# tech-earnings-deepdive

Runs deep financial audits on tech company earnings reports across 16 analytical modules and six investment perspectives. Anchors valuation decisions on primary SEC filings.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 531 installs · ★ 1060
- Author: star23
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/star23/Day1Global-Skills/tree/HEAD/tech-earnings-deepdive/SKILL.md
- Updated: 2026-08-18
- HTML: https://affpapa.org/en/nevibecode/skills/tech-earnings-deepdive

## What it does

The skill conducts deep audits of quarterly and annual reports for technology businesses. Execution begins by identifying one to three decisive forces expected to alter enterprise value over the next three to five years. The workflow then prioritizes sixteen analysis modules around these catalysts, examining revenue composition, margin trends, and capital allocation.

Evidence gathering follows a strict three-tier hierarchy. You must provide at least three tier-one primary sources, such as CEO statements, customer feedback, or GitHub commits. Core numbers must trace back to regulatory SEC filings like 10-K or 10-Q forms. Wall Street consensus summaries cannot serve as the sole foundation for an investment memo. If an argument lacks primary filings, the skill flags it immediately.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add star23/Day1Global-Skills --skill tech-earnings-deepdive -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tech-earnings-deepdive/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tech-earnings-deepdive/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add star23/Day1Global-Skills --skill tech-earnings-deepdive -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tech-earnings-deepdive/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add star23/Day1Global-Skills --skill tech-earnings-deepdive -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tech-earnings-deepdive/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install star23/Day1Global-Skills tech-earnings-deepdive
```

2. **Manually: put the files in place**
- File: `.github/skills/tech-earnings-deepdive/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add star23/Day1Global-Skills --skill tech-earnings-deepdive -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/star23/Day1Global-Skills --path tech-earnings-deepdive
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tech-earnings-deepdive/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add star23/Day1Global-Skills --skill tech-earnings-deepdive -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/star23/Day1Global-Skills/tree/HEAD/tech-earnings-deepdive/SKILL.md

## When to use it
- audit quarterly tech earnings against regulatory SEC filings
- reconcile GAAP versus Non-GAAP variances and stock-based compensation dilution
- evaluate historical management guidance accuracy and earnings call sentiment
- calculate valuation matrices using cash flow models and sensitivity thresholds

## Example prompts
- Analyze NVDA's latest earnings report with focus on revenue quality and free cash flow
- Evaluate META's operating margin trends and management guidance using tech-earnings-deepdive
- Audit the GAAP to Non-GAAP variance and SBC dilution for MSFT across the past four quarters

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 5148510cf6e5492c.

BEGIN THIRD-PARTY FILE 5148510cf6e5492c
````text
---
name: tech-earnings-deepdive
description: 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 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

---

## Master Execution Flow

```
````
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