Agent-Skills.md

Agent Skills: Financial Analyst Skill

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[financial-analysisdcfbudgetingforecastingratios]
financeID: aaaaqwq/claude-code-skills/financial-analyst

Repository

aaaaqwq/claude-code-skills
aAAaqwqLicense: MIT
7317

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pnpm dlx add-skill https://github.com/aAAaqwq/AGI-Super-Team/tree/HEAD/skills/financial-analyst

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skills/financial-analyst/SKILL.md

Skill Metadata

Name
financial-analyst
Description
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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.

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)
  • 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

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 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

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.