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datapack-builder

Extract, normalize, and standardize financial data into Excel workbooks.

Updated May 10, 2026
Search Tags:#financial data#Excel#investment analysis#data normalization#data pack
One-click install
npx skills add https://github.com/rpoole-dev/comps-site --skill datapack-builder-rpoole-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datapack-builder
Source: https://github.com/rpoole-dev/comps-site/tree/main/financial-services-main/plugins/vertical-plugins/investment-banking/skills/datapack-builder
Command: npx skills add https://github.com/rpoole-dev/comps-site --skill datapack-builder-rpoole-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the process of building professional financial data packs from various sources, enabling users to quickly extract, normalize, and standardize financial data into investment committee-ready Excel workbooks.

Core Features & Use Cases

  • Data Extraction: Extract financial data from CIMs, offering memorandums, SEC filings, web search, or MCP servers.
  • Normalization & Standardization: Convert financial data into standardized Excel workbooks with consistent structure and formatting.
  • Use Case: Users can utilize this Skill to build a comprehensive data pack for M&A due diligence or private equity analysis, significantly reducing the time spent on manual data processing.

Quick Start

Use the datapack-builder skill to create a financial data pack for a company named 'XYZ Corp' from their SEC filings and offering memorandums.

Frequently Asked Questions about datapack-builder

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

FAQPage Schema
How do I automate financial data extraction from SEC filings into Excel for investment analysis?▼

You can automate financial data extraction from SEC filings into Excel by using this Skill to extract, normalize, and standardize data sources directly into structured workbooks for investment analysis.

What is the best way to normalize financial data from offering memorandums for M&A due diligence?▼

The best way to normalize financial data from offering memorandums for M&A due diligence is to automate the standardization process into consistently structured Excel workbooks, ensuring the data is investment committee-ready.

Can I build a data pack from multiple sources like web search and MCP servers for private equity analysis?▼

Yes, you can build a comprehensive data pack for private equity analysis by extracting financial data from multiple sources including web search, MCP servers, and CIMs into a standardized format.

Which Python libraries do I need to standardize financial data into Excel workbooks?▼

You need the pandas, numpy, and openpyxl Python libraries to process financial data and manipulate Excel files when standardizing data into investment analysis workbooks.

Does this data normalization approach work with raw data extracted from CIMs and SEC filings?▼

Yes, this data normalization approach works directly with raw financial data extracted from CIMs and SEC filings, converting unstructured information into standardized Excel workbooks with consistent formatting.

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