Agent skill · Business & Finance

financial-analyst

Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill financial-analyst-alirezarezvani-claude-skills --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Path: skills/analysis/financial-analyst-alirezarezvani-claude-skills/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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 valuat

What's inside
Steps it walks through
  1. Overview
  2. 5-Phase Workflow
  3. Phase 1: Scoping
  4. Phase 2: Data Analysis & Modeling
  5. Phase 3: Insight Generation
  6. Phase 4: Reporting
  7. Phase 5: Follow-up
  8. Tools
  9. 1. Ratio Calculator (scripts/ratiocalculator.py)
  10. 2. DCF Valuation (scripts/dcfvaluation.py)
  11. 3. Budget Variance Analyzer (scripts/budgetvarianceanalyzer.py)
  12. 4. Forecast Builder (scripts/forecastbuilder.py)
  13. Knowledge Bases
  14. Templates
Ships with 1 file
  • metadata.json
Commands it runs
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
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
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
python scripts/forecast_builder.py forecast_data.json
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About this skill
What does the financial-analyst skill do?

Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill financial-analyst-alirezarezvani-claude-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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