

Unless otherwise stated by the user or existing template

Industry-Standard Color Conventions

• Blue text (RGB: 0,0,255): Hardcoded inputs, and numbers users will change for scenarios
• Black text (RGB: 0,0,0): ALL formulas and calculations
• Green text (RGB: 0,128,0): Links pulling from other worksheets within same workbook
• Red text (RGB: 255,0,0): External links to other files
• Yellow background (RGB: 255,255,0): Key assumptions needing attention or cells that need to be updated

Number Formatting Standards

Required Format Rules

• Years: Format as text strings (e.g., "2024" not "2,024")
• Currency: Use $#,##0 format; ALWAYS specify units in headers ("Revenue ($mm)")
• Zeros: Use number formatting to make all zeros "-", including percentages (e.g., "$#,##0;($#,##0);-")
• Percentages: Default to 0.0% format (one decimal)
• Multiples: Format as 0.0x for valuation multiples (EV/EBITDA, P/E)
• Negative numbers: Use parentheses (123) not minus -123

Formula Construction Rules

Assumptions Placement

• Place ALL assumptions (growth rates, margins, multiples, etc.) in separate assumption cells
• Use cell references instead of hardcoded values in formulas
• Example: Use =B5*(1+$B$6) instead of =B5*1.05

Formula Error Prevention

• Verify all cell references are correct
• Check for off-by-one errors in ranges
• Ensure consistent formulas across all projection periods
• Test with edge cases (zero values, negative numbers)
• Verify no unintended circular references

Reading and analyzing data

Read .xlsx files by downloading from the given S3 path and using tools in code_interpreter.

Data analysis with pandas

import boto3
import pandas as pd

s3 = boto3.client('s3')
s3.download_file(bucket, key, 'spreadsheet.xlsx')

df = pd.read_excel('spreadsheet.xlsx')  # Default: first sheet
all_sheets = pd.read_excel('spreadsheet.xlsx', sheet_name=None)  # All sheets as dict

df.head()      # Preview data
df.info()      # Column info
df.describe()  # Statistics

Excel File Workflows

CRITICAL: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python and hardcoding them. This ensures the spreadsheet remains dynamic and updateable.

❌ WRONG - Hardcoding Calculated Values

total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes 5000

growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue']
sheet['C5'] = growth  # Hardcodes 0.15

✅ CORRECT - Using Excel Formulas

sheet['B10'] = '=SUM(B2:B9)'
sheet['C5'] = '=(C4-C2)/C2'

Formulas are stored as strings by openpyxl and recalculated automatically when the user opens the file in Excel or LibreOffice Calc.

Common Workflow

1. Choose tool: pandas for data, openpyxl for formulas/formatting
2. Create/Load: Create new workbook or load existing file
3. Modify: Add/edit data, formulas, and formatting
4. Save: Write to file
5. Upload: Upload to S3

Creating new Excel files

from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment
import boto3

wb = Workbook()
sheet = wb.active

sheet['A1'] = 'Hello'
sheet['B1'] = 'World'
sheet.append(['Row', 'of', 'data'])

sheet['B2'] = '=SUM(A1:A10)'

sheet['A1'].font = Font(bold=True, color='FF0000')
sheet['A1'].fill = PatternFill('solid', start_color='FFFF00')
sheet['A1'].alignment = Alignment(horizontal='center')

sheet.column_dimensions['A'].width = 20

wb.save('./output.xlsx')

# Upload to S3
s3 = boto3.client('s3')
with open('./output.xlsx', 'rb') as f:
    s3.upload_fileobj(
        f, BUCKET, KEY,
        ExtraArgs={'ContentType': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'}
    )

Editing existing Excel files

from openpyxl import load_workbook
import boto3

# Download from S3
s3 = boto3.client('s3')
s3.download_file(bucket, key, 'existing.xlsx')

wb = load_workbook('existing.xlsx')
sheet = wb.active  # or wb['SheetName'] for specific sheet

for sheet_name in wb.sheetnames:
    sheet = wb[sheet_name]

sheet['A1'] = 'New Value'
sheet.insert_rows(2)
sheet.delete_cols(3)

new_sheet = wb.create_sheet('NewSheet')
new_sheet['A1'] = 'Data'

wb.save('./modified.xlsx')

# Upload to S3
with open('./modified.xlsx', 'rb') as f:
    s3.upload_fileobj(
        f, BUCKET, KEY,
        ExtraArgs={'ContentType': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'}
    )

Best Practices

Library Selection

• pandas: Best for data analysis, bulk operations, and simple data export
• openpyxl: Best for complex formatting, formulas, and Excel-specific features

Working with openpyxl

• Cell indices are 1-based (row=1, column=1 refers to cell A1)
• Use data_only=True to read cached values from existing files: load_workbook('file.xlsx', data_only=True)
• Note: openpyxl-generated files do not have cached formula values — formulas recalculate when opened in Excel/LibreOffice Calc
• For large files: Use read_only=True for reading or write_only=True for writing

Working with pandas

• Specify data types to avoid inference issues: pd.read_excel('file.xlsx', dtype={'id': str})
• For large files, read specific columns: pd.read_excel('file.xlsx', usecols=['A', 'C', 'E'])
• Handle dates properly: pd.read_excel('file.xlsx', parse_dates=['date_column'])

Code Style Guidelines

IMPORTANT: When generating Python code for Excel operations:

• Write minimal, concise Python code without unnecessary comments
• Avoid verbose variable names and redundant operations
• Avoid unnecessary print statements

For Excel files themselves:

• Document data sources for hardcoded values
• Include notes for key calculations and model sections

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Dependencies

openpyxl, pandas, boto3, numpy, Pillow, matplotlib are pre-installed in the Code Interpreter sandbox. Do NOT call !pip install — import directly.