Finance Skills

What Is Finance Skills?

Finance Skills is a production-grade financial analysis agent skill and plugin, purpose-built for use with modern AI coding environments such as Claude Code, Codex CLI, Gemini CLI, Cursor, and OpenClaw. Developed to assist developers, analysts, and business professionals, this skillset delivers a suite of Python-based analytical tools for ratio analysis, discounted cash flow (DCF) valuation, budget variance analysis, and rolling forecasts. The entire toolkit is implemented using Python's standard library, ensuring minimal dependencies and maximum portability. Finance Skills is open source under the MIT License and is maintained by Alireza Rezvani.

Why Use Finance Skills?

Financial analysis is at the core of strategic business decision-making. Whether you are evaluating investment opportunities, managing corporate budgets, or forecasting financial outcomes, robust, transparent, and reproducible analysis tools are essential. Finance Skills empowers users to perform standard financial analyses directly within their development or AI agent workflow. This integration eliminates the need for separate, often proprietary, financial software, and makes it easier to automate, audit, and customize analytical routines. By leveraging only the Python standard library, the skill is lightweight, highly portable, and suitable for environments where installing external packages is restricted. The inclusion of DCF valuation and forecasting tools extends applicability from simple ratio checks to advanced forward-looking analysis.

How to Get Started

Finance Skills is designed for rapid adoption across several agent platforms. Here’s how to begin:

In Claude Code

You can load the Finance Skills module directly within the Claude Code environment:

/read finance/financial-analyst/SKILL.md

This command reads the skill’s documentation and initializes its capabilities within the Claude agent context.

In Codex CLI

Add the skill to your Codex CLI environment using the agent skills CLI:

npx agent-skills-cli add alirezarezvani/claude-skills/finance

This will fetch the latest version from the GitHub repository and register it for use.

Python Command Line

Each financial tool is a standalone Python script located in financial-analyst/scripts/. For example, to perform ratio analysis:

python3 financial-analyst/scripts/ratio_calculator.py --help

Replace ratio_calculator.py with dcf_valuation.py, budget_variance_analyzer.py, or forecast_builder.py for the respective analyses.

Key Features

Finance Skills provides four primary analytical tools, each implemented as a standalone Python script:

1. Ratio

Analysis

The ratio_calculator.py script computes key financial ratios from user-supplied data. This includes liquidity ratios (current, quick), profitability ratios (net margin, ROE), and leverage ratios (debt/equity).

Example Usage:

python3 financial-analyst/scripts/ratio_calculator.py --input sample_balance_sheet.csv

2. DCF

Valuation

The dcf_valuation.py tool performs discounted cash flow valuation using standard financial inputs. It supports both simple and multi-stage DCF models.

Example Usage:

python3 financial-analyst/scripts/dcf_valuation.py --cash-flows 10000,12000,14000 --discount-rate 0.09

3. Budget Variance

Analysis

With budget_variance_analyzer.py, users can compare actual financial performance against budgets, highlighting variances for cost control and forecasting.

Example Usage:

python3 financial-analyst/scripts/budget_variance_analyzer.py --budget budget.csv --actual actual.csv

4. Rolling

Forecasts

The forecast_builder.py script enables users to build rolling forecasts based on historical data and user assumptions, supporting dynamic scenario analysis.

Example Usage:

python3 financial-analyst/scripts/forecast_builder.py --historical sales.csv --periods 6

All scripts are standalone, require only standard Python 3.x, and accept CSV file inputs for seamless data integration.

Best Practices

• Data Validation: Always validate your input data for accuracy and completeness before using any analytical tool. Incorrect input can lead to misleading results.
• Version Control: Use source control for input data and scripts to ensure reproducibility and auditability of financial analyses.
• Parameter Sensitivity: When performing DCF or forecasting, test multiple scenarios by varying discount rates, growth rates, and forecast periods to understand the full range of potential outcomes.
• Automation: Where possible, automate repetitive analyses using shell scripts or task runners to save time and reduce human error.
• Documentation: Document all assumptions, data sources, and parameters used in your analyses for transparency and future reference.

Important Notes

• Skill Selection: Load only the specific skill or script you require for your analysis to minimize resource usage and avoid unnecessary complexity.
• Output Verification: Always cross-check financial outputs against original source data. The automated tools are only as reliable as the data provided.
• Compliance: Ensure that your use of financial analysis complies with your organization’s regulatory and auditing standards.
• Community Support: As an open-source project, contributions, bug reports, and feature requests are welcome via the official GitHub repository.
• No External Dependencies: All scripts use only the Python standard library, enhancing security and compatibility but also requiring users to handle more complex data manipulations manually if needed.

Finance Skills brings robust, automatable, and portable financial analysis directly to the developer and analyst workflow, enabling better, faster, and more reliable business decision support.