Individual skill
tech-earnings-deepdive
Tech Stock Earnings Deep Dive Analysis and Multi-Perspective Investment Memo System (v3.0). Covers 16 major analysis modules (A-P), 6 investment philosophy perspectives, institutional-grade evidence standards, anti-bias framework, and actionable decision system. When users mention topics such as tech company earnings analysis, quarterly/annual report interpretation, earnings call, revenue growth analysis, margin changes, guidance, valuation models, DCF, reverse DCF, EV/EBITDA, PEG, Rule of 40, management analysis, competitive landscape, position sizing, whether to buy/sell/add to a tech stock position, how to interpret a company's latest earnings, doing a deep dive, multi-angle valuation, how investment masters view a company, variant view, key forces, kill conditions, ownership structure, executive team, partner ecosystem, macro policy impact, etc., this skill should be used. Even if the user simply asks "help me look at NVDA's latest earnings" or "how did META do this quarter" or "should I keep holding MSFT," this skill should be triggered to provide comprehensive earnings analysis and a multi-perspective investment memo. This skill complements the us-value-investing skill — us-value-investing focuses on long-term value four-dimensional scoring, while this skill focuses on in-depth dissection of the latest earnings, comprehensive judgment across multiple investment philosophies, and actionable position decisions.
tech-earnings-deepdive/SKILL.md
Source commit 562c14b0c0bc84abff755181ead30ebea613d063
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- Last checked
- Nov 10, 2026
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- Repository last updated
- Aug 18, 2026
- License
- MIT
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# Tech Stock Earnings Deep Dive Analysis & Multi-Perspective Investment Memo v3.0
## Positioning & Design Philosophy
You are providing **institutional-grade** earnings analysis services for a "large retail investor" — someone investing their own capital, with no LPs, who holds tech stock positions on a quarterly and annual basis.
Core design principles:
- **Key Forces Driven**: First identify 1-3 decisive forces, then prioritize the 16 modules around those forces — deeply examine related modules, provide standard coverage for the rest
- **Multi-Philosophy Confrontation**: Review the same dataset through 6 completely different investment worldviews, letting conclusions emerge from the collision
- **Primary Evidence First**: Third-party aggregation sites are the floor, not the ceiling — trace information back to its source
- **Actionable Decisions**: Not "bullish/bearish," but "at what price take what action, what conditions trigger an exit"
- **Quarterly Tracking Design**: Each module has built-in QoQ and YoY comparison frameworks to support continuous cross-quarter tracking
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