Financial Analyst Skill
When to Use
Use this skill when the user wants to:
- run ratio analysis or valuation work
- build or review a DCF
- analyze budget variance or forecast performance
- turn financial statement data into executive insights
Usage
Recommended flow:
scope analysis goal
-> collect and validate inputs
-> run the appropriate model
-> interpret outputs
-> present insights and follow-up actions
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.
1---2name: financial-analyst3description: Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making4---5
6# Financial Analyst Skill
7
8## When to Use
9
10Use this skill when the user wants to:
11
12- run ratio analysis or valuation work
13- build or review a DCF
14- analyze budget variance or forecast performance
15- turn financial statement data into executive insights
16
17## Usage
18
19Recommended flow:
20
21```text
22scope analysis goal
23-> collect and validate inputs
24-> run the appropriate model
25-> interpret outputs
26-> present insights and follow-up actions
27```
28
29## Overview
30
31Production-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.
32
33## 5-Phase Workflow
34
35### Phase 1: Scoping
36- Define analysis objectives and stakeholder requirements
37- Identify data sources and time periods
38- Establish materiality thresholds and accuracy targets
39- Select appropriate analytical frameworks
40
41### Phase 2: Data Analysis & Modeling
42- Collect and validate financial data (income statement, balance sheet, cash flow)
43- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
44- Build DCF models with WACC and terminal value calculations
45- Construct budget variance analyses with favorable/unfavorable classification
46- Develop driver-based forecasts with scenario modeling
47
48### Phase 3: Insight Generation
49- Interpret ratio trends and benchmark against industry standards
50- Identify material variances and root causes
51- Assess valuation ranges through sensitivity analysis
52- Evaluate forecast scenarios (base/bull/bear) for decision support
53
54### Phase 4: Reporting
55- Generate executive summaries with key findings
56- Produce detailed variance reports by department and category
57- Deliver DCF valuation reports with sensitivity tables
58- Present rolling forecasts with trend analysis
59
60### Phase 5: Follow-up
61- Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)
62- Monitor report delivery timeliness (target: 100% on time)
63- Update models with actuals as they become available
64- Refine assumptions based on variance analysis
65
66## Tools
67
68### 1. Ratio Calculator (`scripts/ratio_calculator.py`)
69
70Calculate and interpret financial ratios from financial statement data.
71
72**Ratio Categories:**
73- **Profitability:** ROE, ROA, Gross Margin, Operating Margin, Net Margin
74- **Liquidity:** Current Ratio, Quick Ratio, Cash Ratio
75- **Leverage:** Debt-to-Equity, Interest Coverage, DSCR
76- **Efficiency:** Asset Turnover, Inventory Turnover, Receivables Turnover, DSO
77- **Valuation:** P/E, P/B, P/S, EV/EBITDA, PEG Ratio
78
79```bash
80python scripts/ratio_calculator.py sample_financial_data.json
81python scripts/ratio_calculator.py sample_financial_data.json --format json
82python scripts/ratio_calculator.py sample_financial_data.json --category profitability
83```
84
85### 2. DCF Valuation (`scripts/dcf_valuation.py`)
86
87Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.
88
89**Features:**
90- WACC calculation via CAPM
91- Revenue and free cash flow projections (5-year default)
92- Terminal value via perpetuity growth and exit multiple methods
93- Enterprise value and equity value derivation
94- Two-way sensitivity analysis (discount rate vs growth rate)
95
96```bash
97python scripts/dcf_valuation.py valuation_data.json
98python scripts/dcf_valuation.py valuation_data.json --format json
99python scripts/dcf_valuation.py valuation_data.json --projection-years 7
100```
101
102### 3. Budget Variance Analyzer (`scripts/budget_variance_analyzer.py`)
103
104Analyze actual vs budget vs prior year performance with materiality filtering.
105
106**Features:**
107- Dollar and percentage variance calculation
108- Materiality threshold filtering (default: 10% or $50K)
109- Favorable/unfavorable classification with revenue/expense logic
110- Department and category breakdown
111- Executive summary generation
112
113```bash
114python scripts/budget_variance_analyzer.py budget_data.json
115python scripts/budget_variance_analyzer.py budget_data.json --format json
116python scripts/budget_variance_analyzer.py budget_data.json --threshold-pct 5 --threshold-amt 25000
117```
118
119### 4. Forecast Builder (`scripts/forecast_builder.py`)
120
121Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.
122
123**Features:**
124- Driver-based revenue forecast model
125- 13-week rolling cash flow projection
126- Scenario modeling (base/bull/bear cases)
127- Trend analysis using simple linear regression (standard library)
128
129```bash
130python scripts/forecast_builder.py forecast_data.json
131python scripts/forecast_builder.py forecast_data.json --format json
132python scripts/forecast_builder.py forecast_data.json --scenarios base,bull,bear
133```
134
135## Knowledge Bases
136
137| Reference | Purpose |
138|-----------|---------|
139| `references/financial-ratios-guide.md` | Ratio formulas, interpretation, industry benchmarks |
140| `references/valuation-methodology.md` | DCF methodology, WACC, terminal value, comps |
141| `references/forecasting-best-practices.md` | Driver-based forecasting, rolling forecasts, accuracy |
142
143## Templates
144
145| Template | Purpose |
146|----------|---------|
147| `assets/variance_report_template.md` | Budget variance report template |
148| `assets/dcf_analysis_template.md` | DCF valuation analysis template |
149| `assets/forecast_report_template.md` | Revenue forecast report template |
150
151## Industry Adaptations
152
153### SaaS
154- Key metrics: MRR, ARR, CAC, LTV, Churn Rate, Net Revenue Retention
155- Revenue recognition: subscription-based, deferred revenue tracking
156- Unit economics: CAC payback period, LTV/CAC ratio
157- Cohort analysis for retention and expansion revenue
158
159### Retail
160- Key metrics: Same-store sales, Revenue per square foot, Inventory turnover
161- Seasonal adjustment factors in forecasting
162- Gross margin analysis by product category
163- Working capital cycle optimization
164
165### Manufacturing
166- Key metrics: Gross margin by product line, Capacity utilization, COGS breakdown
167- Bill of materials cost analysis
168- Absorption vs variable costing impact
169- Capital expenditure planning and ROI
170
171### Financial Services
172- Key metrics: Net Interest Margin, Efficiency Ratio, ROA, Tier 1 Capital
173- Regulatory capital requirements
174- Credit loss provisioning and reserves
175- Fee income analysis and diversification
176
177### Healthcare
178- Key metrics: Revenue per patient, Payer mix, Days in A/R, Operating margin
179- Reimbursement rate analysis by payer
180- Case mix index impact on revenue
181- Compliance cost allocation
182
183## Key Metrics & Targets
184
185| Metric | Target |
186|--------|--------|
187| Forecast accuracy (revenue) | +/-5% |
188| Forecast accuracy (expenses) | +/-3% |
189| Report delivery | 100% on time |
190| Model documentation | Complete for all assumptions |
191| Variance explanation | 100% of material variances |
192
193## Input Data Format
194
195All scripts accept JSON input files. See `assets/sample_financial_data.json` for the complete input schema covering all four tools.
196
197## Dependencies
198
199**None** - All scripts use Python standard library only (`math`, `statistics`, `json`, `argparse`, `datetime`). No numpy, pandas, or scipy required.