Financial Analyst
FreeComprehensive toolkit for financial analysis and forecasting.
Free · Opens the source repo
What Financial Analyst does
The Financial Analyst skill is a production-ready toolkit designed to assist users in performing complex financial analyses, including ratio analysis, discounted cash flow (DCF) valuation, budget variance analysis, and rolling forecast construction. This skill is particularly useful for financial professionals and analysts who need to evaluate financial statements, build valuation models, and assess budget variances to support strategic decision-making. With a focus on accuracy and detail, the skill provides a structured approach to financial modeling and forecasting.
Users can leverage this skill through a five-phase workflow that encompasses scoping, data analysis, insight generation, reporting, and follow-up. Each phase is designed to ensure that financial data is meticulously analyzed and interpreted, allowing for informed decision-making. The skill includes a variety of tools, such as a ratio calculator that covers multiple financial categories, a DCF valuation script that incorporates sensitivity analysis, and a budget variance analyzer that highlights material variances in performance. Additionally, the forecast builder enables users to create driver-based revenue forecasts with scenario modeling, enhancing the ability to project future financial performance.
The skill is equipped with essential templates and reference materials that guide users through the financial analysis process. Templates for variance reports, DCF analyses, and forecast reports streamline documentation, while reference guides on financial ratios, valuation methodologies, and industry adaptations provide crucial context and benchmarks. This makes the Financial Analyst skill suitable for finance professionals, analysts, and anyone involved in financial planning and analysis who seeks to enhance their analytical capabilities and improve the accuracy of their financial projections.
When to use it
Use this skill when you need to analyze financial statements, build valuation models, or assess budget variances for strategic decision-making.
When not to use it
This skill may not be suitable for casual users or those without a foundational understanding of financial concepts, as it requires familiarity with financial data and analysis techniques.
What you can build with it
Financial Statement Analysis
Use the ratio calculator to analyze profitability, liquidity, and leverage ratios from financial statements.
Valuation Modeling
Leverage the DCF valuation script to derive enterprise and equity values with sensitivity analysis.
Budget Performance Review
Utilize the budget variance analyzer to compare actual performance against budgets and prior year results.
How to install Financial Analyst
View source1. Install with the skills CLI
npx skills add alirezarezvani/claude-skills/financial-analyst --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by alirezarezvaniFinancial 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.
Frequently asked questions about Financial Analyst
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