Summary

This repository provides a sophisticated "tech-earnings-deepdive" skill for Claude, automating institutional-grade analysis of technology company earnings reports. It targets investors and researchers seeking a structured, multi-faceted approach to evaluating tech stocks, offering comprehensive insights and actionable investment memos to streamline analysis and identify potential market blind spots.

How It Works

The skill automatically activates upon relevant queries to Claude, performing deep analysis across 16 modules (e.g., revenue quality, cash flow, valuation) and from 6 investment philosophy perspectives (e.g., Quality Compounder, Imaginative Growth). It employs a multi-method valuation matrix, an anti-bias framework with cognitive trap checks, and a three-tier evidence standard prioritizing primary sources. This layered methodology aims for a robust, nuanced, and less biased investment assessment.

Quick Start & Requirements

• Primary install / run command: /install-skill https://github.com/Day1Global/Day1Global-Skills/raw/main/tech-earnings-deepdive.skill (via URL, recommended) or manual upload to Claude.
• Prerequisites: Requires access to Claude AI.
• Usage: Natural language queries to Claude after installation.

Highlighted Details

• Comprehensive analysis covering 16 modules (A-P) and 6 investment philosophies.
• Multi-method valuation matrix, variant view for market blind spots, and Pre-Mortem analysis.
• Actionable outputs: "Action Price," position sizing cadence, and exit triggers.
• Three-tier evidence standard prioritizing primary and factual sources.

Maintenance & Community

No specific details regarding maintenance, contributors, sponsorships, or community channels are provided in the README.

Licensing & Compatibility

The README does not explicitly state the software license, making commercial use or closed-source linking compatibility indeterminate.

Limitations & Caveats

The skill's analyses are for research purposes only and do not constitute investment advice. Effectiveness relies on the Claude model and public data quality.