finance-manager

SKILL.md

Finance Manager

A comprehensive toolkit for personal finance management that processes transaction data, performs sophisticated financial analysis, generates actionable insights, and creates beautiful visual reports.

Core Capabilities

Transaction Data Processing: Extract financial data from PDFs, CSVs, or JSON files

Financial Analysis: Calculate key metrics, identify spending patterns, and track savings

Visualization: Generate interactive HTML reports with charts and graphs

Budget Recommendations: Provide personalized, actionable advice based on spending patterns

Trend Analysis: Identify spending patterns, anomalies, and opportunities for optimization

Workflow

1. Data Extraction and Preparation
For PDF files:

python scripts/extract_pdf_data.py <input.pdf> <output.csv>

For CSV/JSON files:

Ensure data has columns: Date, Description, Income (category), Type, Amount

Date format: YYYY-MM-DD or parseable date string

Amount: Positive for income, negative for expenses

2. Financial Analysis

Run comprehensive analysis on transaction data:

python scripts/analyze_finances.py <transactions.csv> > analysis_output.json

Output includes:

Summary statistics (total income, expenses, net savings, savings rate)

Spending trends (daily averages, top expenses, category percentages)

Budget recommendations (personalized based on spending patterns)

Visualization data (prepared for charting)

3. Report Generation

Create interactive HTML report with visualizations:

python scripts/generate_report.py <analysis_output.json> <report.html>

Report features:

Summary dashboard with key metrics

Interactive pie chart showing spending by category

Bar chart comparing income vs expenses over time

Color-coded indicators (green for positive, red for negative)

Personalized recommendations section

Responsive design for all devices

4. Complete Workflow Example

# Extract data from PDF
python scripts/extract_pdf_data.py finance_data.pdf transactions.csv

# Analyze the data
python scripts/analyze_finances.py transactions.csv > analysis.json

# Generate visual report
python scripts/generate_report.py analysis.json financial_report.html

Key Metrics and Benchmarks

Savings Rate

Savings Rate = (Total Income - Total Expenses) / Total Income × 100

Benchmarks:

Below 10%: Needs improvement

10-20%: Good

20-30%: Excellent

Above 30%: Outstanding

Category Guidelines (% of income)

Housing: 25-30%

Transportation: 10-15%

Food: 10-15%

Utilities: 5-10%

Savings: Minimum 20%

For detailed frameworks and methodologies, see references/financial_frameworks.md.

Analysis Features

Summary Statistics

Total income and expenses for the period

Net savings (can be positive or negative)

Savings rate percentage

Transaction count

Date range covered

Spending Trends

Daily average spending

Top 5 largest expenses with details

Category percentage breakdown

Spending patterns over time

Budget Recommendations

The system generates personalized recommendations based on:

Savings rate thresholds

Category spending percentages

Income diversification

Budget guideline comparisons

Example recommendations:

"⚠️ Your savings rate is below 10%. Consider reducing discretionary spending."

"🍽️ Food spending is 18% of expenses. Consider meal planning to reduce costs."

"✅ Excellent savings rate! You're on track for strong financial health."

Visualization Components

Category Spending Chart (Doughnut)

Shows proportional breakdown of expenses by category with color coding.

Income vs Expenses Chart (Bar)

Displays monthly comparison of income and expenses to identify cash flow trends.

Interactive Features

Hover tooltips showing exact values

Responsive design adapting to screen size

Color-coded positive (green) and negative (red) indicators

Tips for Best Results

Data Quality

Ensure all transactions are properly categorized

Use consistent category names

Include complete date information

Verify amounts are correctly signed (+ for income, - for expenses)

Analysis Frequency

Run monthly analysis for trend tracking

Generate reports at month-end for review

Compare month-over-month to identify changes

Action on Recommendations

Prioritize recommendations by potential impact

Set specific, measurable goals based on insights

Track progress by re-running analysis regularly

Dependencies

All scripts require Python 3.7+ with standard libraries. Additional requirements:

For PDF extraction:

pip install pdfplumber --break-system-packages

For data analysis:

pip install pandas --break-system-packages

All visualization dependencies are loaded from CDN in the HTML output (Chart.js).

File Organization

finance-manager/
├── scripts/
│   ├── extract_pdf_data.py     # PDF → CSV conversion
│   ├── analyze_finances.py     # Financial analysis engine
│   └── generate_report.py      # HTML report generator
└── references/
    └── financial_frameworks.md # Detailed analysis methodologies

Customization

Adding Custom Categories

Edit the category definitions in analyze_finances.py to match your tracking system.

Adjusting Thresholds

Modify recommendation thresholds in the generate_budget_recommendations() function to match personal goals.

Styling Reports

Customize the HTML_TEMPLATE in generate_report.py to adjust colors, fonts, or layout.

Common Use Cases

Monthly Review:
"Analyze my October spending and create a report"

Budget Optimization:

"Where am I spending too much money?"

Trend Analysis:
"How does my spending this month compare to last month?"

Goal Setting:
"What's my savings rate and how can I improve it?"

Category Insights:
"Break down my food spending by transaction"

PDF Processing:
"Extract all transactions from my bank statement PDF"

Best Practices

Consistent Categorization: Use the same category names across all transactions

Regular Analysis: Run monthly to spot trends early

Act on Insights: Use recommendations to make specific spending changes

Track Progress: Compare reports month-over-month

Verify Data: Always check extracted PDF data for accuracy before analysis

Reference Materials

For comprehensive financial frameworks, budgeting guidelines, and analysis methodologies, read:

view references/financial_frameworks.md

This includes:

The 50/30/20 budget rule

Category spending benchmarks

Financial health indicators

Analysis workflow details

Visualization best practices

Recommendation logic