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earnings-analysis

Generate professional earnings update reports from quarterly financial results.

1|Updated Apr 30, 2026
Search Tags:#report generation#financial reporting#equity research#estimates#earnings analysis
One-click install
npx skills add https://github.com/wpsadi/stock-agent --skill earnings-analysis-wpsadi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: earnings-analysis
Source: https://github.com/wpsadi/stock-agent/tree/main/.agents/skills/earnings-analysis
Command: npx skills add https://github.com/wpsadi/stock-agent --skill earnings-analysis-wpsadi

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 solves the problem of creating detailed and professional earnings update reports for companies that are already under coverage. It automates the process of analyzing quarterly results, generating reports, and updating estimates.

Core Features & Use Cases

  • Automated Report Generation: Create comprehensive earnings update reports with tables, charts, and analysis.
  • Beat/Miss Analysis: Analyze whether a company beat or missed earnings estimates.
  • Estimate Updates: Update forward estimates based on quarterly results.
  • Use Case: If a user needs an earnings update for a covered company, this Skill can be used to quickly generate a professional report with analysis and updated estimates.

Quick Start

Generate an earnings update report for "Apple Inc." Q1 FY24 earnings results.

Frequently Asked Questions about earnings-analysis

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

FAQPage Schema
How do I automate financial reporting and earnings analysis for covered companies?▼

You can automate earnings analysis by processing quarterly financial results to generate professional reports. The workflow analyzes beat/miss performance against estimates and updates forward estimates, outputting comprehensive equity research documents with tables and charts.

How does beat/miss analysis work when generating an earnings update report?▼

Beat/miss analysis compares actual quarterly financial results against prior earnings estimates. The mechanism identifies whether a company exceeded or fell short of expectations, then incorporates those variance results into the generated financial reporting and updated forward estimates.

Can I use Python with pandas and matplotlib to create equity research reports?▼

Yes, this earnings analysis workflow relies on Python for data processing, utilizing pandas for data manipulation and matplotlib with seaborn for visualizations. It outputs professional earnings update reports formatted as Word documents via the python-docx library.

What is the best way to generate an earnings update report after quarterly results?▼

The best way to generate an earnings update report is to automate the analysis of quarterly financial results. This process handles beat/miss analysis, updates forward estimates, and compiles the findings into a professional document with supporting tables and charts.

Do I need Python environment setup to run financial reporting and estimate updates?▼

Yes, a Python environment is required because the financial reporting and estimate updates depend on specific libraries. You must have pandas, matplotlib, seaborn, and python-docx installed to process the quarterly data and generate the final report files.

What are the limitations of using automated earnings analysis for institutional reporting?▼

Automated earnings analysis is limited to companies already under coverage and requires structured quarterly financial results as input. It does not replace broader equity research judgment but focuses strictly on report generation, beat/miss analysis, and estimate updates.

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