Skip to content
Agentmart
Submit a tool

xlsx_mastery

Comprehensive guide to designing, creating, calculating, formatting, and charting professional Excel spreadsheets (.xlsx) in Python using openpyxl.

Skill by ParisNeo

IndexedApache-2.0★ 13
Write a review

Chalk Labs

Need help deploying xlsx_mastery?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/parisneo-xlsx-mastery

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Comprehensive guide to designing, creating, calculating, formatting, and charting professional Excel spreadsheets (.xlsx) in Python using openpyxl.

Use cases: spreadsheets

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Nov 1, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/xlsx-mastery

What's inside

Skill name
xlsx_mastery
Description
Comprehensive guide to designing, creating, calculating, formatting, and charting professional Excel spreadsheets (.xlsx) in Python using openpyxl.
License
Apache-2.0
Instructions
239 lines in SKILL.md · view on GitHub

Agent Skills spec issues

  • name: missing

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Professional Spreadsheet & Financial Model Engineering with Python

This skill teaches how to build clean, formula-driven, auditable Excel workbooks (`.xlsx`) using `openpyxl`, with corporate design standards, dynamic formulas, conditional formatting, and charts.

---

## 🎨 Part 1: High-Level Spreadsheet Design & Financial Standards

A production spreadsheet is a software artifact. It must be clean, readable, self-documenting, and free of hardcoded formula inputs.

### 1. Financial Color-Coding Conventions (FAST Standard)
* **Blue text on Soft Gray / White background** (`#0000FF` on `#F2F4F8`): **User Input / Hardcoded Assumptions**.
* **Black text on White** (`#000000`): **Formulas & Calculations**.
* **Green text** (`#008000`): **External Workbook / Database Link**.
* **Dark Header Fills** (Navy `#1F4E79`, Charcoal `#262626`): **Table headers with White Bold Text**.
* **Total / Summary Rows**: Double bottom border (`accounting underline`) and top thin border.

### 2. Number Formatting Matrix
Never leave raw unformatted numbers. Always assign explicit number formats:
- **Currency**: `"$#,##0"` (Standard) or `"$#,##0.00"` (Cents required)
- **Accounting (Zero as Dash)**: `_($* #,##0.00_);_($* (#,##0.00);_($* "-"??_);_(@_)`
- **Percentage**: `"0.0%"` or `"0.00%"`
- **Integer Quantities**: `"#,##0"`
- **Dates**: `"YYYY-MM-DD"` or `"DD-MMM-YYYY"`

### 3. Layout Architecture
- **Tab 1: `Summary / Dashboard`**: High-level KPI summary cards, KPI trends, dynamic executive charts.
- **Tab 2: `Assumptions`**: Input variables (growth rates, cost factors, tax percentages) with named ranges.
- **Tab 3: `Model / Calculations`**: Formulaic time-series projection (Monthly / Quarterly / Annual).
- **Freeze Panes**: Always freeze top header rows and left identification columns so data stays anchored on scroll.
- **Gridlines**: Always ensure `ws.views.sheetView[0].showGridLines = True` is set explicitly.

---

## 💻 Part 2: Building Professional Workbooks with openpyxl

### Required Dependency
```bash
pip install openpyxl
```

### Complete Dashboard & Financial Model Template

```python
import openpyxl
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.utils import get_column_letter
from openpyxl.chart import BarChart, Reference, Series

def create_financial_model(output_path: str = "financial_model.xlsx"):
    wb = openpyxl.Workbook()
    
    # ── 1. Tab 1: Executive Summary & Dashboard ──
    ws = wb.active
    ws.title = "Financial Summary"
    ws.views.sheetView[0].showGridLines = True

    # Palette Constants
    NAVY_FILL = PatternFill(start_color="1F4E79", end_color="1F4E79", fill_type="solid")
    ACCENT_FILL = PatternFill(start_color="D9E1F2", end_color="D9E1F2", fill_type="solid")
    ZEBRA_FILL = PatternFill(start_color="F9FAFB", end_color="F9FAFB", fill_type="solid")
    
    WHITE_BOLD = Font(name="Calibri", size=11, bold=True, color="FFFFFF")
    TITLE_FONT = Font(name="Calibri", size=16, bold=True, color="1F4E79")
    SECTION_FONT = Font(name="Calibri", size=12, bold=True, color="1F4E79")
    BOLD_FONT = Font(name="Calibri", size=10.5, bold=True)
    REGULAR_FONT = Font(name="Calibri", size=10.5)
    
    THIN_BORDER_SIDE = Side(style='thin', color='D3D3D3')
    THIN_BORDER = Border(left=THIN_BORDER_SIDE, right=THIN_BORDER_SIDE, top=THIN_BORDER_SIDE, bottom=THIN_BORDER_SIDE)
    DOUBLE_BOTTOM = Border(top=Side(style='thin', color='000000'), bottom=Side(style='double', color='000000'))

    # Title Block
    ws["B2"] = "Quarterly Revenue & Margin Projections"
    ws["B2"].font = TITLE_FONT
    ws["B3"] = "Forecast Model — Fiscal Year 2026"
    ws["B3"].font = Font(name="Calibri", size=11, italic=True, color="595959")

    # KPI Card 1: Total Revenue
    ws["B5"] = "Projected FY26 Revenue"
    ws["B5"].font = Font(size=9, color="595959")
    ws["B6"] = "=SUM(C11:F11)"
    ws["B6"].font = Font(size=16, bold=True, color="1F4E79")
    ws["B6"].number_format = "$#,##0"
    ws["B5"].fill = ACCENT_FILL
    ws["B6"].fill = ACCENT_FILL

    # KPI Card 2: Net Margin Average
    ws["D5"] = "Average Net Margin"
    ws["D5"].font = Font(size=9, color="595959")
    ws["D6"] = "=AVERAGE(C14:F14)"
    ws["D6"].font = Font(size=16, bold=True, color="1F4E79")
    ws["D6"].number_format = "0.0%"
    ws["D5"].fill = ACCENT_FILL
    ws["D6"].fill = ACCENT_FILL

    # Data Table Headers
    headers = ["Financial Metric", "Q1 2026", "Q2 2026", "Q3 2026", "Q4 2026", "FY2026 Total"]
    start_row = 10
    
    for col_idx, h in enumerate(headers, start=2):
        cell = ws.cell(row=start_row, column=col_idx, value=h)
        cell.font = WHITE_BOLD
        cell.fill = NAVY_FILL
        cell.alignment = Alignment(horizontal="left" if col_idx == 2 else "right", vertical="center")

    # Financial Rows Setup
    row_specs = [
        ("Revenue", [1200000, 1350000, 1500000, 1800000], "$#,##0", False),
        ("Cost of Goods Sold (COGS)", [480000, 520000, 580000, 690000], "$#,##0", False),
        ("Gross Profit", ["=C11-C12", "=D11-D12", "=E11-E12", "=F11-F12"], "$#,##0", True),
        ("Gross Margin %", ["=C13/C11", "=D13/D11", "=E13/E11", "=F13/F11"], "0.0%", False),
        ("Operating Expenses (OPEX)", [320000, 340000, 370000, 410000], "$#,##0", False),
        ("Operating Income (EBIT)", ["=C13-C15", "=D13-D15", "=E13-E15", "=F13-F15"], "$#,##0", True),
        ("Net Income", ["=C16*0.75", "=D16*0.75", "=E16*0.75", "=F16*0.75"], "$#,##0", True),
    ]

    for idx, (label, values, num_fmt, is_total) in enumerate(row_specs, start=11):
        # Label

From ParisNeo/lollms_client, Apache-2.0.

More from ParisNeo/lollms_client

Signals

Stars
13
Forks
5
Open issues
0
Last commit (this folder)
Sep 20, 2026 (1 month ago)
Last release
Mar 5, 2025 (1 year ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from ParisNeo

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get xlsx_mastery running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying xlsx_mastery?

CallGet help
Cite this page

xlsx_mastery skill. Agent Mart. https://agentmart.dev/skills/parisneo-xlsx-mastery (last updated 2026-10-24)