datapack-builder

Extract and standardize financial data into Excel workbooks.

Updated Jun 5, 2026

Search Tags:#standardization #financial data #Excel #investment analysis #data pack

Ready to run

npx skills add https://github.com/Duzhenyang111/stock_money --skill datapack-builder-duzhenyang111

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: datapack-builder
Source: https://github.com/Duzhenyang111/stock_money/tree/main/financial-services-main/plugins/vertical-plugins/investment-banking/skills/datapack-builder
Command: npx skills add https://github.com/Duzhenyang111/stock_money --skill datapack-builder-duzhenyang111

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires xlsx, pandas, numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the complex process of building standardized financial data packs for investment committees, significantly reducing manual effort and ensuring accuracy in financial reporting.

Core Features & Use Cases

Data Extraction and Standardization: Extract financial data from various sources and standardize it into a consistent format.
Excel Workbook Creation: Generate investment committee-ready Excel workbooks with structured data and proper formatting.
Use Case: Streamline the preparation of data packs for M&A due diligence, private equity analysis, and investment committee meetings.

Quick Start

Use the datapack-builder skill to create a financial data pack for Company XYZ from the sources provided.

Frequently Asked Questions about datapack-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate building financial data packs for investment committees?▼

Automating financial data pack building involves extracting and standardizing data from multiple sources into consistent Excel workbooks. This process streamlines M&A due diligence and investment committee preparation by ensuring structured data and proper formatting without manual effort.

What is the best way to standardize financial data from multiple sources for private equity analysis?▼

Standardizing financial data for private equity analysis requires extracting disparate figures and compiling them into a consistent format. Using Python libraries like pandas and numpy automates this standardization, resulting in accurate, structured data ready for investment committee review.

Does this financial data pack builder require Python and specific libraries to generate Excel workbooks?▼

Yes, generating standardized Excel workbooks requires Python and specific dependencies. The automation relies on the pandas and numpy libraries for data processing, and the xlsx library to create the final formatted Excel workbooks for investment committee meetings.

Can I use this tool to compile M&A due diligence data into a single Excel workbook?▼

Yes, you can compile M&A due diligence data into a single Excel workbook. The tool extracts financial data from various provided sources, standardizes the figures, and generates a structured, investment committee-ready Excel workbook for your analysis.