# Dependencies for Running financial_rigor.py in AI-Berkshire: Zero External Packages Required

> Discover the dependencies for financial_rigor.py. ai-berkshire requires Python 3.7+ and uses only standard libraries, eliminating external package installations.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: getting-started
- Published: 2026-07-04

---

**The financial_rigor.py script requires only Python 3.7 or higher and exclusively uses standard library modules, meaning no external packages need to be installed.**

The [`financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/financial_rigor.py) script is a **stand-alone command-line utility** located in the `tools/` directory of the `xbtlin/ai-berkshire` repository. Designed to validate financial numbers through rigorous arithmetic checks, it deliberately avoids third-party dependencies to ensure execution in constrained environments like CI runners and Docker containers without pip or virtual-environment management.

## Python Version Requirements

The script requires **Python 3.7 or higher**. While the code utilizes f-strings and type hints introduced in Python 3.6, version 3.7 ensures reliable operation of the `decimal` module's context for exact arithmetic. This version dependency supports the precise financial calculations performed by the tool's validation algorithms.

## Standard Library Modules Used

As explicitly documented in the header comments at lines 7-9 of [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py), the script maintains **"Zero external dependencies — uses only Python stdlib"**. The specific imports declared at lines 18-22 include:

- **argparse** – Parses sub-commands and command-line options
- **json** – Decodes JSON strings supplied via CLI arguments
- **math** – Provides logarithm functions for Benford's Law analysis
- **sys** – Handles the entry point through `if __name__ == "__main__":`
- **decimal** – Supplies `Decimal`, `Context`, and rounding constants for exact financial arithmetic

## Running the Script Without Installation

Because all dependencies are satisfied by the Python standard library, the script executes immediately without `pip install`. Run it directly from the repository root:

```bash
python3 tools/financial_rigor.py <sub-command> [options]

```

The following examples demonstrate core functionality that works in any standard Python environment:

Verify market capitalization calculations against reported values:

```bash
python3 tools/financial_rigor.py verify-market-cap --price 510 --shares 9.11e9 --reported 4.65e12 --currency HKD

```

Calculate valuation ratios using exact decimal arithmetic:

```bash
python3 tools/financial_rigor.py verify-valuation --price 510 --eps 23.5 --bvps 120

```

Cross-validate financial data from multiple sources:

```bash
python3 tools/financial_rigor.py cross-validate --field revenue --values '{"AnnualReport":7518,"Yahoo":7500}' --unit 亿

```

Perform Benford's Law analysis on numerical datasets:

```bash
python3 tools/financial_rigor.py benford --values '[1234, 2345, 3456, ...]'

```

Evaluate arithmetic expressions without floating-point error:

```bash
python3 tools/financial_rigor.py calc --expr '510 * 9.11e9'

```

Generate three-scenario valuation targets:

```bash
python3 tools/financial_rigor.py three-scenario --price 510 --eps 23.5 --shares 9.11 --growth 0.15 0.08 0.0 --pe 25 20 15

```

## Summary

- **Python 3.7+** is the sole runtime requirement for executing [`financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/financial_rigor.py)
- The script imports exclusively from the **Python standard library**: `argparse`, `json`, `math`, `sys`, and `decimal`
- **No external dependencies** are required, as confirmed by the header documentation at lines 7-9 in [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)
- The zero-dependency architecture supports automated workflows in constrained environments where pip is unavailable
- Located at [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) in the `xbtlin/ai-berkshire` repository, the script validates market caps, valuation ratios, and cross-source financial data

## Frequently Asked Questions

### What Python version is required for financial_rigor.py?

The script requires **Python 3.7 or higher**. While Python 3.6 supports the f-strings and type hints used in the code, version 3.7 ensures reliable `Decimal` context handling for exact financial arithmetic, which is critical for the script's validation accuracy.

### Does financial_rigor.py require pip install?

**No.** According to the source code in `xbtlin/ai-berkshire`, the script uses only standard library modules. The import section at lines 18-22 confirms dependencies on `argparse`, `json`, `math`, `sys`, and `decimal` only, allowing immediate execution with `python3 tools/financial_rigor.py` without any package installation.

### Which standard library modules does financial_rigor.py use?

The script imports five standard library modules: **argparse** for CLI parsing, **json** for data decoding, **math** for logarithmic calculations in Benford's Law tests, **sys** for entry-point handling, and **decimal** for exact arithmetic. These imports are located at lines 18-22 of [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py).

### Why does the script avoid external dependencies?

The zero-dependency design is **intentional** to support integration with Claude Code skills and automated workflows. By relying solely on the Python standard library, the tool executes reliably in CI/CD pipelines, Docker containers, and other restricted environments without requiring dependency resolution or virtual environment management.