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earnings-analysis

Generate equity research earnings update reports with beat/miss analysis and charts.

Updated Jun 5, 2026
Search Tags:#report generation#data analysis#financial reporting#equity research#earnings analysis
One-click install
npx skills add https://github.com/Duzhenyang111/stock_money --skill earnings-analysis-duzhenyang111
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: earnings-analysis
Source: https://github.com/Duzhenyang111/stock_money/tree/main/financial-services-main/plugins/vertical-plugins/equity-research/skills/earnings-analysis
Command: npx skills add https://github.com/Duzhenyang111/stock_money --skill earnings-analysis-duzhenyang111

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, matplotlib, seaborn, python-docx, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the creation of comprehensive equity research earnings update reports, significantly reducing the manual effort required for financial analysts.

Core Features & Use Cases

  • Professional Report Generation: Creates earnings update reports with specific formatting, including tables and charts.
  • Data Analysis: Automates the analysis of financial data, providing insights on revenue, margins, and guidance.
  • Use Case: Ideal for financial analysts who need to quickly produce earnings reports post-earnings release.

Quick Start

Use the earnings-analysis skill to generate an earnings update report for Company XYZ for Q3 2024.

Frequently Asked Questions about earnings-analysis

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

FAQPage Schema
How do I automate equity research earnings update report generation?▼

Automating equity research earnings update report generation involves using Python to analyze quarterly financial results, creating visualizations with matplotlib and seaborn, and exporting professional DOCX reports with beat/miss analysis and revised thesis.

Can I use Python to analyze quarterly financial results and create earnings reports?▼

Yes, you can use Python with pandas for data analysis to evaluate quarterly financial results, generate charts with seaborn and matplotlib, and produce formatted DOCX earnings reports including updated estimates and key metrics.

What is included in an automated earnings analysis report?▼

An automated earnings analysis report includes beat/miss analysis, key financial metrics, updated estimates, revised thesis, revenue insights, margin analysis, and guidance evaluation, all formatted with tables and charts in a DOCX file.

Do I need matplotlib and seaborn to generate financial reporting charts?▼

Yes, matplotlib and seaborn are required to generate the visualizations and charts for financial reporting, providing graphical insights into revenue, margins, and other key metrics within the final equity research report.

How do I perform beat/miss analysis for post-earnings release data?▼

Performing beat/miss analysis for post-earnings release data requires processing quarterly financial data with pandas to compare actual results against estimates, generating insights on revenue and margins for your equity research report.

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