financial-calculator

dkyazzentwatwa/chatgpt-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill financial-calculator
0 commentsdiscussion
summary

Professional-grade financial calculations with detailed breakdowns, visualizations, and exportable reports. Handles everything from simple loan payments to complex retirement projections with Monte Carlo simulations.

skill.md

Financial Calculator Suite

Professional-grade financial calculations with detailed breakdowns, visualizations, and exportable reports. Handles everything from simple loan payments to complex retirement projections with Monte Carlo simulations.

Core Calculators

  • Loan Calculator: Amortization schedules, payment breakdowns, prepayment scenarios
  • Investment Calculator: Future value, compound growth, recurring contributions
  • NPV/IRR Calculator: Net present value, internal rate of return, payback period
  • Retirement Calculator: Savings projections, withdrawal strategies, longevity analysis
  • Monte Carlo Simulator: Risk analysis with probability distributions
  • Mortgage Calculator: Home affordability, refinance comparison
  • Savings Goal Calculator: Time to goal, required contributions

Quick Start

from scripts.financial_calc import FinancialCalculator

# Loan calculation
calc = FinancialCalculator()
loan = calc.loan_payment(principal=250000, rate=6.5, years=30)
print(f"Monthly payment: ${loan['monthly_payment']:,.2f}")

# Investment growth
growth = calc.investment_growth(
    principal=10000,
    rate=7,
    years=20,
    monthly_contribution=500
)
print(f"Final value: ${growth['final_value']:,.2f}")

Loan Calculator

Basic Loan Payment

from scripts.financial_calc import FinancialCalculator

calc = FinancialCalculator()

# Calculate monthly payment
loan = calc.loan_payment(
    principal=250000,    # Loan amount
    rate=6.5,            # Annual interest rate (%)
    years=30             # Loan term
)

print(f"Monthly Payment: ${loan['monthly_payment']:,.2f}")
print(f"Total Payments: ${loan['total_payments']:,.2f}")
print(f"Total Interest: ${loan['total_interest']:,.2f}")

Amortization Schedule

# Get full amortization schedule
schedule = calc.amortization_schedule(
    principal=250000,
    rate=6.5,
    years=30
)

# Schedule is a list of monthly payments
for payment in schedule[:12]:  # First year
    print(f"Month {payment['month']}: "
          f"Payment ${payment['payment']:,.2f}, "
          f"Principal ${payment['principal']:,.2f}, "
          f"Interest ${payment['interest']:,.2f}, "
          f"Balance ${payment['balance']:,.2f}")

# Export to CSV
calc.export_amortization(schedule, "loan_schedule.csv")

Prepayment Analysis

# Compare with extra payments
comparison = calc.prepayment_comparison(
    principal=250000,
    rate=6.5,
    years=30,
    extra_monthly=200
)

print(f"With extra payments:")
print(f"  Months saved: {comparison['months_saved']}")
print(f"  Interest saved: ${comparison['interest_saved']:,.2f}")
print(f"  New payoff: {comparison['new_term_years']:.1f} years")

Investment Calculator

Future Value

# Simple compound growth
result = calc.future_value(
    principal=10000,
    rate=7,           # Annual return (%)
    years=20
)
print(f"Future value: ${result['future_value']:,.2f}")

# With monthly contributions
result = calc.investment_growth(
    principal=10000,
    rate=7,
    years=20,
    monthly_contribution=500
)
print(f"Final value: ${result['final_value']:,.2f}")
print(f"Total contributions: ${result['total_contributions']:,.2f}")
print(f"Total growth: ${result['total_growth']:,.2f}")

Investment Comparison

# Compare different scenarios
scenarios = calc.compare_investments([
    {'name': 'Conservative', 'rate': 4, 'principal': 10000, 'monthly': 500},
    {'name': 'Moderate', 'rate': 7, 'principal': 10000, 'monthly': 500},
    {'name': 'Aggressive', 'rate': 10, 'principal': 10000, 'monthly': 500},
], years=20)

for s in scenarios:
    print(f"{s['name']}: ${s['final_value']:,.2f}")

NPV/IRR Calculator

Net Present Value

# Calcu
how to use financial-calculator

How to use financial-calculator on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add financial-calculator
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill financial-calculator

The skills CLI fetches financial-calculator from GitHub repository dkyazzentwatwa/chatgpt-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/financial-calculator

Reload or restart Cursor to activate financial-calculator. Access the skill through slash commands (e.g., /financial-calculator) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.567 reviews
  • Chaitanya Patil· Dec 24, 2024

    I recommend financial-calculator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Aisha Mensah· Dec 24, 2024

    financial-calculator is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Maya Diallo· Dec 20, 2024

    Solid pick for teams standardizing on skills: financial-calculator is focused, and the summary matches what you get after install.

  • Aisha Abbas· Dec 20, 2024

    Registry listing for financial-calculator matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Li Thompson· Dec 20, 2024

    Useful defaults in financial-calculator — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Arya Yang· Dec 16, 2024

    financial-calculator has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Arya Wang· Nov 19, 2024

    financial-calculator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Piyush G· Nov 15, 2024

    Solid pick for teams standardizing on skills: financial-calculator is focused, and the summary matches what you get after install.

  • Ira Jain· Nov 15, 2024

    Keeps context tight: financial-calculator is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Carlos Verma· Nov 11, 2024

    I recommend financial-calculator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

showing 1-10 of 67

1 / 7