# Financial Analyst

> Financial Analyst Skill

- Skill: `neekware/financial-analyst-2` (Agent Skill, multi-file: 14 files)
- Install (CLI): `npx skillmds@latest add neekware/financial-analyst-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neekware/financial-analyst-2/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: neekware (https://skillmd.com/u/neekware)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/neekware/financial-analyst-2

---

# Financial 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.md`)

> **Note:** Bundled scripts ship as Markdown reference (`.md`) — copy the code out of the `.md` file to run it.

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

```bash
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.md`)

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)

```bash
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.md`)

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

```bash
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.md`)

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)

```bash
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                                                    |
| `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. 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.

> **Creator:** Finance
> **License:** MIT
> **Source Repo:** `neekware/dojo-skills`
> **Source Bucket:** `finance`
> **Original Path:** `finance/financial-analyst`