finance-manager

Q: How do I install finance-manager?

Run npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill finance-manager in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/finance-manager/ (personal) or .claude/skills/finance-manager/ (project).

by ailabs-393Business & Finance

Comprehensive personal finance management system for analyzing transaction data, generating insights, creating visualizations, and providing actionable financial recommendations. Use when users need to analyze spending patterns, track budgets, visualize financial data, extract transactions from PDFs, calculate savings rates, identify spending trends, generate financial reports, or receive personalized budget recommendations. Triggers include requests like "analyze my finances", "track my spending", "create a financial report", "extract transactions from PDF", "visualize my budget", "where is my money going", "financial insights", "spending breakdown", or any finance-related analysis tasks.

Installs: 2.5K
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill finance-manager

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads finance-manager automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

FAQ

What does the finance-manager skill do?

How do I install finance-manager?

Run npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill finance-manager in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/finance-manager/ (personal) or .claude/skills/finance-manager/ (project).

Which AI agents can use finance-manager?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is finance-manager safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains finance-manager?

It is published by ailabs-393 in ailabs-393/ai-labs-claude-skills. It has 2,485 recorded installs.