Financial Analyst
This skill provides a production-ready financial analysis toolkit for ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construct...
Install
npx promptshop add financial-analystDetails
What This Skill Does
This skill provides a production-ready financial analysis toolkit for ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. It is designed for financial modeling, forecasting, budgeting, management reporting, and investment analysis. It is useful for finance professionals and business analysts.
When to Use
Perform ratio analysis on financial statementsBuild a DCF model for company valuationAnalyze budget variancesConstruct rolling forecastsGenerate executive summaries of financial performanceIdentify trends and benchmarks
Key Features
Calculates financial ratios across 5 categoriesBuilds DCF models with WACC and terminal valueConstructs budget variance analysesDevelops driver-based forecastsGenerates executive summariesValidates input data completeness
Manual Installation
Overview
Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis.
5-Phase Workflow
Phase 1: Scoping
Define analysis objectives and stakeholder requirements Identify data sources and time periods Establish materiality thresholds and accuracy targets Select appropriate analytical frameworks
Phase 2: Data Analysis & Modeling
Collect and validate financial data (income statement, balance sheet, cash flow) Validate input data completeness before running ratio calculations (check for missing fields, nulls, or implausible values) Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation) Build DCF models with WACC and terminal value calculations; cross-check DCF outputs against sanity bounds (e.g., implied multiples vs. comparables) Construct budget variance analyses with favorable/unfavorable classification Develop driver-based forecasts with scenario modeling
Phase 3: Insight Generation
Interpret ratio trends and benchmark against industry standards Identify material variances and root causes Assess valuation ranges through sensitivity analysis Evaluate forecast scenarios (base/bull/bear) for decision support
Phase 4: Reporting
Generate executive summaries with key findings Produce detailed variance reports by department and category Deliver DCF valuation reports with sensitivity tables Present rolling forecasts with trend analysis
Phase 5: Follow-up
Track forecast accuracy (target: +/-5% revenue, +/-3% expenses) Monitor report delivery timeliness (target: 100% on time) Update models with actuals as they become available Refine assumptions based on variance analysis
Tools
1. Ratio Calculator (scripts/ratio_calculator.py)
Calculate and interpret financial ratios from financial statement data.
Ratio Categories: Profitability: ROE, ROA, Gross Margin, Operating Margin, Net Margin Liquidity: Current Ratio, Quick Ratio, Cash Ratio Leverage: Debt-to-Equity, Interest Coverage, DSCR Efficiency: Asset Turnover, Inventory Turnover, Receivables Turnover, DSO Valuation: P/E, P/B, P/S, EV/EBITDA, PEG Ratio
python scripts/ratio_calculator.py sample_financial_data.json python scripts/ratio_calculator.py sample_financial_data.json --format json python scripts/ratio_calculator.py sample_financial_data.json --category profitability
2. DCF Valuation (scripts/dcf_valuation.py)
Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.
Features: WACC calculation via CAPM Revenue and free cash flow projections (5-year default) Terminal value via perpetuity growth and exit multiple methods Enterprise value and equity value derivation Two-way sensitivity analysis (discount rate vs growth rate)
python scripts/dcf_valuation.py valuation_data.json python scripts/dcf_valuation.py valuation_data.json --format json python scripts/dcf_valuation.py valuation_data.json --projection-years 7
3. Budget Variance Analyzer (scripts/budget_variance_analyzer.py)
Analyze actual vs budget vs prior year performance with materiality filtering.
Features: Dollar and percentage variance calculation Materiality threshold filtering (default: 10% or $50K) Favorable/unfavorable classification with revenue/expense logic Department and category breakdown Executive summary generation
python scripts/budget_variance_analyzer.py budget_data.json python scripts/budget_variance_analyzer.py budget_data.json --format json python scripts/budget_variance_analyzer.py budget_data.json --threshold-pct 5 --threshold-amt 25000
4. Forecast Builder (scripts/forecast_builder.py)
Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.
Features: Driver-based revenue forecast model 13-week rolling cash flow projection Scenario modeling (base/bull/bear cases) Trend analysis using simple linear regression (standard library)
python scripts/forecast_builder.py forecast_data.json python scripts/forecast_builder.py forecast_data.json --format json python scripts/forecast_builder.py forecast_data.json --scenarios base,bull,bear
Knowledge Bases
| Reference | Purpose |
|---|---|
| references/financial-ratios-guide.md | Ratio formulas, interpretation, industry benchmarks |
| references/valuation-methodology.md | DCF methodology, WACC, terminal value, comps |
| references/forecasting-best-practices.md | Driver-based forecasting, rolling forecasts, accuracy |
| references/industry-adaptations.md | Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare) |
Templates
| Template | Purpose |
|---|---|
| assets/variance_report_template.md | Budget variance report template |
| assets/dcf_analysis_template.md | DCF valuation analysis template |
| assets/forecast_report_template.md | Revenue forecast report template |
Key Metrics & Targets
| Metric | Target |
|---|---|
| Forecast accuracy (revenue) | +/-5% |
| Forecast accuracy (expenses) | +/-3% |
| Report delivery | 100% on time |
| Model documentation | Complete for all assumptions |
| Variance explanation | 100% of material variances |
Input Data Format
All scripts accept JSON input files. See assets/sample_financial_data.json for the complete input schema covering all four tools.
Dependencies
- None - All scripts use Python standard library only (math, statistics, json, argparse, datetime). No numpy, pandas, or scipy required.
- Financial Analyst Skill.
Overview
Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis.
5-Phase Workflow
Phase 1: Scoping
Define analysis objectives and stakeholder requirements Identify data sources and time periods Establish materiality thresholds and accuracy targets Select appropriate analytical frameworks
Phase 2: Data Analysis & Modeling
Collect and validate financial data (income statement, balance sheet, cash flow) Validate input data completeness before running ratio calculations (check for missing fields, nulls, or implausible values) Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation) Build DCF models with WACC and terminal value calculations; cross-check DCF outputs against sanity bounds (e.g., implied multiples vs. comparables) Construct budget variance analyses with favorable/unfavorable classification Develop driver-based forecasts with scenario modeling
Phase 3: Insight Generation
Interpret ratio trends and benchmark against industry standards Identify material variances and root causes Assess valuation ra