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From finance-skills
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.
npx claudepluginhub ciciliaeth/claude-skills --plugin finance-skillsHow this skill is triggered — by the user, by Claude, or both
Slash command
/finance-skills:financial-analystThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
assets/dcf_analysis_template.mdassets/expected_output.jsonassets/forecast_report_template.mdassets/sample_financial_data.jsonassets/variance_report_template.mdreferences/financial-ratios-guide.mdreferences/forecasting-best-practices.mdreferences/industry-adaptations.mdreferences/valuation-methodology.mdscripts/budget_variance_analyzer.pyscripts/dcf_valuation.pyscripts/forecast_builder.pyscripts/ratio_calculator.pyMandates invoking relevant skills via tools before any response in coding sessions. Covers access, priorities, and adaptations for Claude Code, Copilot CLI, Gemini CLI.
Share bugs, ideas, or general feedback.
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.
scripts/ratio_calculator.py)Calculate and interpret financial ratios from financial statement data.
Ratio Categories:
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
scripts/dcf_valuation.py)Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.
Features:
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
scripts/budget_variance_analyzer.py)Analyze actual vs budget vs prior year performance with materiality filtering.
Features:
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
scripts/forecast_builder.py)Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.
Features:
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
| 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) |
| 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 |
| 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 |
All scripts accept JSON input files. See assets/sample_financial_data.json for the complete input schema covering all four tools.
None - All scripts use Python standard library only (math, statistics, json, argparse, datetime). No numpy, pandas, or scipy required.