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ViviennaMAO
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earnings-analysis

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

26|2|Updated Apr 30, 2026
Search Tags:#valuation#financial reporting#equity research#earnings analysis#quarterly analysis
One-click install
npx skills add https://github.com/ViviennaMAO/money_banking_financial_market --skill earnings-analysis-viviennamao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: earnings-analysis
Source: https://github.com/ViviennaMAO/money_banking_financial_market/tree/main/financial-services-main/plugins/vertical-plugins/equity-research/skills/earnings-analysis
Command: npx skills add https://github.com/ViviennaMAO/money_banking_financial_market --skill earnings-analysis-viviennamao

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides quick, professional earnings update reports for companies under coverage, streamlining the analysis and report generation process for equity research professionals.

Core Features & Use Cases

  • Earnings Analysis: Quickly analyze and summarize quarterly earnings results.
  • Report Generation: Automatically generate detailed reports with tables, charts, and valuation analysis.
  • Use Case: With the earnings-analysis skill, generate a comprehensive earnings update report for a company like "Apple Inc." for the Q1 2024 financial quarter.

Quick Start

Use the earnings-analysis skill to create an earnings update report for "Apple Inc." for the Q1 2024 financial quarter.

Frequently Asked Questions about earnings-analysis

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

FAQPage Schema
How do I generate an equity research earnings update report for quarterly results?▼

Generate an equity research earnings report by analyzing quarterly results, including beat/miss analysis, key metrics, and revised thesis. Python processes the data, while pandas and matplotlib handle data visualization and analysis to produce the final report.

What is the best way to analyze quarterly earnings beat/miss and update estimates?▼

Analyzing quarterly earnings beat/miss and updating estimates is done by processing financial reporting data with Python. The analysis evaluates key metrics and revised thesis to automatically generate professional equity research reports with detailed tables and charts.

Do I need Python and pandas to create financial reporting visualizations for earnings analysis?▼

Yes, you need Python and pandas to create financial reporting visualizations for earnings analysis. The report generation process specifically requires Python for data processing, pandas for data analysis, and matplotlib for data visualization.

Can I automatically generate valuation charts and tables for equity research?▼

Yes, you can automatically generate valuation charts and tables for equity research. The Python script uses matplotlib and pandas to process quarterly analysis data and output comprehensive reports containing visualizations and updated estimates.

Does this earnings analysis approach work for specific companies like Apple Inc.?▼

Yes, this earnings analysis approach works for specific companies like Apple Inc. You can target a specific financial quarter, such as Q1 2024, to produce a professional earnings update report with beat/miss analysis and valuation data.

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