xlsx-processing-manus

name	xlsx-processing-manus
description	Professional Excel spreadsheet creation with a focus on aesthetics and data analysis. Use when creating spreadsheets for organizing, analyzing, and presenting structured data in a clear and professional format.
metadata	{"author":"Manus","license":"Proprietary.","version":"2025.12.01"}

Excel Generator Skill

Goal

Make the user able to use the Excel immediately and gain insights upon opening.

Core Principle

Enrich visuals as much as possible, while ensuring content clarity and not adding cognitive burden. Every visual element should be meaningful and purposeful—serving the content, not decorating it.

---

Part 1: User Needs & Feature Matching

Before creating any Excel, think through:

1. What does the user need? — Not "an Excel file", but what problem are they solving?
2. What can I provide? — Which features will help them?
3. How to match? — Select the right combination for this specific scenario.

Feature ↔ User Value Pairs

Help Users「Understand Data」

Feature	User Value	When to Use
Bar/Column Chart	See comparisons at a glance	Comparing values across categories
Line Chart	See trends at a glance	Time series data
Pie Chart	See proportions at a glance	Part-to-whole (≤6 categories)
Data Bars	Compare magnitude without leaving the cell	Numeric columns needing quick comparison
Color Scale	Heatmap effect, patterns pop out	Matrices, ranges, distributions
Sparklines	See trend within a single cell	Summary rows with historical context

Help Users「Find What Matters」

Feature	User Value	When to Use
Pre-sorting	Most important data comes first	Rankings, Top N, priorities
Conditional Highlighting	Key data stands out automatically	Outliers, thresholds, Top/Bottom N
Icon Sets	Status visible at a glance	KPI status, categorical states (use sparingly)
Bold/Color Emphasis	Visual distinction between primary and secondary	Summary rows, key metrics
KEY INSIGHTS Section	Conclusions delivered directly	Analytical reports

Help Users「Save Time」

Feature	User Value	When to Use
Overview Sheet	Summary on first page, no hunting	All multi-sheet files
Pre-calculated Summaries	Results ready, no manual calculation	Data requiring statistics
Consistent Number Formats	No format adjustments needed	All numeric data
Freeze Panes	Headers visible while scrolling	Tables with >10 rows
Sheet Index with Links	Quick navigation, no guessing	Files with >3 sheets

Help Users「Use Directly」

Feature	User Value	When to Use
Filters	Users can explore data themselves	Exploratory analysis needs
Hyperlinks	Click to navigate, no manual switching	Cross-sheet references, external sources
Print-friendly Layout	Ready to print or export to PDF	Reports for sharing
Formulas (not hardcoded)	Change parameters, results update	Models, forecasts, adjustable scenarios
Data Validation Dropdowns	Prevent input errors	Templates requiring user input

Help Users「Trust the Data」

Feature	User Value	When to Use
Data Source Attribution	Know where data comes from	All external data
Generation Date	Know data freshness	Time-sensitive reports
Data Time Range	Know what period is covered	Time series data
Professional Formatting	Looks reliable	All external-facing files
Consistent Precision	No doubts about accuracy	All numeric values

Help Users「Gain Insights」

Feature	User Value	When to Use
Comparison Columns (Δ, %)	No manual calculation for comparisons	YoY, MoM, A vs B
Rank Column	Position visible directly	Competitive analysis, performance
Grouped Summaries	Aggregated results by dimension	Segmented analysis
Trend Indicators (↑↓)	Direction clear at a glance	Change direction matters
Insight Text	The "so what" is stated explicitly	Analytical reports

---

Part 2: Four-Layer Implementation

Layer 1: Structure (How It's Organized)

Goal: Logical, easy to navigate, user finds what they need immediately.

Sheet Organization

Guideline	Recommendation
Sheet count	3-5 ideal, max 7
First sheet	Always "Overview" with summary and navigation
Sheet order	General → Specific (Overview → Data → Analysis)
Naming	Clear, concise (e.g., "Revenue Data", not "Sheet1")

Information Architecture

• Overview sheet must stand alone: User should understand the main message without opening other sheets
• Progressive disclosure: Summary first, details available for those who want to dig deeper
• Consistent structure across sheets: Same layout patterns, same starting positions

Layout Rules

Element	Position
Left margin	Column A empty (width 3)
Top margin	Row 1 empty
Content start	Cell B2
Section spacing	1 empty row between sections
Table spacing	2 empty rows between tables
Charts	Below all tables (2 rows gap), or right of related table

Chart placement:

• Default: below all tables, left-aligned with content
• Alternative: right of a single related table
• Charts must never overlap each other or tables

Standalone Text Rows

For rows with a single text cell (titles, descriptions, notes, bullet points), text will naturally extend into empty cells to the right. However, text is clipped if right cells contain any content (including spaces).

Decision logic:

Condition	Action
Right cells guaranteed empty	No action needed—text extends naturally
Right cells may have content	Merge cells to content width, or wrap text
Text exceeds content area width	Wrap text + set row height manually

Technical note: Fill and border alone do NOT block text overflow—only actual cell content (including space characters) blocks it.

Navigation

For files with 3+ sheets, include a Sheet Index on Overview:

# Sheet Index with hyperlinks
ws['B5'] = "CONTENTS"
ws['B5'].font = Font(name=SERIF_FONT, size=14, bold=True, color=THEME['accent'])

sheets = ["Overview", "Data", "Analysis"]
for i, sheet_name in enumerate(sheets, start=6):
    cell = ws.cell(row=i, column=2, value=sheet_name)
    cell.hyperlink = f"#'{sheet_name}'!A1"
    cell.font = Font(color=THEME['accent'], underline='single')

---

Layer 2: Information (What They Learn)

Goal: Accurate, complete, insightful—user gains knowledge, not just data.

Number Formats

Critical rules:

1. Every numeric cell must have number_format set — both input values AND formula results
2. Same column = same precision — never mix 0.1074 and 1.0 in one column
3. Formula results have no default format — they display raw precision unless explicitly formatted

Data Type	Format Code	Example
Integer	#,##0	1,234,567
Decimal (1)	#,##0.0	1,234.6
Decimal (2)	#,##0.00	1,234.56
Percentage	0.0%	12.3%
Currency	$#,##0.00	$1,234.56

Common mistake: Setting format only for input cells, forgetting formula cells.

# WRONG: Formula cell without number_format
ws['C10'] = '=C7-C9'  # Will display raw precision like 14.123456789

# CORRECT: Always set number_format for formula cells
ws['C10'] = '=C7-C9'
ws['C10'].number_format = '#,##0.0'  # Displays as 14.1

# Best practice: Define format by column/data type, apply to ALL cells
for row in range(data_start, data_end + 1):
    cell = ws.cell(row=row, column=value_col)
    cell.number_format = '#,##0.0'  # Applies to both values and formulas

Data Context

Every data set needs context:

Element	Location	Example
Data source	Overview or sheet footer	"Source: Company Annual Report 2024"
Time range	Near title or in subtitle	"Data from Jan 2020 - Dec 2024"
Generation date	Overview footer	"Generated: 2024-01-15"
Definitions	Notes section or separate sheet	"Revenue = Net sales excluding returns"

Key Insights

For analytical content, don't just present data—tell the user what it means:

ws['B20'] = "KEY INSIGHTS"
ws['B20'].font = Font(name=SERIF_FONT, size=14, bold=True, color=THEME['accent'])

insights = [
    "• Revenue grew 23% YoY, driven primarily by APAC expansion",
    "• Top 3 customers account for 45% of total revenue",
    "• Q4 showed strongest performance across all metrics"
]
for i, insight in enumerate(insights, start=21):
    ws.cell(row=i, column=2, value=insight)

Content Completeness

Check	Action
Missing values	Show as blank or "N/A", never 0 unless actually zero
Calculated fields	Include formula or note explaining calculation
Abbreviations	Define on first use or in notes
Units	Include in header (e.g., "Revenue ($M)")

---

Layer 3: Visual (What They See)

Goal: Professional appearance, immediate sense of value, visuals serve content.

Essential Setup

from openpyxl.styles import Font, PatternFill, Alignment, Border, Side

# Hide gridlines for cleaner look
ws.sheet_view.showGridLines = False

# Left margin
ws.column_dimensions['A'].width = 3

Theme System

Choose ONE theme per workbook. All visual elements derive from the theme color.

Available Themes (select based on context, or let user specify):

Theme	Primary	Light	Use Case
Elegant Black	2D2D2D	E5E5E5	Luxury, fashion, premium reports (recommended default)
Corporate Blue	1F4E79	D6E3F0	Finance, corporate analysis
Forest Green	2E5A4C	D4E5DE	Sustainability, environmental
Burgundy	722F37	E8D5D7	Luxury brands, wine industry
Slate Gray	4A5568	E2E8F0	Tech, modern, minimalist
Navy	1E3A5F	D3DCE6	Government, maritime, institutional
Charcoal	36454F	DDE1E4	Professional, executive
Deep Purple	4A235A	E1D5E7	Creative, innovation, premium tech
Teal	1A5F5F	D3E5E5	Healthcare, wellness
Warm Brown	5D4037	E6DDD9	Natural, organic, artisan
Royal Blue	1A237E	D3D5E8	Academic, institutional
Olive	556B2F	E0E5D5	Military, outdoor

Theme Configuration:

# === THEME CONFIGURATION ===
THEMES = {
    'elegant_black': {
        'primary': '2D2D2D',
        'light': 'E5E5E5',
        'accent': '2D2D2D',
        'chart_colors': ['2D2D2D', '4A4A4A', '6B6B6B', '8C8C8C', 'ADADAD', 'CFCFCF'],
    },
    'corporate_blue': {
        'primary': '1F4E79',
        'light': 'D6E3F0',
        'accent': '1F4E79',
        'chart_colors': ['1F4E79', '2E75B6', '5B9BD5', '9DC3E6', 'BDD7EE', 'DEEBF7'],
    },
    # ... other themes follow same pattern
}

THEME = THEMES['elegant_black']  # Default
SERIF_FONT = 'Source Serif Pro'   # or 'Georgia' as fallback
SANS_FONT = 'Source Sans Pro'     # or 'Calibri' as fallback

How Theme Colors Apply:

Element	Color	Background
Document title	THEME['primary']	None
Section header	THEME['primary']	None or THEME['light']
Table header	White	THEME['primary']
Data cells	Black	None or alternating F9F9F9
Chart elements	THEME['chart_colors']	—

Semantic Colors

For data meaning (independent of theme):

Semantic	Color	Use
Positive	2E7D32	Growth, profit, success
Negative	C62828	Decline, loss, failure
Warning	F57C00	Caution, attention

Row Highlight Colors

For row-level emphasis. Use high-lightness tints (85-95% lightness) for subtle distinction.

Semantic	Hex	Hue	Use
Emphasis	E6F3FF	209°	Top rated, important data
Section	FFF3E0	37°	Section dividers
Input	FFFDE7	55°	Editable cells
Special	FFF9C4	55°	Base case, benchmarks
Success	E8F5E9	125°	Passed, completed
Warning	FFCCBC	14°	Needs attention

Rule: Same semantic = same color. Different semantic = different color.

HIGHLIGHT = {
    'emphasis': 'E6F3FF',
    'section': 'FFF3E0',
    'input': 'FFFDE7',
    'special': 'FFF9C4',
    'success': 'E8F5E9',
    'warning': 'FFCCBC',
}

Typography

Use serif + sans-serif pairing. Serif for hierarchy (titles, headers); sans-serif for data (tabular figures).

Font Pairings (in preference order):

Serif (Titles)	Sans-Serif (Data)	Notes
Source Serif Pro	Source Sans Pro	Adobe superfamily, recommended
IBM Plex Serif	IBM Plex Sans	Modern, corporate
Georgia	Calibri	Pre-installed fallback

Hierarchy:

Element	Font	Size	Style
Document title	Serif	18-22	Bold, Primary color
Section header	Serif	12-14	Bold, Primary color
Table header	Serif	10-11	Bold, White
Data cells	Sans-Serif	11	Regular, Black
Notes	Sans-Serif	9-10	Italic, 666666

# Document title
ws['B2'].font = Font(name=SERIF_FONT, size=18, bold=True, color=THEME['primary'])

# Section header
ws['B6'].font = Font(name=SERIF_FONT, size=14, bold=True, color=THEME['primary'])

# Table header
header_font = Font(name=SERIF_FONT, size=10, bold=True, color='FFFFFF')

# Data cells
data_font = Font(name=SANS_FONT, size=11)

# Notes
notes_font = Font(name=SANS_FONT, size=10, italic=True, color='666666')

Data Block Definition

A Data Block is a group of continuous, non-empty rows that form a visual unit requiring borders. Data Blocks are separated by empty rows.

Identification rules:

1. Scan from Section Header downward
2. Non-empty row starts a Data Block
3. Empty row ends the current Data Block
4. Each Data Block gets its own outer frame

Data Block types:

Type	Structure	Example
With Header	Header row + data rows	Table with column titles
Without Header	Data rows only	Continuation data, sub-tables

Example recognition:

Row 5: Section Header "INCOME STATEMENT"   → No border (not a Data Block)
Row 6: Empty                               → Separator
Row 7: Header (Item, Year1, Year2...)      → Data Block 1 starts
Row 8: Revenue, 47061, 48943...            
Row 9: Growth Rate, 4.0%, 3.5%...          → Data Block 1 ends
Row 10: Empty                              → Separator
Row 11: EBITDA, 12121, 12627...            → Data Block 2 starts (no header)
Row 12: EBITDA Margin, 25.8%, 25.8%...     → Data Block 2 ends
Row 13: Empty                              → Separator
Row 14: D&A, 1200, 1224...                 → Data Block 3 starts (no header)
Row 15: EBIT, 10921, 11404...              
Row 16: EBIT Margin, 23.2%, 23.3%...       → Data Block 3 ends

Result: 3 separate Data Blocks, each with its own outer frame.

Border Rules

Recommended style: Horizontal-only — cleaner, more modern.

Each Data Block must have:

Border Type	Where
Outer frame	All 4 sides of Data Block (top, bottom, left, right)
Header bottom	Medium weight, theme primary color (if has header)
Internal horizontal	Thin, between all rows
Internal vertical	None (omit for cleaner look)

Critical: Every cell in the Data Block must have its border set. Do not only set header and label cells—data cells need borders too.

# Border definitions
outer_border = Side(style='thin', color='D1D1D1')
header_bottom = Side(style='medium', color=THEME['primary'])
inner_horizontal = Side(style='thin', color='D1D1D1')
no_border = Side(style=None)

def apply_data_block_borders(ws, start_row, end_row, start_col, end_col, has_header=True):
    """
    Apply borders to a Data Block.
    Every cell must be processed—not just headers and labels.
    """
    for row in range(start_row, end_row + 1):
        for col in range(start_col, end_col + 1):
            cell = ws.cell(row=row, column=col)
            
            # Left/Right: outer frame
            left = outer_border if col == start_col else no_border
            right = outer_border if col == end_col else no_border
            
            # Top: outer for first row, inner horizontal for others
            top = outer_border if row == start_row else inner_horizontal
            
            # Bottom: header_bottom for header, outer for last row, inner for middle
            if has_header and row == start_row:
                bottom = header_bottom
            elif row == end_row:
                bottom = outer_border
            else:
                bottom = inner_horizontal
            
            cell.border = Border(left=left, right=right, top=top, bottom=bottom)

Non-Data-Block elements (titles, section headers, standalone text, notes): No border.

Alignment

Headers: Center.

Data cells:

Content	Horizontal
Short text (words)	Center
Long text (sentences)	Left + indent=1
Numbers	Right
Dates, Status	Center

Vertical: Always center (including wrapped text).

# Left-aligned text with padding
cell.alignment = Alignment(horizontal='left', vertical='center', indent=1)

# Numbers
cell.alignment = Alignment(horizontal='right', vertical='center')

# Wrapped text
cell.alignment = Alignment(horizontal='left', vertical='center', wrap_text=True, indent=1)

Column Width

Calculation scope: Only Data Block cells. Exclude standalone text rows, section headers, and notes.

Formula: max(max_content_length_in_data_blocks + padding, minimum)

Column Type	Padding	Minimum	Notes
Label/Text	+4	15	First column usually
Numbers	+6	14	Extra space for formatted numbers (negative signs, commas)
Long text	+4	20, max 40	Wrap if exceeds max

Design constraint: Same column across different Data Blocks should serve similar roles. Column width is determined by the widest content across all Data Blocks in that column.

Standalone text rows: Do NOT include in column width calculation. Let text extend naturally or use wrap/merge.

def calculate_column_width(ws, col, data_block_ranges):
    """
    Calculate column width based only on Data Block content.
    Standalone text rows are excluded.
    
    data_block_ranges: list of (start_row, end_row) tuples
    """
    max_len = 0
    is_numeric = True
    
    for start_row, end_row in data_block_ranges:
        for row in range(start_row, end_row + 1):
            cell = ws.cell(row=row, column=col)
            if cell.value:
                # Get formatted display length
                display_value = str(cell.value)
                max_len = max(max_len, len(display_value))
                if not isinstance(cell.value, (int, float)):
                    is_numeric = False
    
    padding = 6 if is_numeric else 4
    minimum = 14 if is_numeric else 15
    
    return max(max_len + padding, minimum)

Row Height

Must set manually—openpyxl does not auto-adjust.

Row Type	Height
Document title	35
Section header	25
Table header	30
Standard data	18
Wrapped text	lines × 15 + 10

ws.row_dimensions[2].height = 35   # Title
ws.row_dimensions[5].height = 25   # Section header
ws.row_dimensions[7].height = 30   # Table header
ws.row_dimensions[8].height = 18   # Data row

Merge Cells

Element	Merge?	Span
Document/Sheet title	Yes	Width of content below
Section header	Yes	Width of related table
Multi-level header (parent)	Yes	Span child columns
Long text row	Yes	Width of content area

When to merge: Merge when text would otherwise be clipped at the column boundary. If text fits within a single column, merging is optional.

Common cases requiring merge:

• Titles and subtitles (usually span full content width)
• Section headers (span width of related table)
• KEY INSIGHTS bullet points (long sentences)
• Notes and disclaimers (multi-sentence text)

Section header with background — merge width must match table width:

# Section header spans same width as table below
last_col = 8  # Must match table's last column
ws.merge_cells(f'B6:{get_column_letter(last_col)}6')
ws['B6'] = "KEY METRICS"
ws['B6'].font = Font(name=SERIF_FONT, size=14, bold=True, color=THEME['primary'])
ws['B6'].fill = PatternFill(start_color=THEME['light'], end_color=THEME['light'], fill_type='solid')
ws['B6'].alignment = Alignment(horizontal='left', vertical='center')

Data Visualization

Data Bars:

from openpyxl.formatting.rule import DataBarRule

rule = DataBarRule(start_type='min', end_type='max', color=THEME['primary'])
ws.conditional_formatting.add('C5:C50', rule)

Color Scale:

from openpyxl.formatting.rule import ColorScaleRule

rule = ColorScaleRule(
    start_type='min', start_color='FFFFFF',
    end_type='max', end_color=THEME['primary']
)
ws.conditional_formatting.add('D5:D50', rule)

Charts:

from openpyxl.chart import BarChart, Reference

chart = BarChart()
chart.title = "Revenue by Region"
data = Reference(ws, min_col=3, min_row=4, max_row=10)
cats = Reference(ws, min_col=2, min_row=5, max_row=10)
chart.add_data(data, titles_from_data=True)
chart.set_categories(cats)

# Apply theme colors to chart series
for i, series in enumerate(chart.series):
    series.graphicalProperties.solidFill = THEME['chart_colors'][i % len(THEME['chart_colors'])]

ws.add_chart(chart, "F5")

---

Layer 4: Interaction (How They Interact)

Goal: Usable, flexible, user can explore and work with the data.

Freeze Panes

For tables with >10 rows:

ws.freeze_panes = 'B5'  # Freeze below header row

Filters

For tables with >20 rows:

ws.auto_filter.ref = f"B4:{get_column_letter(last_col)}{last_row}"

Hyperlinks

# Internal link
cell.hyperlink = "#'Data'!A1"
cell.font = Font(color=THEME['accent'], underline='single')

# External link
cell.hyperlink = "https://example.com"
cell.font = Font(color=THEME['accent'], underline='single')

Sorting

Pre-sort by most meaningful dimension:

• Rankings → by value descending
• Time series → by date ascending
• Alphabetical → when no clear priority

df = df.sort_values('revenue', ascending=False)

Editability

• Use formulas when users may update inputs
• Use hardcoded values when data is final
• Keep formulas simple; document complex ones