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 constru...4license: MIT5---67# Financial Analyst Skill89## Overview1011Production-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.1213## 5-Phase Workflow1415### Phase 1: Scoping16- Define analysis objectives and stakeholder requirements17- Identify data sources and time periods18- Establish materiality thresholds and accuracy targets19- Select appropriate analytical frameworks2021### Phase 2: Data Analysis & Modeling22- Collect and validate financial data (income statement, balance sheet, cash flow)23- **Validate input data completeness** before running ratio calculations (check for missing fields, nulls, or implausible values)24- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)25- Build DCF models with WACC and terminal value calculations; **cross-check DCF outputs against sanity bounds** (e.g., implied multiples vs. comparables)26- Construct budget variance analyses with favorable/unfavorable classification27- Develop driver-based forecasts with scenario modeling2829### Phase 3: Insight Generation30- Interpret ratio trends and benchmark against industry standards31- Identify material variances and root causes32- Assess valuation ranges through sensitivity analysis33- Evaluate forecast scenarios (base/bull/bear) for decision support3435### Phase 4: Reporting36- Generate executive summaries with key findings37- Produce detailed variance reports by department and category38- Deliver DCF valuation reports with sensitivity tables39- Present rolling forecasts with trend analysis4041### Phase 5: Follow-up42- Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)43- Monitor report delivery timeliness (target: 100% on time)44- Update models with actuals as they become available45- Refine assumptions based on variance analysis4647## Tools4849### 1. Ratio Calculator (`scripts/ratio_calculator.py`)5051Calculate and interpret financial ratios from financial statement data.5253**Ratio Categories:**54- **Profitability:** ROE, ROA, Gross Margin, Operating Margin, Net Margin55- **Liquidity:** Current Ratio, Quick Ratio, Cash Ratio56- **Leverage:** Debt-to-Equity, Interest Coverage, DSCR57- **Efficiency:** Asset Turnover, Inventory Turnover, Receivables Turnover, DSO58- **Valuation:** P/E, P/B, P/S, EV/EBITDA, PEG Ratio5960```bash61python scripts/ratio_calculator.py assets/sample_financial_data.json62python scripts/ratio_calculator.py assets/sample_financial_data.json --format json63python scripts/ratio_calculator.py assets/sample_financial_data.json --category profitability64```6566### 2. DCF Valuation (`scripts/dcf_valuation.py`)6768Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.6970**Features:**71- WACC calculation via CAPM72- Revenue and free cash flow projections (5-year default)73- Terminal value via perpetuity growth and exit multiple methods74- Enterprise value and equity value derivation75- Two-way sensitivity analysis (discount rate vs growth rate)7677```bash78python scripts/dcf_valuation.py assets/sample_financial_data.json79python scripts/dcf_valuation.py assets/sample_financial_data.json --format json80python scripts/dcf_valuation.py assets/sample_financial_data.json --projection-years 781```8283### 3. Budget Variance Analyzer (`scripts/budget_variance_analyzer.py`)8485Analyze actual vs budget vs prior year performance with materiality filtering.8687**Features:**88- Dollar and percentage variance calculation89- Materiality threshold filtering (default: 10% or $50K)90- Favorable/unfavorable classification with revenue/expense logic91- Department and category breakdown92- Executive summary generation9394```bash95python scripts/budget_variance_analyzer.py assets/sample_financial_data.json96python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --format json97python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --threshold-pct 5 --threshold-amt 2500098```99100### 4. Forecast Builder (`scripts/forecast_builder.py`)101102Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.103104**Features:**105- Driver-based revenue forecast model106- 13-week rolling cash flow projection107- Scenario modeling (base/bull/bear cases)108- Trend analysis using simple linear regression (standard library)109110```bash111python scripts/forecast_builder.py assets/sample_financial_data.json112python scripts/forecast_builder.py assets/sample_financial_data.json --format json113python scripts/forecast_builder.py assets/sample_financial_data.json --scenarios base,bull,bear114```115116## Knowledge Bases117118| Reference | Purpose |119|-----------|---------|120| `references/financial-ratios-guide.md` | Ratio formulas, interpretation, industry benchmarks |121| `references/valuation-methodology.md` | DCF methodology, WACC, terminal value, comps |122| `references/forecasting-best-practices.md` | Driver-based forecasting, rolling forecasts, accuracy |123| `references/industry-adaptations.md` | Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare) |124125## Templates126127| Template | Purpose |128|----------|---------|129| `assets/variance_report_template.md` | Budget variance report template |130| `assets/dcf_analysis_template.md` | DCF valuation analysis template |131| `assets/forecast_report_template.md` | Revenue forecast report template |132133## Key Metrics & Targets134135| Metric | Target |136|--------|--------|137| Forecast accuracy (revenue) | +/-5% |138| Forecast accuracy (expenses) | +/-3% |139| Report delivery | 100% on time |140| Model documentation | Complete for all assumptions |141| Variance explanation | 100% of material variances |142143## Input Data Format144145All scripts accept JSON input files in either of two shapes:1461471. **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).1482. **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.149150Each script auto-detects the shape (flat keys win if present) and exits non-zero with a clear error if neither shape yields usable data.151152## Dependencies153154**None** - All scripts use Python standard library only (`math`, `statistics`, `json`, `argparse`, `datetime`). No numpy, pandas, or scipy required.
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