[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6D6SERACK157M0N9EW2HYEY":3,"skill-same:01M6D6SERACK157M0N9EW2HYEY":117},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":46,"ownerDescription":47,"updatedAt":48,"install":49,"resources":62},"01M6D6SERACK157M0N9EW2HYEY","financial-analyst",null,"Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making","oabdelmaksoud\u002FSuperClaw","oabdelmaksoud","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F50699149?v=4","01M6D5CVVN778ZCDP1WHS816SP","Omar Abdelmaksoud","01M6D5CW1RHNSKCJR85GJACBFT",2,"MIT","https:\u002F\u002Fgithub.com\u002Foabdelmaksoud\u002FSuperClaw\u002Fblob\u002Fmain\u002Fupstream-import\u002Fclaude-skills\u002Freferences\u002Ffinance\u002Ffinancial-analyst\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000040","财务","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34],"financial analyst","analyst","valuation","forecast","variance analysis","Budget","analysis","decision","dcf","budget variance analysis","decision-making","2026-11-10 10:12:30",false,"---\nname: financial-analyst\ndescription: Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making\n---\n\n# Financial Analyst Skill\n\n## Overview\n\nProduction-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial analysts with 3-6 years experience performing financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis.\n\n## 5-Phase Workflow\n\n### Phase 1: Scoping\n- Define analysis objectives and stakeholder requirements\n- Identify data sources and time periods\n- Establish materiality thresholds and accuracy targets\n- Select appropriate analytical frameworks\n\n### Phase 2: Data Analysis & Modeling\n- Collect and validate financial data (income statement, balance sheet, cash flow)\n- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)\n- Build DCF models with WACC and terminal value calculations\n- Construct budget variance analyses with favorable\u002Funfavorable classification\n- Develop driver-based forecasts with scenario modeling\n\n### Phase 3: Insight Generation\n- Interpret ratio trends and benchmark against industry standards\n- Identify material variances and root causes\n- Assess valuation ranges through sensitivity analysis\n- Evaluate forecast scenarios (base\u002Fbull\u002Fbear) for decision support\n\n### Phase 4: Reporting\n- Generate executive summaries with key findings\n- Produce detailed variance reports by department and category\n- Deliver DCF valuation reports with sensitivity tables\n- Present rolling forecasts with trend analysis\n\n### Phase 5: Follow-up\n- Track forecast accuracy (target: +\u002F-5% revenue, +\u002F-3% expenses)\n- Monitor report delivery timeliness (target: 100% on time)\n- Update models with actuals as they become available\n- Refine assumptions based on variance analysis\n\n## Tools\n\n### 1. Ratio Calculator (`scripts\u002Fratio_calculator.py`)\n\nCalculate and interpret financial ratios from financial statement data.\n\n**Ratio Categories:**\n- **Profitability:** ROE, ROA, Gross Margin, Operating Margin, Net Margin\n- **Liquidity:** Current Ratio, Quick Ratio, Cash Ratio\n- **Leverage:** Debt-to-Equity, Interest Coverage, DSCR\n- **Efficiency:** Asset Turnover, Inventory Turnover, Receivables Turnover, DSO\n- **Valuation:** P\u002FE, P\u002FB, P\u002FS, EV\u002FEBITDA, PEG Ratio\n\n```bash\npython scripts\u002Fratio_calculator.py sample_financial_data.json\npython scripts\u002Fratio_calculator.py sample_financial_data.json --format json\npython scripts\u002Fratio_calculator.py sample_financial_data.json --category profitability\n```\n\n### 2. DCF Valuation (`scripts\u002Fdcf_valuation.py`)\n\nDiscounted Cash Flow enterprise and equity valuation with sensitivity analysis.\n\n**Features:**\n- WACC calculation via CAPM\n- Revenue and free cash flow projections (5-year default)\n- Terminal value via perpetuity growth and exit multiple methods\n- Enterprise value and equity value derivation\n- Two-way sensitivity analysis (discount rate vs growth rate)\n\n```bash\npython scripts\u002Fdcf_valuation.py valuation_data.json\npython scripts\u002Fdcf_valuation.py valuation_data.json --format json\npython scripts\u002Fdcf_valuation.py valuation_data.json --projection-years 7\n```\n\n### 3. Budget Variance Analyzer (`scripts\u002Fbudget_variance_analyzer.py`)\n\nAnalyze actual vs budget vs prior year performance with materiality filtering.\n\n**Features:**\n- Dollar and percentage variance calculation\n- Materiality threshold filtering (default: 10% or $50K)\n- Favorable\u002Funfavorable classification with revenue\u002Fexpense logic\n- Department and category breakdown\n- Executive summary generation\n\n```bash\npython scripts\u002Fbudget_variance_analyzer.py budget_data.json\npython scripts\u002Fbudget_variance_analyzer.py budget_data.json --format json\npython scripts\u002Fbudget_variance_analyzer.py budget_data.json --threshold-pct 5 --threshold-amt 25000\n```\n\n### 4. Forecast Builder (`scripts\u002Fforecast_builder.py`)\n\nDriver-based revenue forecasting with rolling cash flow projection and scenario modeling.\n\n**Features:**\n- Driver-based revenue forecast model\n- 13-week rolling cash flow projection\n- Scenario modeling (base\u002Fbull\u002Fbear cases)\n- Trend analysis using simple linear regression (standard library)\n\n```bash\npython scripts\u002Fforecast_builder.py forecast_data.json\npython scripts\u002Fforecast_builder.py forecast_data.json --format json\npython scripts\u002Fforecast_builder.py forecast_data.json --scenarios base,bull,bear\n```\n\n## Knowledge Bases\n\n| Reference | Purpose |\n|-----------|---------|\n| `references\u002Ffinancial-ratios-guide.md` | Ratio formulas, interpretation, industry benchmarks |\n| `references\u002Fvaluation-methodology.md` | DCF methodology, WACC, terminal value, comps |\n| `references\u002Fforecasting-best-practices.md` | Driver-based forecasting, rolling forecasts, accuracy |\n\n## Templates\n\n| Template | Purpose |\n|----------|---------|\n| `assets\u002Fvariance_report_template.md` | Budget variance report template |\n| `assets\u002Fdcf_analysis_template.md` | DCF valuation analysis template |\n| `assets\u002Fforecast_report_template.md` | Revenue forecast report template |\n\n## Industry Adaptations\n\n### SaaS\n- Key metrics: MRR, ARR, CAC, LTV, Churn Rate, Net Revenue Retention\n- Revenue recognition: subscription-based, deferred revenue tracking\n- Unit economics: CAC payback period, LTV\u002FCAC ratio\n- Cohort analysis for retention and expansion revenue\n\n### Retail\n- Key metrics: Same-store sales, Revenue per square foot, Inventory turnover\n- Seasonal adjustment factors in forecasting\n- Gross margin analysis by product category\n- Working capital cycle optimization\n\n### Manufacturing\n- Key metrics: Gross margin by product line, Capacity utilization, COGS breakdown\n- Bill of materials cost analysis\n- Absorption vs variable costing impact\n- Capital expenditure planning and ROI\n\n### Financial Services\n- Key metrics: Net Interest Margin, Efficiency Ratio, ROA, Tier 1 Capital\n- Regulatory capital requirements\n- Credit loss provisioning and reserves\n- Fee income analysis and diversification\n\n### Healthcare\n- Key metrics: Revenue per patient, Payer mix, Days in A\u002FR, Operating margin\n- Reimbursement rate analysis by payer\n- Case mix index impact on revenue\n- Compliance cost allocation\n\n## Key Metrics & Targets\n\n| Metric | Target |\n|--------|--------|\n| Forecast accuracy (revenue) | +\u002F-5% |\n| Forecast accuracy (expenses) | +\u002F-3% |\n| Report delivery | 100% on time |\n| Model documentation | Complete for all assumptions |\n| Variance explanation | 100% of material variances |\n\n## Input Data Format\n\nAll scripts accept JSON input files. See `assets\u002Fsample_financial_data.json` for the complete input schema covering all four tools.\n\n## Dependencies\n\n**None** - All scripts use Python standard library only (`math`, `statistics`, `json`, `argparse`, `datetime`). No numpy, pandas, or scipy required.\n","# Financial Analyst Skill\n\n## Overview\n\nProduction-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial analysts with 3-6 years experience performing financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis.\n\n## 5-Phase Workflow\n\n### Phase 1: Scoping\n- Define analysis objectives and stakeholder requirements\n- Identify data sources and time periods\n- Establish materiality thresholds and accuracy targets\n- Select appropriate analytical frameworks\n\n### Phase 2: Data Analysis & Modeling\n- Collect and validate financial data (income statement, balance sheet, cash flow)\n- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)\n- Build DCF models with WACC and terminal value calculations\n- Construct budget variance analyses with favorable\u002Funfavorable classification\n- Develop driver-based forecasts with scenario modeling\n\n### Phase 3: Insight Generation\n- Interpret ratio trends and benchmark against industry standards\n- Identify material variances and root causes\n- Assess valuation ranges through sensitivity analysis\n- Evaluate forecast scenarios (base\u002Fbull\u002Fbear) for decision support\n\n### Phase 4: Reporting\n- Generate executive summaries with key findings\n- Produce detailed variance reports by department and category\n- Deliver DCF valuation reports with sensitivity tables\n- Present rolling forecasts with trend analysis\n\n### Phase 5: Follow-up\n- Track forecast accuracy (target: +\u002F-5% revenue, +\u002F-3% expenses)\n- Monitor report delivery timeliness (target: 100% on time)\n- Update models with actuals as they become available\n- Refine assumptions based on variance analysis\n\n## Tools\n\n### 1. Ratio Calculator (`scripts\u002Fratio_calculator.py`)\n\nCalculate and interpret financial ratios from financial statement data.\n\n**Ratio Categories:**\n- **Profitability:** ROE, ROA, Gross Margin, Operating Margin, Net Margin\n- **Liquidity:** Current Ratio, Quick Ratio, Cash Ratio\n- **Leverage:** Debt-to-Equity, Interest Coverage, DSCR\n- **Efficiency:** Asset Turnover, Inventory Turnover, Receivables Turnover, DSO\n- **Valuation:** P\u002FE, P\u002FB, P\u002FS, EV\u002FEBITDA, PEG Ratio\n\n```bash\npython scripts\u002Fratio_calculator.py sample_financial_data.json\npython scripts\u002Fratio_calculator.py sample_financial_data.json --format json\npython scripts\u002Fratio_calculator.py sample_financial_data.json --category profitability\n```\n\n### 2. DCF Valuation (`scripts\u002Fdcf_valuation.py`)\n\nDiscounted Cash Flow enterprise and equity valuation with sensitivity analysis.\n\n**Features:**\n- WACC calculation via CAPM\n- Revenue and free cash flow projections (5-year default)\n- Terminal value via perpetuity growth and exit multiple methods\n- Enterprise value and equity value derivation\n- Two-way sensitivity analysis (discount rate vs growth rate)\n\n```bash\npython scripts\u002Fdcf_valuation.py valuation_data.json\npython scripts\u002Fdcf_valuation.py valuation_data.json --format json\npython scripts\u002Fdcf_valuation.py valuation_data.json --projection-years 7\n```\n\n### 3. Budget Variance Analyzer (`scripts\u002Fbudget_variance_analyzer.py`)\n\nAnalyze actual vs budget vs prior year performance with materiality filtering.\n\n**Features:**\n- Dollar and percentage variance calculation\n- Materiality threshold filtering (default: 10% or $50K)\n- Favorable\u002Funfavorable classification with revenue\u002Fexpense logic\n- Department and category breakdown\n- Executive summary generation\n\n```bash\npython scripts\u002Fbudget_variance_analyzer.py budget_data.json\npython scripts\u002Fbudget_variance_analyzer.py budget_data.json --format json\npython scripts\u002Fbudget_variance_analyzer.py budget_data.json --threshold-pct 5 --threshold-amt 25000\n```\n\n### 4. Forecast Builder (`scripts\u002Fforecast_builder.py`)\n\nDriver-based revenue forecasting with rolling cash flow projection and scenario modeling.\n\n**Features:**\n- Driver-based revenue forecast model\n- 13-week rolling cash flow projection\n- Scenario modeling (base\u002Fbull\u002Fbear cases)\n- Trend analysis using simple linear regression (standard library)\n\n```bash\npython scripts\u002Fforecast_builder.py forecast_data.json\npython scripts\u002Fforecast_builder.py forecast_data.json --format json\npython scripts\u002Fforecast_builder.py forecast_data.json --scenarios base,bull,bear\n```\n\n## Knowledge Bases\n\n| Reference | Purpose |\n|-----------|---------|\n| `references\u002Ffinancial-ratios-guide.md` | Ratio formulas, interpretation, industry benchmarks |\n| `references\u002Fvaluation-methodology.md` | DCF methodology, WACC, terminal value, comps |\n| `references\u002Fforecasting-best-practices.md` | Driver-based forecasting, rolling forecasts, accuracy |\n\n## Templates\n\n| Template | Purpose |\n|----------|---------|\n| `assets\u002Fvariance_report_template.md` | Budget variance report template |\n| `assets\u002Fdcf_analysis_template.md` | DCF valuation analysis template |\n| `assets\u002Fforecast_report_template.md` | Revenue forecast report template |\n\n## Industry Adaptations\n\n### SaaS\n- Key metrics: MRR, ARR, CAC, LTV, Churn Rate, Net Revenue Retention\n- Revenue recognition: subscription-based, deferred revenue tracking\n- Unit economics: CAC payback period, LTV\u002FCAC ratio\n- Cohort analysis for retention and expansion revenue\n\n### Retail\n- Key metrics: Same-store sales, Revenue per square foot, Inventory turnover\n- Seasonal adjustment factors in forecasting\n- Gross margin analysis by product category\n- Working capital cycle optimization\n\n### Manufacturing\n- Key metrics: Gross margin by product line, Capacity utilization, COGS breakdown\n- Bill of materials cost analysis\n- Absorption vs variable costing impact\n- Capital expenditure planning and ROI\n\n### Financial Services\n- Key metrics: Net Interest Margin, Efficiency Ratio, ROA, Tier 1 Capital\n- Regulatory capital requirements\n- Credit loss provisioning and reserves\n- Fee income analysis and diversification\n\n### Healthcare\n- Key metrics: Revenue per patient, Payer mix, Days in A\u002FR, Operating margin\n- Reimbursement rate analysis by payer\n- Case mix index impact on revenue\n- Compliance cost allocation\n\n## Key Metrics & Targets\n\n| Metric | Target |\n|--------|--------|\n| Forecast accuracy (revenue) | +\u002F-5% |\n| Forecast accuracy (expenses) | +\u002F-3% |\n| Report delivery | 100% on time |\n| Model documentation | Complete for all assumptions |\n| Variance explanation | 100% of material variances |\n\n## Input Data Format\n\nAll scripts accept JSON input files. 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