[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-e6aba429-3fdf-4eac-aeeb-81056c21c667":3,"$fUxbRUL0FYg2cAgH3q1_-x_tDiIIcF3fgStOZefVlnPs":43},{"id":4,"title":5,"description":6,"categoryId":7,"moduleId":8,"tags":9,"prompt":10,"icon":11,"source":12,"sourceUrl":13,"authorId":14,"authorName":15,"isPublic":16,"stars":17,"runs":18,"createdAt":19,"updatedAt":19,"module":20,"category":27,"packages":34},"e6aba429-3fdf-4eac-aeeb-81056c21c667","financial-analyst","执行财务比率分析、DCF估值、预算差异分析以及滚动预测构建，以支持战略决策。用于分析财务报表、构建估值模型、评估预算差异、构建财务预测和预测。也适用于用户提及财务建模、现金流分析、公司估值、财务预测或电子表格分析时。","cat_prod_data","mod_productivity","alirezarezvani,productivity","---\nname: \"financial-analyst\"\ndescription: 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.\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 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- **Validate input data completeness** before running ratio calculations (check for missing fields, nulls, or implausible values)\n- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)\n- Build DCF models with WACC and terminal value calculations; **cross-check DCF outputs against sanity bounds** (e.g., implied multiples vs. comparables)\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| `references\u002Findustry-adaptations.md` | Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare) |\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## 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","","imported","https:\u002F\u002Fgithub.com\u002Falirezarezvani\u002Fclaude-skills","user_system_seed","SkillOPIC",true,174,498,"2026-05-16 13:58:39",{"id":8,"name":21,"slug":22,"icon":23,"description":24,"sort":25,"createdAt":26},"效率工具","productivity","mdi-lightning-bolt-outline","文档处理、数据分析、自动化工作流",4,"2026-05-16 12:53:40",{"id":7,"name":28,"slug":29,"icon":30,"description":31,"moduleId":8,"sort":32,"skillCount":33,"createdAt":26},"数据分析","data-analysis","mdi-chart-bar","数据可视化、统计分析",2,30,[35],{"id":36,"skillId":4,"version":37,"fileName":38,"fileSize":39,"filePath":40,"fileHash":41,"manifest":42,"createdAt":19},"42abb271-ce3e-4327-938e-202b4a70d6fc","1.0.0","financial-analyst.zip",39887,"uploads\u002Fskills\u002Fe6aba429-3fdf-4eac-aeeb-81056c21c667\u002Ffinancial-analyst.zip","14cf4aa59d9bc8ec5134695f9dbd2eaec9d3da17be50797fdf4fdaa6f14a9112","[{\"path\":\"SKILL.md\",\"isDirectory\":false,\"size\":6404},{\"path\":\"assets\u002Fdcf_analysis_template.md\",\"isDirectory\":false,\"size\":5930},{\"path\":\"assets\u002Fexpected_output.json\",\"isDirectory\":false,\"size\":5259},{\"path\":\"assets\u002Fforecast_report_template.md\",\"isDirectory\":false,\"size\":5189},{\"path\":\"assets\u002Fsample_financial_data.json\",\"isDirectory\":false,\"size\":6437},{\"path\":\"assets\u002Fvariance_report_template.md\",\"isDirectory\":false,\"size\":3863},{\"path\":\"references\u002Ffinancial-ratios-guide.md\",\"isDirectory\":false,\"size\":9212},{\"path\":\"references\u002Fforecasting-best-practices.md\",\"isDirectory\":false,\"size\":10012},{\"path\":\"references\u002Findustry-adaptations.md\",\"isDirectory\":false,\"size\":2914},{\"path\":\"references\u002Fvaluation-methodology.md\",\"isDirectory\":false,\"size\":7922},{\"path\":\"scripts\u002Fbudget_variance_analyzer.py\",\"isDirectory\":false,\"size\":14574},{\"path\":\"scripts\u002Fdcf_valuation.py\",\"isDirectory\":false,\"size\":16803},{\"path\":\"scripts\u002Fforecast_builder.py\",\"isDirectory\":false,\"size\":19086},{\"path\":\"scripts\u002Fratio_calculator.py\",\"isDirectory\":false,\"size\":16068}]",{"code":44,"message":45,"data":46},200,"success",{"items":47,"stats":48,"page":51},[],{"averageRating":49,"totalRatings":49,"ratingCounts":50},0,[49,49,49,49,49],{"limit":52,"offset":49,"hasMore":53,"nextOffset":52,"ratedOnly":16},15,false]