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 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 assets/sample_financial_data.json
python scripts/ratio_calculator.py assets/sample_financial_data.json --format json
python scripts/ratio_calculator.py assets/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 assets/sample_financial_data.json
python scripts/dcf_valuation.py assets/sample_financial_data.json --format json
python scripts/dcf_valuation.py assets/sample_financial_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 assets/sample_financial_data.json
python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --format json
python scripts/budget_variance_analyzer.py assets/sample_financial_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 assets/sample_financial_data.json
python scripts/forecast_builder.py assets/sample_financial_data.json --format json
python scripts/forecast_builder.py assets/sample_financial_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 in either of two shapes:
- Flat — the tool's expected keys at the top level (e.g.,
income_statement / balance_sheet for the ratio calculator, historical / assumptions for DCF, line_items for variance, historical_periods / drivers / assumptions / cash_flow_inputs for forecasting).
- Nested (bundled) — inputs for all four tools in one file, nested under per-tool keys:
ratio_analysis, dcf_valuation, budget_variance, forecast. See assets/sample_financial_data.json for the complete bundled schema; every quick-start command above runs directly against it.
Each script auto-detects the shape (flat keys win if present) and exits non-zero with a clear error if neither shape yields usable data.
Dependencies
None - All scripts use Python standard library only (math, statistics, json, argparse, datetime). No numpy, pandas, or scipy required.
1---2name: financial-analyst3description: Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.4---56# Financial Analyst Skill78## Overview910Production-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.1112## 5-Phase Workflow1314### Phase 1: Scoping15- Define analysis objectives and stakeholder requirements16- Identify data sources and time periods17- Establish materiality thresholds and accuracy targets18- Select appropriate analytical frameworks1920### Phase 2: Data Analysis & Modeling21- Collect and validate financial data (income statement, balance sheet, cash flow)22- **Validate input data completeness** before running ratio calculations (check for missing fields, nulls, or implausible values)23- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)24- Build DCF models with WACC and terminal value calculations; **cross-check DCF outputs against sanity bounds** (e.g., implied multiples vs. comparables)25- Construct budget variance analyses with favorable/unfavorable classification26- Develop driver-based forecasts with scenario modeling2728### Phase 3: Insight Generation29- Interpret ratio trends and benchmark against industry standards30- Identify material variances and root causes31- Assess valuation ranges through sensitivity analysis32- Evaluate forecast scenarios (base/bull/bear) for decision support3334### Phase 4: Reporting35- Generate executive summaries with key findings36- Produce detailed variance reports by department and category37- Deliver DCF valuation reports with sensitivity tables38- Present rolling forecasts with trend analysis3940### Phase 5: Follow-up41- Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)42- Monitor report delivery timeliness (target: 100% on time)43- Update models with actuals as they become available44- Refine assumptions based on variance analysis4546## Tools4748### 1. Ratio Calculator (`scripts/ratio_calculator.py`)4950Calculate and interpret financial ratios from financial statement data.5152**Ratio Categories:**53- **Profitability:** ROE, ROA, Gross Margin, Operating Margin, Net Margin54- **Liquidity:** Current Ratio, Quick Ratio, Cash Ratio55- **Leverage:** Debt-to-Equity, Interest Coverage, DSCR56- **Efficiency:** Asset Turnover, Inventory Turnover, Receivables Turnover, DSO57- **Valuation:** P/E, P/B, P/S, EV/EBITDA, PEG Ratio5859```bash60python scripts/ratio_calculator.py assets/sample_financial_data.json61python scripts/ratio_calculator.py assets/sample_financial_data.json --format json62python scripts/ratio_calculator.py assets/sample_financial_data.json --category profitability63```6465### 2. DCF Valuation (`scripts/dcf_valuation.py`)6667Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.6869**Features:**70- WACC calculation via CAPM71- Revenue and free cash flow projections (5-year default)72- Terminal value via perpetuity growth and exit multiple methods73- Enterprise value and equity value derivation74- Two-way sensitivity analysis (discount rate vs growth rate)7576```bash77python scripts/dcf_valuation.py assets/sample_financial_data.json78python scripts/dcf_valuation.py assets/sample_financial_data.json --format json79python scripts/dcf_valuation.py assets/sample_financial_data.json --projection-years 780```8182### 3. Budget Variance Analyzer (`scripts/budget_variance_analyzer.py`)8384Analyze actual vs budget vs prior year performance with materiality filtering.8586**Features:**87- Dollar and percentage variance calculation88- Materiality threshold filtering (default: 10% or $50K)89- Favorable/unfavorable classification with revenue/expense logic90- Department and category breakdown91- Executive summary generation9293```bash94python scripts/budget_variance_analyzer.py assets/sample_financial_data.json95python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --format json96python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --threshold-pct 5 --threshold-amt 2500097```9899### 4. Forecast Builder (`scripts/forecast_builder.py`)100101Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.102103**Features:**104- Driver-based revenue forecast model105- 13-week rolling cash flow projection106- Scenario modeling (base/bull/bear cases)107- Trend analysis using simple linear regression (standard library)108109```bash110python scripts/forecast_builder.py assets/sample_financial_data.json111python scripts/forecast_builder.py assets/sample_financial_data.json --format json112python scripts/forecast_builder.py assets/sample_financial_data.json --scenarios base,bull,bear113```114115## Knowledge Bases116117| Reference | Purpose |118|-----------|---------|119| `references/financial-ratios-guide.md` | Ratio formulas, interpretation, industry benchmarks |120| `references/valuation-methodology.md` | DCF methodology, WACC, terminal value, comps |121| `references/forecasting-best-practices.md` | Driver-based forecasting, rolling forecasts, accuracy |122| `references/industry-adaptations.md` | Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare) |123124## Templates125126| Template | Purpose |127|----------|---------|128| `assets/variance_report_template.md` | Budget variance report template |129| `assets/dcf_analysis_template.md` | DCF valuation analysis template |130| `assets/forecast_report_template.md` | Revenue forecast report template |131132## Key Metrics & Targets133134| Metric | Target |135|--------|--------|136| Forecast accuracy (revenue) | +/-5% |137| Forecast accuracy (expenses) | +/-3% |138| Report delivery | 100% on time |139| Model documentation | Complete for all assumptions |140| Variance explanation | 100% of material variances |141142## Input Data Format143144All scripts accept JSON input files in either of two shapes:1451461. **Flat** — the tool's expected keys at the top level (e.g., `income_statement` / `balance_sheet` for the ratio calculator, `historical` / `assumptions` for DCF, `line_items` for variance, `historical_periods` / `drivers` / `assumptions` / `cash_flow_inputs` for forecasting).1472. **Nested (bundled)** — inputs for all four tools in one file, nested under per-tool keys: `ratio_analysis`, `dcf_valuation`, `budget_variance`, `forecast`. See `assets/sample_financial_data.json` for the complete bundled schema; every quick-start command above runs directly against it.148149Each script auto-detects the shape (flat keys win if present) and exits non-zero with a clear error if neither shape yields usable data.150151## Dependencies152153**None** - All scripts use Python standard library only (`math`, `statistics`, `json`, `argparse`, `datetime`). No numpy, pandas, or scipy required.
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