Performs financial ratio analysis, DCF valuation

Financial Analyst Skill

by alirezarezvani · MIT license · ★ 26,349 on GitHub · updated Aug 30

Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances,…

---
name: "financial-analyst"
description: Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.
---

# 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

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.

Manual install (npx, ZIP)

Claude Codenpx skills add alirezarezvani/claude-skills --skill financial-analyst -g -a claude-code

Claude Code (manual)

mkdir -p ~/.claude/skills && curl -sL https://codeload.github.com/alirezarezvani/claude-skills/tar.gz/19392f7a0826 \
  | tar -xz -C ~/.claude/skills --strip-components=3 claude-skills-19392f7a0826/finance/skills/financial-analyst

Windows (PowerShell)

New-Item -ItemType Directory -Force "$HOME\.claude\skills" | Out-Null; Invoke-WebRequest "https://m.agentalley.io/zip/4e4a311ed8cf5d87323b/financial-analyst.zip" -OutFile "$env:TEMP\financial-analyst.zip"; Expand-Archive "$env:TEMP\financial-analyst.zip" -DestinationPath "$HOME\.claude\skills" -Force

Claude.ai / desktopDownload the ZIP and upload it under Settings → Capabilities → Skills.

Codex, Cursor, other agentsPoint them at SKILL.md. The other files are plain text.

Connects with Agent Alley MCP · agentalley.io/mcp

Files included

SKILL.md7.0 KB
assets/5 files · 26.1 KB
references/4 files · 29.4 KB
scripts/4 files · 69.0 KB

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SKILL.md

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Financial Analyst Skill

Overview

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_sheet for the ratio calculator, historical / assumptions for DCF, line_items for variance, historical_periods / drivers / assumptions / cash_flow_inputs for forecasting).
Nested (bundled) — inputs for all four tools in one file, nested under per-tool keys: ratio_analysis, dcf_valuation, budget_variance, forecast. See assets/sample_financial_data.json for 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.

1	---
2	name: "financial-analyst"
3	description: Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.
4	---
5
6	# Financial Analyst Skill
7
8	## Overview
9
10	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.
11
12	## 5-Phase Workflow
13
14	### Phase 1: Scoping
15	- Define analysis objectives and stakeholder requirements
16	- Identify data sources and time periods
17	- Establish materiality thresholds and accuracy targets
18	- Select appropriate analytical frameworks
19
20	### Phase 2: Data Analysis & Modeling
21	- Collect and validate financial data (income statement, balance sheet, cash flow)
22	- Validate input data completeness before running ratio calculations (check for missing fields, nulls, or implausible values)
23	- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
24	- Build DCF models with WACC and terminal value calculations; cross-check DCF outputs against sanity bounds (e.g., implied multiples vs. comparables)
25	- Construct budget variance analyses with favorable/unfavorable classification
26	- Develop driver-based forecasts with scenario modeling
27
28	### Phase 3: Insight Generation
29	- Interpret ratio trends and benchmark against industry standards
30	- Identify material variances and root causes
31	- Assess valuation ranges through sensitivity analysis
32	- Evaluate forecast scenarios (base/bull/bear) for decision support
33
34	### Phase 4: Reporting
35	- Generate executive summaries with key findings
36	- Produce detailed variance reports by department and category
37	- Deliver DCF valuation reports with sensitivity tables
38	- Present rolling forecasts with trend analysis
39
40	### Phase 5: Follow-up
41	- Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)
42	- Monitor report delivery timeliness (target: 100% on time)
43	- Update models with actuals as they become available
44	- Refine assumptions based on variance analysis
45
46	## Tools
47
48	### 1. Ratio Calculator (`scripts/ratio_calculator.py`)
49
50	Calculate and interpret financial ratios from financial statement data.
51
52	Ratio Categories:
53	- Profitability: ROE, ROA, Gross Margin, Operating Margin, Net Margin
54	- Liquidity: Current Ratio, Quick Ratio, Cash Ratio
55	- Leverage: Debt-to-Equity, Interest Coverage, DSCR
56	- Efficiency: Asset Turnover, Inventory Turnover, Receivables Turnover, DSO
57	- Valuation: P/E, P/B, P/S, EV/EBITDA, PEG Ratio
58
59	```bash
60	python scripts/ratio_calculator.py assets/sample_financial_data.json
61	python scripts/ratio_calculator.py assets/sample_financial_data.json --format json
62	python scripts/ratio_calculator.py assets/sample_financial_data.json --category profitability
63	```
64
65	### 2. DCF Valuation (`scripts/dcf_valuation.py`)
66
67	Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.
68
69	Features:
70	- WACC calculation via CAPM
71	- Revenue and free cash flow projections (5-year default)
72	- Terminal value via perpetuity growth and exit multiple methods
73	- Enterprise value and equity value derivation
74	- Two-way sensitivity analysis (discount rate vs growth rate)
75
76	```bash
77	python scripts/dcf_valuation.py assets/sample_financial_data.json
78	python scripts/dcf_valuation.py assets/sample_financial_data.json --format json
79	python scripts/dcf_valuation.py assets/sample_financial_data.json --projection-years 7
80	```
81
82	### 3. Budget Variance Analyzer (`scripts/budget_variance_analyzer.py`)
83
84	Analyze actual vs budget vs prior year performance with materiality filtering.
85
86	Features:
87	- Dollar and percentage variance calculation
88	- Materiality threshold filtering (default: 10% or $50K)
89	- Favorable/unfavorable classification with revenue/expense logic
90	- Department and category breakdown
91	- Executive summary generation
92
93	```bash
94	python scripts/budget_variance_analyzer.py assets/sample_financial_data.json
95	python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --format json
96	python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --threshold-pct 5 --threshold-amt 25000
97	```
98
99	### 4. Forecast Builder (`scripts/forecast_builder.py`)
100
101	Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.
102
103	Features:
104	- Driver-based revenue forecast model
105	- 13-week rolling cash flow projection
106	- Scenario modeling (base/bull/bear cases)
107	- Trend analysis using simple linear regression (standard library)
108
109	```bash
110	python scripts/forecast_builder.py assets/sample_financial_data.json
111	python scripts/forecast_builder.py assets/sample_financial_data.json --format json
112	python scripts/forecast_builder.py assets/sample_financial_data.json --scenarios base,bull,bear
113	```
114
115	## Knowledge Bases
116
117	\| Reference \| Purpose \|
118	\|-----------\|---------\|
119	\| `references/financial-ratios-guide.md` \| Ratio formulas, interpretation, industry benchmarks \|
120	\| `references/valuation-methodology.md` \| DCF methodology, WACC, terminal value, comps \|
121	\| `references/forecasting-best-practices.md` \| Driver-based forecasting, rolling forecasts, accuracy \|
122	\| `references/industry-adaptations.md` \| Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare) \|
123
124	## Templates
125
126	\| Template \| Purpose \|
127	\|----------\|---------\|
128	\| `assets/variance_report_template.md` \| Budget variance report template \|
129	\| `assets/dcf_analysis_template.md` \| DCF valuation analysis template \|
130	\| `assets/forecast_report_template.md` \| Revenue forecast report template \|
131
132	## Key Metrics & Targets
133
134	\| Metric \| Target \|
135	\|--------\|--------\|
136	\| Forecast accuracy (revenue) \| +/-5% \|
137	\| Forecast accuracy (expenses) \| +/-3% \|
138	\| Report delivery \| 100% on time \|
139	\| Model documentation \| Complete for all assumptions \|
140	\| Variance explanation \| 100% of material variances \|
141
142	## Input Data Format
143
144	All scripts accept JSON input files in either of two shapes:
145
146	1. Flat — the tool's expected keys at the top level (e.g., `income_statement` / `balance_sheet` for the ratio calculator, `historical` / `assumptions` for DCF, `line_items` for variance, `historical_periods` / `drivers` / `assumptions` / `cash_flow_inputs` for forecasting).
147	2. Nested (bundled) — inputs for all four tools in one file, nested under per-tool keys: `ratio_analysis`, `dcf_valuation`, `budget_variance`, `forecast`. See `assets/sample_financial_data.json` for the complete bundled schema; every quick-start command above runs directly against it.
148
149	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.
150
151	## Dependencies
152
153	None - All scripts use Python standard library only (`math`, `statistics`, `json`, `argparse`, `datetime`). No numpy, pandas, or scipy required.
154