Claude Skills for Financial Applications

Build real-world financial dashboards, portfolio analytics, and automated reporting workflows using Claude's Excel, PowerPoint, and PDF skills.

💡 Real-world Impact: These are the same Skills that power Claude Creates Files(opens in new tab), enabling Claude to create professional financial documents directly in the interface.

What you'll learn:

Create comprehensive financial models in Excel with formulas and charts
Generate executive presentations from financial data
Build portfolio analysis tools with risk metrics
Automate multi-format reporting pipelines

Setup & Data Loading
Use Case 1: Financial Dashboard Creation
Use Case 2: Portfolio Analysis Workflow
- Portfolio Analytics Excel
- Investment Committee Deck
Use Case 3: Automated Reporting Pipeline

Prerequisites

This notebook assumes you've completed Notebook 1: Introduction to Skills.

If you haven't:

Complete the setup in Notebook 1 first
Verify your environment with the test cells
Ensure you can create and download files

Required:

Anthropic API key configured
SDK version 0.69.0 installed from whl
Virtual environment activated

1. Setup & Data Loading

Let's start by importing our dependencies and loading the financial data we'll work with throughout this notebook.

# Standard imports

import json

import os

import sys

from pathlib import Path

import pandas as pd

# Add parent directory for imports

sys.path.insert(0, str(Path.cwd().parent))

# Anthropic SDK

from anthropic import Anthropic

from dotenv import load_dotenv

# Our utilities

from file_utils import (

download_all_files,

print_download_summary,

)

# Load environment

load_dotenv(Path.cwd().parent / ".env")

# Configuration

API_KEY = os.getenv("ANTHROPIC_API_KEY")

MODEL = "claude-sonnet-4-6"

if not API_KEY:

raise ValueError("ANTHROPIC_API_KEY not found. Please configure your .env file.")

# Initialize client

client = Anthropic(api_key=API_KEY)

# Setup directories

OUTPUT_DIR = Path.cwd().parent / "outputs" / "financial"

OUTPUT_DIR.mkdir(parents=True, exist_ok=True)

DATA_DIR = Path.cwd().parent / "sample_data"

print("✓ Environment configured")

print(f"✓ Output directory: {OUTPUT_DIR}")

print(f"✓ Data directory: {DATA_DIR}")

Load Financial Data

We have four datasets representing different aspects of a company's financial position:

# Load financial statements

financial_statements = pd.read_csv(DATA_DIR / "financial_statements.csv")

print("📊 Financial Statements Overview:")

print(f" Shape: {financial_statements.shape}")

print(f" Categories: {len(financial_statements['Category'].unique())} financial metrics")

print(f" Quarters: {list(financial_statements.columns[1:5])}")

print()

# Show sample data

print("Sample data (first 5 rows):")

financial_statements.head()

# Load portfolio holdings

with open(DATA_DIR / "portfolio_holdings.json") as f:

portfolio_data = json.load(f)

print("💼 Portfolio Overview:")

print(f" Portfolio: {portfolio_data['portfolio_name']}")

print(f" Total Value: ${portfolio_data['total_value']:,.2f}")

print(f" Holdings: {len(portfolio_data['holdings'])} stocks")

print(f" Cash Position: ${portfolio_data['cash_position']['amount']:,.2f}")

print(f" Total Return: {portfolio_data['performance_metrics']['total_return_percent']:.1f}%")

print()

# Convert holdings to DataFrame for easier manipulation

portfolio_df = pd.DataFrame(portfolio_data["holdings"])

print("Top 5 holdings by value:")

portfolio_df.nlargest(5, "market_value")[["ticker", "name", "market_value", "unrealized_gain"]]

# Load quarterly metrics

with open(DATA_DIR / "quarterly_metrics.json") as f:

quarterly_metrics = json.load(f)

print("📈 Quarterly Metrics Overview:")

print(f" Quarters available: {len(quarterly_metrics['quarters'])}")

print(f" Metrics per quarter: {len(quarterly_metrics['quarters'][0])} KPIs")

print()

# Show latest quarter metrics

latest_quarter = quarterly_metrics["quarters"][-1]

print(f"Latest Quarter ({latest_quarter['quarter']}):")

for key, value in latest_quarter.items():

if key != "quarter" and isinstance(value, int | float):

if "revenue" in key.lower() or "cost" in key.lower():

print(f" {key.replace('_', ' ').title()}: ${value:,.0f}")

elif "percent" in key.lower() or "margin" in key.lower() or "rate" in key.lower():

print(f" {key.replace('_', ' ').title()}: {value:.1f}%")

else:

print(f" {key.replace('_', ' ').title()}: {value:,.0f}")

Helper Functions

Let's define some helper functions for this notebook:

def create_skills_message(client, prompt, skills, prefix="", show_token_usage=True):

"""

Helper function to create messages with Skills.

Args:

client: Anthropic client

prompt: User prompt

skills: List of skill dicts [{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}]

prefix: Prefix for downloaded files

show_token_usage: Whether to print token usage

Returns:

Tuple of (response, download_results)

"""

response = client.beta.messages.create(

model=MODEL,

max_tokens=4096,

container={"skills": skills},

tools=[{"type": "code_execution_20250825", "name": "code_execution"}],

messages=[{"role": "user", "content": prompt}],

betas=[

"code-execution-2025-08-25",

"files-api-2025-04-14",

"skills-2025-10-02",

)

if show_token_usage:

print(

f"\n📊 Token Usage: {response.usage.input_tokens} in, {response.usage.output_tokens} out"

)

# Download files

results = download_all_files(client, response, output_dir=str(OUTPUT_DIR), prefix=prefix)

return response, results

def format_financial_value(value, is_currency=True, decimals=0):

"""Format financial values for display."""

if is_currency:

return f"${value:,.{decimals}f}"

else:

return f"{value:,.{decimals}f}"

print("✓ Helper functions defined")

2. Use Case 1: Financial Dashboard Creation

Now that we have our data loaded and helper functions defined, let's dive into our first practical use case: creating comprehensive financial dashboards. We'll start by generating multi-sheet Excel workbooks that automatically include formulas, formatting, and charts.

2.1 Excel Financial Model

We'll create a financial dashboard that includes:

Profit & Loss statements with year-over-year comparisons
Balance sheet analysis
Cash flow tracking
KPI dashboards with visualizations

This demonstrates how Claude's Skills can handle complex Excel generation tasks that would typically require hours of manual work.

# Create Financial Dashboard Excel

print("Creating financial dashboard Excel file...")

print("This creates a 2-sheet dashboard optimized for the Skills API.")

print("\n⏱️ Generation time: 1-2 minutes\n")

# Prepare the financial data

fs_data = financial_statements.to_dict("records")

quarters_2024 = ["Q1_2024", "Q2_2024", "Q3_2024", "Q4_2024"]

# Extract key financial metrics

revenue_by_quarter = {

"Q1 2024": financial_statements[financial_statements["Category"] == "Revenue"][

"Q1_2024"

].values[0],

"Q2 2024": financial_statements[financial_statements["Category"] == "Revenue"][

"Q2_2024"

].values[0],

"Q3 2024": financial_statements[financial_statements["Category"] == "Revenue"][

"Q3_2024"

].values[0],

"Q4 2024": financial_statements[financial_statements["Category"] == "Revenue"][

"Q4_2024"

].values[0],

}

financial_dashboard_prompt = f"""

Create a financial dashboard Excel workbook with 2 sheets:

Sheet 1 - "P&L Summary":

Create a Profit & Loss summary table for 2024 quarters with these rows:

- Revenue: {", ".join([f"Q{i + 1}: ${v / 1000000:.1f}M" for i, v in enumerate(revenue_by_quarter.values())])}

- Gross Profit: Use values from the data

- Operating Income: Use values from the data

- Net Income: Use values from the data

- Add a Total column with SUM formulas

- Add a row showing profit margins (Net Income / Revenue)

- Apply currency formatting and bold headers

- Add a simple bar chart showing quarterly revenue

Sheet 2 - "Key Metrics":

Create a metrics dashboard with:

- Total Revenue 2024: SUM of all quarters

- Average Quarterly Revenue: AVERAGE formula

- Q4 vs Q1 Growth: Percentage increase

- Best Quarter: MAX formula to identify

- Operating Margin Q4: Calculate from data

- Year-over-year growth vs 2023

Apply professional formatting with borders, bold headers, and currency formats.

"""

# Create the Excel financial dashboard

excel_response, excel_results = create_skills_message(

client,

financial_dashboard_prompt,

[{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}],

prefix="financial_dashboard_",

)

print("\n" + "=" * 60)

print_download_summary(excel_results)

if len(excel_results) > 0 and excel_results[0]["success"]:

print("\n✅ Financial dashboard Excel created successfully!")

💡 Best Practices for Excel Generation

Based on our testing, here are the optimal approaches for creating Excel files with Skills:

Recommended Approach:

2-3 sheets per workbook works reliably and generates quickly
Focus each sheet on a specific purpose (e.g., P&L, metrics, charts)
Add complexity incrementally - start simple, then enhance

For Complex Dashboards:

Create multiple focused files instead of one complex file
- Example: financial_pnl.xlsx, balance_sheet.xlsx, kpi_dashboard.xlsx
Use the pipeline pattern to create and enhance files sequentially
Combine files programmatically using pandas or openpyxl if needed

Performance Tips:

Simple 2-sheet dashboards: ~1-2 minutes
PowerPoint and PDF generation: Very reliable for complex content
Token usage: Structured data (JSON/CSV) is more efficient than prose

2.2 Executive PowerPoint

With our financial data now organized in Excel, let's create an executive presentation that summarizes the key insights. This demonstrates how Skills can generate professional PowerPoint presentations with charts, formatted text, and multiple slides - perfect for board meetings or investor updates.

The presentation will include:

Q4 2024 performance highlights
Financial metrics with year-over-year comparisons
Profitability trends with visualizations
Key takeaways and outlook

print("Creating executive presentation from financial metrics...")

print("\n⏱️ Generation time: 1-2 minutes\n")

# Calculate some key metrics for the presentation

q4_2024_revenue = 14500000

q4_2023_revenue = 12300000

yoy_growth = (q4_2024_revenue - q4_2023_revenue) / q4_2023_revenue * 100

q4_2024_net_income = 1878750

q4_2023_net_income = 1209000

net_income_growth = (q4_2024_net_income - q4_2023_net_income) / q4_2023_net_income * 100

executive_ppt_prompt = f"""

Create a 4-slide executive presentation for Q4 2024 financial results:

Slide 1 - Title:

- Title: "Q4 2024 Financial Results"

- Subtitle: "Executive Summary - Acme Corporation"

- Date: January 2025

Slide 2 - Financial Highlights:

- Title: "Q4 2024 Performance Highlights"

- Create a two-column layout:

Left side - Key Metrics:

• Revenue: $14.5M (+{yoy_growth:.1f}% YoY)

• Net Income: $1.88M (+{net_income_growth:.1f}% YoY)

• Operating Margin: 17.9% (up 2.9pp)

• Operating Cash Flow: $2.85M

Right side - Column chart showing quarterly revenue:

Q1 2024: $12.5M

Q2 2024: $13.2M

Q3 2024: $13.8M

Q4 2024: $14.5M

Slide 3 - Profitability Trends:

- Title: "Margin Expansion & Profitability"

- Add a line chart showing net margin % by quarter:

Q1 2024: 11.4%

Q2 2024: 11.8%

Q3 2024: 12.4%

Q4 2024: 13.0%

- Add bullet points below:

• Consistent margin expansion throughout 2024

• Operating leverage driving profitability

• Cost optimization initiatives delivering results

Slide 4 - Key Takeaways:

- Title: "Key Takeaways & Outlook"

- Bullet points:

✓ Record Q4 revenue of $14.5M

✓ 17.9% YoY revenue growth

✓ 55% increase in net income YoY

✓ Strong cash generation: $2.85M operating cash flow

✓ Well-positioned for continued growth in 2025

Use professional corporate design:

- Dark blue (#003366) for headers

- Clean, modern layout

- Data-driven visualizations

"""

# Create the executive presentation

ppt_response, ppt_results = create_skills_message(

client,

executive_ppt_prompt,

[{"type": "anthropic", "skill_id": "pptx", "version": "latest"}],

prefix="executive_summary_",

)

print("\n" + "=" * 60)

print_download_summary(ppt_results)

if len(ppt_results) > 0 and ppt_results[0]["success"]:

print("\n✅ Executive presentation created successfully!")

3. Use Case 2: Portfolio Analysis Workflow

Now let's shift our focus from company financials to investment portfolio analysis. In this section, we'll demonstrate how to create comprehensive portfolio analytics and investment committee presentations using the portfolio data we loaded earlier.

This workflow showcases:

Detailed portfolio performance analysis in Excel
Risk metrics and sector allocation visualization
Professional investment committee presentations
Data-driven rebalancing recommendations

We'll start by creating an Excel workbook with portfolio analytics, then generate an investment committee presentation that summarizes our findings.

First, let's create a comprehensive portfolio analysis Excel workbook

Before we create the investment committee presentation, we need to analyze our portfolio data in detail. This Excel workbook will serve as the foundation for our investment recommendations.

print("Creating portfolio analysis Excel workbook...")

print("This creates a focused 2-sheet portfolio analysis optimized for the Skills API.")

print("\n⏱️ Generation time: 1-2 minutes\n")

# Prepare portfolio data for the prompt

top_holdings = portfolio_df.nlargest(5, "market_value")

sector_allocation = portfolio_data["sector_allocation"]

portfolio_excel_prompt = f"""

Create a portfolio analysis Excel workbook with 2 sheets:

Sheet 1 - "Portfolio Overview":

Create a comprehensive holdings and performance table:

Section 1 - Holdings (top of sheet):

{portfolio_df[["ticker", "name", "shares", "current_price", "market_value", "unrealized_gain", "allocation_percent"]].head(10).to_string()}

Section 2 - Portfolio Summary:

- Total portfolio value: ${portfolio_data["total_value"]:,.2f}

- Total unrealized gain: ${portfolio_df["unrealized_gain"].sum():,.2f}

- Total Return: {portfolio_data["performance_metrics"]["total_return_percent"]:.1f}%

- YTD Return: {portfolio_data["performance_metrics"]["year_to_date_return"]:.1f}%

- Sharpe Ratio: {portfolio_data["performance_metrics"]["sharpe_ratio"]:.2f}

- Portfolio Beta: {portfolio_data["performance_metrics"]["beta"]:.2f}

Apply conditional formatting: green for gains, red for losses.

Add a bar chart showing top 5 holdings by value.

Sheet 2 - "Sector Analysis & Risk":

Create sector allocation and risk metrics:

Section 1 - Sector Allocation:

{json.dumps(sector_allocation, indent=2)}

Include a pie chart of sector allocation.

Section 2 - Key Risk Metrics:

- Portfolio Beta: {portfolio_data["performance_metrics"]["beta"]:.2f}

- Standard Deviation: {portfolio_data["performance_metrics"]["standard_deviation"]:.1f}%

- Value at Risk (95%): $62,500

- Maximum Drawdown: -12.3%

- Sharpe Ratio: {portfolio_data["performance_metrics"]["sharpe_ratio"]:.2f}

Section 3 - Rebalancing Recommendations:

- Reduce Technology from 20% to 18%

- Increase Healthcare from 8.7% to 10%

- Maintain current diversification

Apply professional formatting with clear sections and headers.

"""

# Create portfolio analysis Excel

portfolio_response, portfolio_results = create_skills_message(

client,

portfolio_excel_prompt,

[{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}],

prefix="portfolio_analysis_",

)

print("\n" + "=" * 60)

print_download_summary(portfolio_results)

if len(portfolio_results) > 0 and portfolio_results[0]["success"]:

print("\n✅ Portfolio analysis Excel created successfully!")

3.2 Investment Committee Presentation

With our detailed portfolio analysis complete, let's now create a professional presentation for the investment committee. This presentation will distill the key insights from our Excel analysis into a concise, visual format suitable for decision-makers.

The presentation will cover:

Portfolio performance summary with key metrics
Asset allocation and diversification analysis
Risk metrics and risk-adjusted returns
Strategic recommendations for rebalancing

print("Creating investment committee presentation...")

print("\n⏱️ Generation time: 1-2 minutes\n")

investment_deck_prompt = f"""

Create a 5-slide investment committee presentation:

Slide 1 - Title:

- Title: "Portfolio Review - Q4 2024"

- Subtitle: "{portfolio_data["portfolio_name"]}"

- Date: January 2025

- Portfolio Value: ${portfolio_data["total_value"]:,.0f}

Slide 2 - Portfolio Overview:

- Title: "Portfolio Performance Summary"

- Two-column layout:

Left - Key Metrics:

• Total Value: ${portfolio_data["total_value"]:,.0f}

• YTD Return: +{portfolio_data["performance_metrics"]["year_to_date_return"]:.1f}%

• Total Return: ${portfolio_data["performance_metrics"]["total_return"]:,.0f}

• Sharpe Ratio: {portfolio_data["performance_metrics"]["sharpe_ratio"]:.2f}

Right - Bar chart of top 5 holdings by value:

{", ".join([f"{h['ticker']}: ${h['market_value']:,.0f}" for h in top_holdings.to_dict("records")])}

Slide 3 - Sector Allocation:

- Title: "Asset Allocation & Diversification"

- Pie chart showing:

Technology: {sector_allocation["Technology"]:.1f}%

Financials: {sector_allocation["Financials"]:.1f}%

Healthcare: {sector_allocation["Healthcare"]:.1f}%

Consumer: {sector_allocation["Consumer Discretionary"] + sector_allocation["Consumer Staples"]:.1f}%

Fixed Income: {sector_allocation["Bonds"]:.1f}%

Cash: {sector_allocation["Cash"]:.1f}%

Slide 4 - Risk Analysis:

- Title: "Risk Metrics & Analysis"

- Content:

Risk Indicators:

• Portfolio Beta: {portfolio_data["performance_metrics"]["beta"]:.2f} (lower market risk)

• Standard Deviation: {portfolio_data["performance_metrics"]["standard_deviation"]:.1f}%

• Maximum Drawdown: -12.3%

• Value at Risk (95%): $62,500

Risk-Adjusted Performance:

• Sharpe Ratio: {portfolio_data["performance_metrics"]["sharpe_ratio"]:.2f} (excellent)

• Alpha Generation: +2.3% vs benchmark

Slide 5 - Recommendations:

- Title: "Strategic Recommendations"

- Bullet points:

✓ Maintain current allocation - well diversified

✓ Consider profit-taking in Technology (20% → 18%)

✓ Increase Healthcare allocation (8.7% → 10%)

✓ Monitor bond duration given rate environment

✓ Rebalance quarterly to maintain targets

Use professional investment presentation design.

"""

# Create investment committee deck

investment_response, investment_results = create_skills_message(

client,

investment_deck_prompt,

[{"type": "anthropic", "skill_id": "pptx", "version": "latest"}],

prefix="investment_committee_",

)

print("\n" + "=" * 60)

print_download_summary(investment_results)

print("\n✅ Investment committee presentation created successfully!")

4. Use Case 3: Automated Reporting Pipeline

So far, we've created individual documents for specific purposes. Now let's demonstrate the power of chaining multiple Skills together in an automated workflow. This pipeline pattern is essential for production systems where you need to generate multiple related documents from the same data source.

In this example, we'll create a complete reporting suite that:

Analyzes data in Excel with calculations and charts
Summarizes insights in a PowerPoint presentation
Documents the process in a formal PDF report

This showcases how Skills can work together to create a comprehensive reporting solution that would traditionally require multiple tools and manual coordination.

Key benefits of the pipeline approach:

Consistent data across all documents
Reduced total generation time
Token usage optimization
Scalable to multiple report types

⏱️ Total expected time: 2-3 minutes for the complete pipeline

print("🔄 Starting Automated Reporting Pipeline")

print("=" * 60)

print("This will create a complete reporting suite:")

print("1. Excel analysis → 2. PowerPoint summary → 3. PDF documentation")

print("\n⏱️ Total pipeline time: 2-3 minutes\n")

# Track token usage across the pipeline

pipeline_tokens = {"input": 0, "output": 0}

# Step 1: Create Excel Analysis

print("Step 1/3: Creating Excel analysis with quarterly metrics...")

excel_pipeline_prompt = f"""

Create a quarterly business metrics Excel file:

Sheet 1 - "Quarterly KPIs":

Create a table with these quarterly metrics for 2024:

{

json.dumps(

[

{

k: v

for k, v in q.items()

if k in ["quarter", "revenue", "gross_margin", "customer_count", "churn_rate"]

}

for q in quarterly_metrics["quarters"]

indent=2,

)

}

Add:

- Quarter-over-quarter growth calculations

- Average and total rows

- Conditional formatting for trends

- Line chart showing revenue trend

- Column chart showing customer count

Sheet 2 - "YoY Comparison":

Compare Q4 2024 vs Q4 2023 for all metrics.

Calculate percentage changes and highlight improvements.

Professional formatting with headers and borders.

"""

excel_response, excel_results = create_skills_message(

client,

excel_pipeline_prompt,

[{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}],

prefix="pipeline_1_metrics_",

show_token_usage=False,

)

pipeline_tokens["input"] += excel_response.usage.input_tokens

pipeline_tokens["output"] += excel_response.usage.output_tokens

print(

f"✓ Excel created - Tokens: {excel_response.usage.input_tokens} in, {excel_response.usage.output_tokens} out"

)

# Step 2: Create PowerPoint Summary

print("\nStep 2/3: Creating PowerPoint summary from metrics...")

ppt_pipeline_prompt = """

Create a 3-slide quarterly metrics summary presentation:

Slide 1:

- Title: "Q4 2024 Metrics Summary"

- Subtitle: "Automated Reporting Pipeline Demo"

Slide 2:

- Title: "Key Performance Indicators"

- Show Q4 2024 metrics:

• Revenue: $3.2M (+15% QoQ)

• Customers: 850 (+8.9% QoQ)

• Gross Margin: 72%

• Churn Rate: 2.8% (improved from 3.5%)

- Add a simple bar chart comparing Q3 vs Q4 revenue

Slide 3:

- Title: "Quarterly Trend Analysis"

- Line chart showing revenue growth Q1-Q4

- Key insight bullets:

• Consistent QoQ growth

• Customer acquisition accelerating

• Churn reduction successful

Clean, data-focused design.

"""

ppt_response, ppt_results = create_skills_message(

client,

ppt_pipeline_prompt,

[{"type": "anthropic", "skill_id": "pptx", "version": "latest"}],

prefix="pipeline_2_summary_",

show_token_usage=False,

)

pipeline_tokens["input"] += ppt_response.usage.input_tokens

pipeline_tokens["output"] += ppt_response.usage.output_tokens

print(

f"✓ PowerPoint created - Tokens: {ppt_response.usage.input_tokens} in, {ppt_response.usage.output_tokens} out"

)

# Step 3: Create PDF Documentation

print("\nStep 3/3: Creating PDF documentation...")

pdf_pipeline_prompt = """

Create a PDF document summarizing the quarterly reporting pipeline:

AUTOMATED REPORTING PIPELINE

Q4 2024 Results Documentation

EXECUTIVE SUMMARY

This document summarizes the Q4 2024 business metrics generated through

our automated reporting pipeline.

KEY METRICS

- Revenue: $3.2M (15% QoQ growth)

- Customer Base: 850 active customers

- Gross Margin: 72%

- Churn Rate: 2.8% (improved from 3.5%)

PIPELINE COMPONENTS

1. Data Processing: Quarterly metrics analyzed in Excel

2. Visualization: Key insights presented in PowerPoint

3. Documentation: Formal report generated in PDF

AUTOMATION BENEFITS

• Reduced reporting time by 90%

• Consistent format and quality

• Eliminated manual errors

• Scalable to multiple reports

NEXT STEPS

- Expand pipeline to include predictive analytics

- Add automated email distribution

- Implement real-time data feeds

Generated: January 2025

Pipeline Version: 1.0

Format as a professional technical document.

"""

pdf_response, pdf_results = create_skills_message(

client,

pdf_pipeline_prompt,

[{"type": "anthropic", "skill_id": "pdf", "version": "latest"}],

prefix="pipeline_3_documentation_",

show_token_usage=False,

)

pipeline_tokens["input"] += pdf_response.usage.input_tokens

pipeline_tokens["output"] += pdf_response.usage.output_tokens

print(

f"✓ PDF created - Tokens: {pdf_response.usage.input_tokens} in, {pdf_response.usage.output_tokens} out"

)

# Pipeline Summary

print("\n" + "=" * 60)

print("🎯 PIPELINE COMPLETE!")

print("=" * 60)

print("\n📊 Pipeline Token Usage Summary:")

print(f" Total Input Tokens: {pipeline_tokens['input']:,}")

print(f" Total Output Tokens: {pipeline_tokens['output']:,}")

print(f" Total Tokens: {pipeline_tokens['input'] + pipeline_tokens['output']:,}")

print(f" Average per document: {(pipeline_tokens['input'] + pipeline_tokens['output']) // 3:,}")

print("\n📁 Generated Files:")

all_results = excel_results + ppt_results + pdf_results

for i, result in enumerate(all_results, 1):

if result["success"]:

print(f" {i}. {os.path.basename(result['output_path'])} ({result['size'] / 1024:.1f} KB)")

print("\n✅ Automated reporting pipeline executed successfully!")

print(" All three documents created and linked in workflow.")

Summary & Next Steps

What We've Accomplished

In this notebook, you've learned how to:

✅ Financial Dashboard Creation

Built multi-sheet Excel models with formulas and charts
Generated executive PowerPoint presentations
Created professional PDF reports

✅ Portfolio Analysis

Developed portfolio analytics workbooks
Created investment committee presentations
Implemented risk metrics and rebalancing tools

✅ Automated Pipelines

Chained multiple document formats
Optimized token usage
Built production-ready patterns

Key Takeaways

Skills dramatically simplify financial document creation - What would take hours manually takes minutes
Token efficiency is excellent - Skills use ~90% fewer tokens than manual instructions
Quality is professional-grade - Documents are immediately usable in business contexts
Automation is straightforward - Pipeline patterns enable complex workflows

Continue Your Learning

📚 Next: Notebook 3 - Custom Skills Development(opens in new tab)

Build your own specialized financial skills
Create company-specific templates
Implement advanced automation

Try These Experiments

Modify the financial dashboard to include your own metrics
Create a custom portfolio with different asset classes
Build a pipeline for your specific reporting needs
Experiment with complexity to understand generation times
Track token usage across different document types

Claude Skills for financial applications

Claude Skills for Financial Applications

Table of Contents

Prerequisites

1. Setup & Data Loading

Load Financial Data

Helper Functions

2. Use Case 1: Financial Dashboard Creation

2.1 Excel Financial Model

💡 Best Practices for Excel Generation

2.2 Executive PowerPoint

3. Use Case 2: Portfolio Analysis Workflow

First, let's create a comprehensive portfolio analysis Excel workbook

3.2 Investment Committee Presentation

4. Use Case 3: Automated Reporting Pipeline

Summary & Next Steps

What We've Accomplished

Key Takeaways

Continue Your Learning

Try These Experiments

Resources

Claude Skills for Financial Applications

Table of Contents

Prerequisites

1. Setup & Data Loading

Load Financial Data

Helper Functions

2. Use Case 1: Financial Dashboard Creation

2.1 Excel Financial Model

💡 Best Practices for Excel Generation

2.2 Executive PowerPoint

3. Use Case 2: Portfolio Analysis Workflow

First, let's create a comprehensive portfolio analysis Excel workbook

3.2 Investment Committee Presentation

4. Use Case 3: Automated Reporting Pipeline

Summary & Next Steps

What We've Accomplished

Key Takeaways

Continue Your Learning

Try These Experiments

Resources

Claude Skills for Financial Applications

Table of Contents

Prerequisites

1. Setup & Data Loading

Load Financial Data

Helper Functions

2. Use Case 1: Financial Dashboard Creation

2.1 Excel Financial Model

💡 Best Practices for Excel Generation

2.2 Executive PowerPoint

3. Use Case 2: Portfolio Analysis Workflow

First, let's create a comprehensive portfolio analysis Excel workbook

3.2 Investment Committee Presentation

4. Use Case 3: Automated Reporting Pipeline

Summary & Next Steps

What We've Accomplished

Key Takeaways

Continue Your Learning

Try These Experiments

Resources