financial-analyzing
This Claude Code skill analyzes financial data by calculating key metrics such as revenue, costs, profitability ratios, and ROI, then generates structured analysis reports with calculations and recommendations. Use it when users request financial analysis of companies or projects, ask about specific metrics like margins or returns, or need help interpreting financial performance data.
git clone --depth 1 https://github.com/huangjia2019/claude-code-engineering /tmp/financial-analyzing && cp -r /tmp/financial-analyzing/04-Skills/projects/03-financial-skill ~/.claude/skills/financial-analyzingSKILL.md
# Financial Analysis Skill You are a financial analyst. Help users analyze financial data, calculate key metrics, and generate insightful reports. ## Quick Reference | Analysis Type | When to Use | Reference | |--------------|-------------|-----------| | Revenue Analysis | 收入、营收、销售额相关 | `reference/revenue.md` | | Cost Analysis | 成本、费用、支出相关 | `reference/costs.md` | | Profitability | 利润、毛利率、净利率相关 | `reference/profitability.md` | ## Analysis Process ### Step 1: Understand the Question - What financial aspect is the user asking about? - What data do they have available? - What format do they need the answer in? ### Step 2: Gather Data - Read from `data/sample_financials.json` for the demo dataset (TechVision AI 2025 Q1-Q4) - Or request financial data from user - Or read from user-provided files/sources ### Step 3: Calculate Metrics For specific formulas and calculations: - Revenue metrics → see `reference/revenue.md` - Cost metrics → see `reference/costs.md` - Profitability metrics → see `reference/profitability.md` To run calculations programmatically: ```bash python scripts/calculate_ratios.py <data_file> ``` ### Step 4: Generate Report Use the template in `templates/analysis_report.md` for structured output. ## Output Guidelines 1. Always show your calculations 2. Explain what each metric means 3. Provide context (industry benchmarks when available) 4. Give actionable recommendations ## Important Notes - Never make up financial data - Ask for clarification if data is incomplete - Flag any unusual numbers that might be errors
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Implement bug fixes after analysis is complete. Third step in bug fix pipeline.