Financial Analyst Skill
Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making.
How to use it
Claude Code
- Run the line below. It pulls the whole folder into
~/.claude/skills/financial-analyst. - Describe your job in plain words. Claude Code follows the skill from there.
Claude Code — installs the whole folder, not just SKILL.md
npx degit alirezarezvani/claude-skills/finance/skills/financial-analyst#main ~/.claude/skills/financial-analystFor one project only, change the path to .claude/skills/financial-analyst.
Claude (web or desktop app)
- On this page open ⋯ → Download .md.
- Save it as SKILL.md in a folder, zip the folder, then Customize → Skills → + → Create skill → Upload a skill.
- Pick the file and Save. Claude shows the name and description and runs a security scan.
- Check the skill is switched on.
- Start a new chat and describe your job in plain words. The AI follows the skill from there.
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- ChatGPT: make a Project and paste it into Instructions.
- Neither? Paste it at the top of a new chat — it works for that chat.
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Source of Financial Analyst Skill
Show the full text154 lines
| name | description |
|---|---|
| financial-analyst | 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. |
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_sheetfor the ratio calculator,historical/assumptionsfor DCF,line_itemsfor variance,historical_periods/drivers/assumptions/cash_flow_inputsfor forecasting). - Nested (bundled) — inputs for all four tools in one file, nested under per-tool keys:
ratio_analysis,dcf_valuation,budget_variance,forecast. Seeassets/sample_financial_data.jsonfor 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 | |
| 2 | name "financial-analyst" |
| 3 | description 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 | |
| 5 | |
| 6 | # Financial Analyst Skill |
| 7 | |
| 8 | ## Overview |
| 9 | |
| 10 | 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. |
| 11 | |
| 12 | ## 5-Phase Workflow |
| 13 | |
| 14 | ### Phase 1: Scoping |
| 15 | Define analysis objectives and stakeholder requirements |
| 16 | Identify data sources and time periods |
| 17 | Establish materiality thresholds and accuracy targets |
| 18 | Select appropriate analytical frameworks |
| 19 | |
| 20 | ### Phase 2: Data Analysis & Modeling |
| 21 | 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 classification |
| 26 | Develop driver-based forecasts with scenario modeling |
| 27 | |
| 28 | ### Phase 3: Insight Generation |
| 29 | Interpret ratio trends and benchmark against industry standards |
| 30 | Identify material variances and root causes |
| 31 | Assess valuation ranges through sensitivity analysis |
| 32 | Evaluate forecast scenarios (base/bull/bear) for decision support |
| 33 | |
| 34 | ### Phase 4: Reporting |
| 35 | Generate executive summaries with key findings |
| 36 | Produce detailed variance reports by department and category |
| 37 | Deliver DCF valuation reports with sensitivity tables |
| 38 | Present rolling forecasts with trend analysis |
| 39 | |
| 40 | ### Phase 5: Follow-up |
| 41 | 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 available |
| 44 | Refine assumptions based on variance analysis |
| 45 | |
| 46 | ## Tools |
| 47 | |
| 48 | ### 1. Ratio Calculator (`scripts/ratio_calculator.py`) |
| 49 | |
| 50 | Calculate and interpret financial ratios from financial statement data. |
| 51 | |
| 52 | **Ratio Categories:** |
| 53 | **Profitability:** ROE, ROA, Gross Margin, Operating Margin, Net Margin |
| 54 | **Liquidity:** Current Ratio, Quick Ratio, Cash Ratio |
| 55 | **Leverage:** Debt-to-Equity, Interest Coverage, DSCR |
| 56 | **Efficiency:** Asset Turnover, Inventory Turnover, Receivables Turnover, DSO |
| 57 | **Valuation:** P/E, P/B, P/S, EV/EBITDA, PEG Ratio |
| 58 | |
| 59 | |
| 60 | python scripts/ratio_calculator.py assets/sample_financial_data.json |
| 61 | python scripts/ratio_calculator.py assets/sample_financial_data.json --format json |
| 62 | python scripts/ratio_calculator.py assets/sample_financial_data.json --category profitability |
| 63 | |
| 64 | |
| 65 | ### 2. DCF Valuation (`scripts/dcf_valuation.py`) |
| 66 | |
| 67 | Discounted Cash Flow enterprise and equity valuation with sensitivity analysis. |
| 68 | |
| 69 | **Features:** |
| 70 | WACC calculation via CAPM |
| 71 | Revenue and free cash flow projections (5-year default) |
| 72 | Terminal value via perpetuity growth and exit multiple methods |
| 73 | Enterprise value and equity value derivation |
| 74 | Two-way sensitivity analysis (discount rate vs growth rate) |
| 75 | |
| 76 | |
| 77 | python scripts/dcf_valuation.py assets/sample_financial_data.json |
| 78 | python scripts/dcf_valuation.py assets/sample_financial_data.json --format json |
| 79 | python scripts/dcf_valuation.py assets/sample_financial_data.json --projection-years 7 |
| 80 | |
| 81 | |
| 82 | ### 3. Budget Variance Analyzer (`scripts/budget_variance_analyzer.py`) |
| 83 | |
| 84 | Analyze actual vs budget vs prior year performance with materiality filtering. |
| 85 | |
| 86 | **Features:** |
| 87 | Dollar and percentage variance calculation |
| 88 | Materiality threshold filtering (default: 10% or $50K) |
| 89 | Favorable/unfavorable classification with revenue/expense logic |
| 90 | Department and category breakdown |
| 91 | Executive summary generation |
| 92 | |
| 93 | |
| 94 | python scripts/budget_variance_analyzer.py assets/sample_financial_data.json |
| 95 | python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --format json |
| 96 | python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --threshold-pct 5 --threshold-amt 25000 |
| 97 | |
| 98 | |
| 99 | ### 4. Forecast Builder (`scripts/forecast_builder.py`) |
| 100 | |
| 101 | Driver-based revenue forecasting with rolling cash flow projection and scenario modeling. |
| 102 | |
| 103 | **Features:** |
| 104 | Driver-based revenue forecast model |
| 105 | 13-week rolling cash flow projection |
| 106 | Scenario modeling (base/bull/bear cases) |
| 107 | Trend analysis using simple linear regression (standard library) |
| 108 | |
| 109 | |
| 110 | python scripts/forecast_builder.py assets/sample_financial_data.json |
| 111 | python scripts/forecast_builder.py assets/sample_financial_data.json --format json |
| 112 | python scripts/forecast_builder.py assets/sample_financial_data.json --scenarios base,bull,bear |
| 113 | |
| 114 | |
| 115 | ## Knowledge Bases |
| 116 | |
| 117 | | 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) | |
| 123 | |
| 124 | ## Templates |
| 125 | |
| 126 | | 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 | |
| 131 | |
| 132 | ## Key Metrics & Targets |
| 133 | |
| 134 | | 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 | |
| 141 | |
| 142 | ## Input Data Format |
| 143 | |
| 144 | All scripts accept JSON input files in either of two shapes: |
| 145 | |
| 146 | **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). |
| 147 | **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. |
| 148 | |
| 149 | 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. |
| 150 | |
| 151 | ## Dependencies |
| 152 | |
| 153 | **None** - All scripts use Python standard library only (`math`, `statistics`, `json`, `argparse`, `datetime`). No numpy, pandas, or scipy required. |
| 154 |
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