Financial Analyst Skill for Claude

Financial Analyst Skill

Overview

Production-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.

Use when

• The user asks to "run financial ratios", "build a DCF", "analyze budget variance", or "build a forecast"
• A valuation range is needed for an acquisition, fundraise, or board presentation
• Actuals vs budget needs investigation (which variances are material, favorable/unfavorable, department breakdown)
• A rolling 13-week cash flow or driver-based revenue forecast needs construction
• Sensitivity analysis is required to stress-test valuation or forecast assumptions
• The user asks about profitability, liquidity, leverage, efficiency, or valuation metrics with industry context

Clarify First

Before the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Which analysis — ratios, DCF valuation, budget variance, or forecast (selects the tool and the required input schema; each needs different data)
• [ ] The decision it supports — acquisition, fundraise, board review, or budget reforecast (sets the executive-summary conclusion and which metrics lead)
• [ ] Materiality threshold — absolute $ or % of budget (drives which variances surface vs. go to the appendix; default 10% / $50K)
• [ ] Key assumptions + as-of date — WACC inputs, terminal growth, or forecast drivers and when each was last sourced (drives DCF/forecast outputs and the assumptions appendix)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

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
• *Validate:* materiality threshold is explicit (absolute $ or %), accuracy target is a number, and the decision the analysis supports is named

Phase 2: Data Analysis & Modeling

• Collect and validate financial data (income statement, balance sheet, cash flow)
• Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
• Build DCF models with WACC and terminal value calculations
• Construct budget variance analyses with favorable/unfavorable classification
• Develop driver-based forecasts with scenario modeling
• *Validate:* input JSON conforms to the expected schema (no missing statements, no mixed periods); WACC inputs sourced within the last quarter; terminal growth rate ≤ long-run GDP growth

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
• *Validate:* every material variance has a root-cause hypothesis; DCF sensitivity range is wider than ±15% on WACC and terminal growth

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
• *Validate:* executive summary leads with the decision-relevant conclusion, not the method; assumptions appendix lists source + last-reviewed date for each

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 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

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 valuation_data.json
python scripts/dcf_valuation.py valuation_data.json --format json
python scripts/dcf_valuation.py valuation_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 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

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 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

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 |

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 |

Industry Adaptations

SaaS

• Key metrics: MRR, ARR, CAC, LTV, Churn Rate, Net Revenue Retention
• Revenue recognition: subscription-based, deferred revenue tracking
• Unit economics: CAC payback period, LTV/CAC ratio
• Cohort analysis for retention and expansion revenue

Retail

• Key metrics: Same-store sales, Revenue per square foot, Inventory turnover
• Seasonal adjustment factors in forecasting
• Gross margin analysis by product category
• Working capital cycle optimization

Manufacturing

• Key metrics: Gross margin by product line, Capacity utilization, COGS breakdown
• Bill of materials cost analysis
• Absorption vs variable costing impact
• Capital expenditure planning and ROI

Financial Services

• Key metrics: Net Interest Margin, Efficiency Ratio, ROA, Tier 1 Capital
• Regulatory capital requirements
• Credit loss provisioning and reserves
• Fee income analysis and diversification

Healthcare

• Key metrics: Revenue per patient, Payer mix, Days in A/R, Operating margin
• Reimbursement rate analysis by payer
• Case mix index impact on revenue
• Compliance cost allocation

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. See assets/sample_financial_data.json for the complete input schema covering all four tools.

Dependencies

None - All scripts use Python standard library only (math, statistics, json, argparse, datetime). No numpy, pandas, or scipy required.

Troubleshooting

| Problem | Cause | Solution |

|---------|-------|----------|

| All ratios return 0.00 | Missing or zeroed financial statement fields in input JSON | Verify income_statement, balance_sheet, and cash_flow keys are populated with non-zero values; check field names match expected schema |

| DCF yields negative equity value | Net debt exceeds enterprise value, or WACC is set lower than terminal growth rate | Confirm net_debt is accurate; ensure terminal_growth_rate < WACC (typically 2-3% vs 8-12%); review capital structure assumptions |

| Sensitivity table shows "N/A" across entire row | WACC value in that row is less than or equal to every terminal growth rate in the range | Widen the gap between WACC and terminal growth; raise WACC inputs or lower the growth range in assumptions.terminal_growth_rate |

| Budget variance analyzer flags every line as material | Materiality thresholds set too low relative to the data scale | Increase --threshold-pct (e.g., from 5 to 10) and --threshold-amt (e.g., from 25000 to 100000) to match organizational materiality policy |

| Forecast builder produces flat projections | Historical data has fewer than 2 periods, or revenue_growth_rate is set to 0 | Provide at least 3-4 historical periods in historical_periods; set a non-zero revenue_growth_rate in assumptions |

| JSON parsing error on script execution | Malformed JSON input file (trailing commas, unquoted keys, encoding issues) | Validate input with python -m json.tool input_file.json; ensure UTF-8 encoding; remove trailing commas and comments |

| Valuation ratios all show "Insufficient data" | Missing market_data section in input JSON (share price, shares outstanding) | Add the market_data object with share_price, shares_outstanding, and earnings_growth_rate fields to the input file |

Success Criteria

• Forecast Accuracy: Revenue forecasts land within +/-5% of actuals; expense forecasts within +/-3% over rolling 12-month periods
• Variance Coverage: 100% of material variances (exceeding threshold) include documented root-cause explanations and corrective action plans
• Valuation Confidence: DCF-derived equity value falls within 15% of comparable-company and precedent-transaction benchmarks, validated through sensitivity analysis
• Report Timeliness: All financial analysis deliverables (ratio reports, variance analyses, forecast updates) published within agreed SLA -- target 100% on-time delivery
• Model Integrity: Every assumption in DCF and forecast models is documented with source, rationale, and last-reviewed date; WACC inputs refresh quarterly against market data
• Stakeholder Adoption: Financial models and dashboards referenced in at least 80% of executive budget reviews, board presentations, and investment committee decisions
• Analytical Efficiency: End-to-end analysis cycle time (data collection through report delivery) reduced by 40%+ compared to manual spreadsheet workflows, measured per reporting period

Scope & Limitations

This skill covers:

• Quantitative financial ratio analysis across profitability, liquidity, leverage, efficiency, and valuation categories with built-in industry benchmarking
• Discounted Cash Flow (DCF) enterprise and equity valuation using CAPM-based WACC, perpetuity growth and exit multiple terminal value methods, and two-way sensitivity analysis
• Budget variance analysis with materiality filtering, favorable/unfavorable classification, department and category breakdowns, and executive summary generation
• Driver-based revenue forecasting with 13-week rolling cash flow projection, base/bull/bear scenario modeling, and linear regression trend analysis

This skill does NOT cover:

• Real-time market data feeds, live stock price retrieval, or automated data ingestion from ERP/accounting systems (all input is via static JSON files)
• Qualitative analysis such as management quality assessment, competitive moat evaluation, ESG scoring, or regulatory risk judgment
• Tax optimization, transfer pricing, multi-entity consolidation, or jurisdiction-specific accounting treatments (IFRS vs GAAP reconciliation)
• Monte Carlo simulation, options pricing (Black-Scholes), credit risk modeling, or any analysis requiring external libraries beyond the Python standard library

Anti-patterns

| Anti-pattern | Failure mode | Fix |

|--------------|--------------|-----|

| Building a DCF on a single-scenario forecast | False precision; one number presented as a target price | Always run base/bull/bear; present valuation as a range with sensitivity tables |

| Terminal growth rate ≥ long-run GDP growth | Valuation dominated by terminal value assuming perpetual above-economy growth | Cap terminal growth at 2-3% (long-run GDP proxy); if comps justify higher, flag explicitly |

| WACC inputs more than a quarter old | Rate environment moved; discount rate is wrong; valuation wrong | Refresh risk-free rate, ERP, and beta quarterly; document "last reviewed" date per input |

| Benchmarking ratios against a generic "industry average" | Peer set is wrong; conclusions are wrong | Use a specific comparable-company set (size, geography, business model) — see industry benchmarks in references/financial-ratios-guide.md |

| Reporting every variance instead of filtering by materiality | Stakeholders tune out; real issues buried | Apply a materiality threshold (absolute $ or % of budget); below threshold goes into an appendix, not the report |

| Favorable variance = "good"; unfavorable = "bad" | Misses revenue shortfalls masked by expense underspend; misses over-delivery hiding scope cuts | Always pair the classification with a root-cause note — direction alone is not insight |

| Mixing forecast periods (quarterly actuals against annual budget) | Variances that don't reconcile; trust collapses | Run the tools on matched periods only; if a period is partial, annotate and use period-adjusted comparisons |

| Treating model output as the answer | Model is a reasoning aid, not a decision | Lead the executive summary with the decision; put the model outputs in support |

Integration Points

| Related Skill | Domain | Integration Use Case |

|---------------|--------|---------------------|

| c-level-advisor/ceo-advisor | C-Level Advisory | Feed DCF valuation outputs and scenario comparisons into CEO strategic investment decisions and board-ready presentations |

| c-level-advisor/cto-advisor | C-Level Advisory | Provide technology investment ROI analysis and CapEx forecasts to support build-vs-buy and infrastructure scaling decisions |

| business-growth/revenue-operations | Business & Growth | Connect revenue forecasts and unit-economics metrics (CAC, LTV, payback period) to pipeline and go-to-market planning |

| product-team/product-manager | Product Team | Supply budget variance data and RICE-weighted financial projections for feature prioritization and resource allocation |

| data-analytics/data-analyst | Data Analytics | Export ratio analysis and forecast outputs as structured JSON for BI dashboard integration and trend visualization |

| project-management/project-financial-management | Project Management | Align budget variance analysis with project-level cost tracking, earned value management, and milestone-based funding releases |

Tool Reference

scripts/ratio_calculator.py

Calculate and interpret financial ratios across 5 categories with industry benchmarking.

usage: ratio_calculator.py [-h] [--format {text,json}]
                           [--category {profitability,liquidity,leverage,efficiency,valuation}]
                           input_file

positional arguments:
  input_file            Path to JSON file with financial statement data
                        (must contain income_statement, balance_sheet,
                        cash_flow, and optionally market_data objects)

options:
  -h, --help            Show help message and exit
  --format {text,json}  Output format (default: text)
  --category {profitability,liquidity,leverage,efficiency,valuation}
                        Calculate only a specific ratio category;
                        omit to calculate all 5 categories (20 ratios)

Ratios computed: ROE, ROA, Gross Margin, Operating Margin, Net Margin, Current Ratio, Quick Ratio, Cash Ratio, Debt-to-Equity, Interest Coverage, DSCR, Asset Turnover, Inventory Turnover, Receivables Turnover, DSO, P/E, P/B, P/S, EV/EBITDA, PEG Ratio.

scripts/dcf_valuation.py

Discounted Cash Flow enterprise and equity valuation with WACC calculation and sensitivity analysis.

usage: dcf_valuation.py [-h] [--format {text,json}]
                        [--projection-years PROJECTION_YEARS]
                        input_file

positional arguments:
  input_file            Path to JSON file with valuation data
                        (must contain historical and assumptions objects)

options:
  -h, --help            Show help message and exit
  --format {text,json}  Output format (default: text)
  --projection-years PROJECTION_YEARS
                        Number of projection years; overrides the value
                        in the input file (default: 5)

Outputs: WACC (CAPM), projected revenue and FCF, terminal value (perpetuity growth + exit multiple), enterprise value, equity value, value per share, and a two-way sensitivity table (WACC vs terminal growth rate).

scripts/budget_variance_analyzer.py

Analyze actual vs budget vs prior year performance with materiality filtering and executive summaries.

usage: budget_variance_analyzer.py [-h] [--format {text,json}]
                                   [--threshold-pct THRESHOLD_PCT]
                                   [--threshold-amt THRESHOLD_AMT]
                                   input_file

positional arguments:
  input_file            Path to JSON file with budget data
                        (must contain line_items array with actual,
                        budget, and optionally prior_year values)

options:
  -h, --help            Show help message and exit
  --format {text,json}  Output format (default: text)
  --threshold-pct THRESHOLD_PCT
                        Materiality threshold as percentage (default: 10.0)
  --threshold-amt THRESHOLD_AMT
                        Materiality threshold as dollar amount (default: 50000.0)

Outputs: Executive summary (revenue/expense/net impact), all variances with favorability classification, material variances filtered by threshold, department summary, and category summary.

scripts/forecast_builder.py

Driver-based revenue forecasting with rolling cash flow projection and multi-scenario modeling.

usage: forecast_builder.py [-h] [--format {text,json}]
                           [--scenarios SCENARIOS]
                           input_file

positional arguments:
  input_file            Path to JSON file with forecast data
                        (must contain historical_periods, drivers,
                        assumptions, cash_flow_inputs, and scenarios objects)

options:
  -h, --help            Show help message and exit
  --format {text,json}  Output format (default: text)
  --scenarios SCENARIOS
                        Comma-separated list of scenarios to model
                        (default: base,bull,bear)

Outputs: Trend analysis (linear regression, growth rates, seasonality index), scenario comparison table, per-period forecast detail (revenue, COGS, gross profit, OpEx, operating income), and 13-week rolling cash flow projection with runway calculation.