budget-analyzer

Q: How do I install budget-analyzer?

Run npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill budget-analyzer 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/budget-analyzer/ (personal) or .claude/skills/budget-analyzer/ (project).

by dkyazzentwatwaData & Databases

Analyze personal or business expenses from CSV/Excel. Categorize spending, identify trends, compare periods, and get savings recommendations.

Installs: 116
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill budget-analyzer

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 budget-analyzer 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.

Budget Analyzer

Comprehensive expense analysis tool for personal finance and business budgeting.

Features

Auto-Categorization: Classify expenses by merchant/description
Trend Analysis: Month-over-month spending patterns
Period Comparison: Compare spending across time periods
Category Breakdown: Pie charts and bar graphs by category
Savings Recommendations: Identify areas to reduce spending
Budget vs Actual: Track against budget targets
Export Reports: PDF and HTML summaries

Quick Start

from budget_analyzer import BudgetAnalyzer

analyzer = BudgetAnalyzer()

# Load transaction data
analyzer.load_csv("transactions.csv",
                  date_col="date",
                  amount_col="amount",
                  description_col="description")

# Analyze spending
summary = analyzer.analyze()
print(summary)

# Get category breakdown
categories = analyzer.by_category()
print(categories)

# Generate report
analyzer.generate_report("budget_report.pdf")

CLI Usage

# Basic analysis
python budget_analyzer.py --input transactions.csv --date date --amount amount

# With custom categories
python budget_analyzer.py --input data.csv --categories custom_categories.json

# Compare two periods
python budget_analyzer.py --input data.csv --compare "2024-01" "2024-02"

# Generate PDF report
python budget_analyzer.py --input data.csv --report report.pdf

# Set budget targets
python budget_analyzer.py --input data.csv --budget budget.json --report report.pdf

Input Format

Transaction CSV

date,amount,description,category
2024-01-15,45.99,Amazon Purchase,Shopping
2024-01-16,12.50,Starbucks,Food & Dining
2024-01-17,150.00,Electric Company,Utilities

Custom Categories (JSON)

{
  "Food & Dining": ["starbucks", "mcdonalds", "restaurant", "uber eats"],
  "Transportation": ["uber", "lyft", "gas station", "shell"],
  "Shopping": ["amazon", "walmart", "target"],
  "Utilities": ["electric", "water", "gas", "internet"]
}

Budget Targets (JSON)

{
  "Food & Dining": 500,
  "Transportation": 200,
  "Shopping": 300,
  "Utilities": 250,
  "Entertainment": 150
}

API Reference

BudgetAnalyzer Class

class BudgetAnalyzer:
    def __init__(self)

    # Data Loading
    def load_csv(self, filepath: str, date_col: str, amount_col: str,
                 description_col: str = None, category_col: str = None) -> 'BudgetAnalyzer'
    def load_dataframe(self, df: pd.DataFrame) -> 'BudgetAnalyzer'

    # Categorization
    def set_categories(self, categories: Dict[str, List[str]]) -> 'BudgetAnalyzer'
    def auto_categorize(self) -> 'BudgetAnalyzer'

    # Analysis
    def analyze(self) -> Dict  # Full summary
    def by_category(self) -> pd.DataFrame
    def by_month(self) -> pd.DataFrame
    def by_day_of_week(self) -> pd.DataFrame
    def top_expenses(self, n: int = 10) -> pd.DataFrame
    def recurring_expenses(self) -> pd.DataFrame

    # Comparison
    def compare_periods(self, period1: str, period2: str) -> Dict
    def year_over_year(self) -> pd.DataFrame

    # Budgeting
    def set_budget(self, budget: Dict[str, float]) -> 'BudgetAnalyzer'
    def budget_vs_actual(self) -> pd.DataFrame
    def budget_alerts(self) -> List[Dict]

    # Insights
    def get_recommendations(self) -> List[str]
    def spending_score(self) -> Dict

    # Visualization
    def plot_categories(self, output: str) -> str
    def plot_trends(self, output: str) -> str
    def plot_budget_comparison(self, output: str) -> str

    # Export
    def generate_report(self, output: str, format: str = "pdf") -> str
    def to_csv(self, output: str) -> str

Analysis Features

Summary Statistics

summary = analyzer.analyze()
# Returns:
# {
#     "total_spent": 2500.00,
#     "transaction_count": 45,
#     "date_range": {"start": "2024-01-01", "end": "2024-01-31"},
#     "average_transaction": 55.56,
#     "largest_expense": {"amount": 500, "description": "Rent"},
#     "categories": {"Food": 450, "Transport": 200, ...}
# }

Category Breakdown

categories = analyzer.by_category()
# Returns DataFrame:
#   category        | amount  | percentage | count
#   Food & Dining   | 450.00  | 18.0%      | 15
#   Transportation  | 200.00  | 8.0%       | 8
#   ...

Monthly Trends

monthly = analyzer.by_month()
# Returns DataFrame:
#   month    | total    | avg_transaction | count
#   2024-01  | 2500.00  | 55.56          | 45
#   2024-02  | 2800.00  | 60.87          | 46

Period Comparison

comparison = analyzer.compare_periods("2024-01", "2024-02")
# Returns:
# {
#     "period1_total": 2500.00,
#     "period2_total": 2800.00,
#     "difference": 300.00,
#     "percent_change": 12.0,
#     "category_changes": {
#         "Food": {"change": 50, "percent": 11.1},
#         ...
#     }
# }

Budget Tracking

Set Budget Targets

analyzer.set_budget({
    "Food & Dining": 500,
    "Transportation": 200,
    "Shopping": 300
})

Budget vs Actual

comparison = analyzer.budget_vs_actual()
# Returns DataFrame:
#   category        | budget | actual | difference | status
#   Food & Dining   | 500    | 450    | 50         | under
#   Transportation  | 200    | 250    | -50        | over

Budget Alerts

alerts = analyzer.budget_alerts()
# Returns:
# [
#     {"category": "Transportation", "status": "over", "amount": 250, "budget": 200, "percent_over": 25},
#     {"category": "Shopping", "status": "warning", "amount": 280, "budget": 300, "percent_used": 93}
# ]

Recommendations Engine

recommendations = analyzer.get_recommendations()
# Returns:
# [
#     "Food & Dining spending increased 15% from last month. Consider meal prepping.",
#     "You have 3 subscription services totaling $45/month. Review for unused subscriptions.",
#     "Transportation costs are 25% over budget. Consider carpooling or public transit.",
#     "Top merchant: Amazon ($350). Set spending limits for online shopping."
# ]

Spending Score

score = analyzer.spending_score()
# Returns:
# {
#     "overall_score": 72,  # 0-100
#     "factors": {
#         "budget_adherence": 65,
#         "spending_consistency": 80,
#         "savings_rate": 70
#     },
#     "grade": "B",
#     "summary": "Good spending habits with room for improvement in budget adherence."
# }

Auto-Categorization

Built-in category patterns:

DEFAULT_CATEGORIES = {
    "Food & Dining": ["restaurant", "cafe", "starbucks", "mcdonald", "uber eats", "doordash"],
    "Transportation": ["uber", "lyft", "gas", "shell", "chevron", "parking"],
    "Shopping": ["amazon", "walmart", "target", "costco", "best buy"],
    "Utilities": ["electric", "water", "gas", "internet", "phone", "verizon"],
    "Entertainment": ["netflix", "spotify", "hulu", "movie", "theater"],
    "Healthcare": ["pharmacy", "cvs", "walgreens", "doctor", "hospital"],
    "Travel": ["airline", "hotel", "airbnb", "booking"],
    "Subscriptions": ["subscription", "membership", "monthly"]
}

Visualizations

Category Pie Chart

analyzer.plot_categories("categories.png")
# Creates pie chart of spending by category

Spending Trends

analyzer.plot_trends("trends.png")
# Creates line chart of monthly spending over time

Budget Comparison

analyzer.plot_budget_comparison("budget.png")
# Creates bar chart comparing budget vs actual by category

Report Generation

PDF Report

analyzer.generate_report("report.pdf")
# Includes:
# - Executive summary
# - Category breakdown with charts
# - Monthly trends
# - Top expenses
# - Budget vs actual (if set)
# - Recommendations

HTML Report

analyzer.generate_report("report.html", format="html")
# Interactive HTML report with charts

Example Workflows

Personal Finance Review

analyzer = BudgetAnalyzer()
analyzer.load_csv("bank_transactions.csv",
                  date_col="Date",
                  amount_col="Amount",
                  description_col="Description")

# Auto-categorize transactions
analyzer.auto_categorize()

# Set monthly budget
analyzer.set_budget({
    "Food & Dining": 600,
    "Transportation": 250,
    "Entertainment": 200
})

# Get full analysis
print(analyzer.analyze())
print(analyzer.budget_vs_actual())
print(analyzer.get_recommendations())

# Generate report
analyzer.generate_report("monthly_review.pdf")

Business Expense Tracking

analyzer = BudgetAnalyzer()
analyzer.load_csv("business_expenses.csv",
                  date_col="date",
                  amount_col="amount",
                  category_col="expense_type")

# Compare quarters
q1_vs_q2 = analyzer.compare_periods("2024-Q1", "2024-Q2")

# Top expense categories
top = analyzer.by_category().head(5)

# Generate report for accounting
analyzer.generate_report("quarterly_expenses.pdf")

Dependencies

pandas>=2.0.0
numpy>=1.24.0
matplotlib>=3.7.0
reportlab>=4.0.0

FAQ

What does the budget-analyzer skill do?

Analyze personal or business expenses from CSV/Excel. Categorize spending, identify trends, compare periods, and get savings recommendations.

How do I install budget-analyzer?

Run npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill budget-analyzer 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/budget-analyzer/ (personal) or .claude/skills/budget-analyzer/ (project).

Which AI agents can use budget-analyzer?

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 budget-analyzer 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 budget-analyzer?

It is published by dkyazzentwatwa in dkyazzentwatwa/chatgpt-skills. It has 116 recorded installs.

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budget-analyzer

Install

Install

Describe the task

Review the result

SKILL.md

Budget Analyzer

Features

Quick Start

CLI Usage

Input Format

Transaction CSV

Custom Categories (JSON)

Budget Targets (JSON)

API Reference

BudgetAnalyzer Class

Analysis Features

Summary Statistics

Category Breakdown

Monthly Trends

Period Comparison

Budget Tracking

Set Budget Targets

Budget vs Actual

Budget Alerts

Recommendations Engine

Spending Score

Auto-Categorization

Visualizations

Category Pie Chart

Spending Trends

Budget Comparison

Report Generation

PDF Report

HTML Report

Example Workflows

Personal Finance Review

Business Expense Tracking

Dependencies

FAQ

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