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earnings-preview

The earnings-preview skill assembles pre-earnings analysis for individual stocks by gathering consensus estimates, recent price action, and SEC filings, then synthesizing a framework of sector-specific operational metrics and bull/base/bear scenarios with stock price implications. Use this skill before company earnings announcements to prepare positioning notes, identify key metrics that will drive stock reaction, and establish scenario benchmarks for rapid decision-making after the print.

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git clone --depth 1 https://github.com/ginlix-ai/LangAlpha /tmp/earnings-preview && cp -r /tmp/earnings-preview/skills/earnings-preview ~/.claude/skills/earnings-preview
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Earnings Preview

description: Build pre-earnings analysis with estimate models, scenario frameworks, and key metrics to watch. Use before a company reports quarterly earnings to prepare positioning notes, set up bull/bear scenarios, and identify what will move the stock. Triggers on "earnings preview", "what to watch for [company] earnings", "pre-earnings", "earnings setup", or "preview Q[X] for [company]".

## Workflow

### Step 1: Gather Context

- Identify the company and reporting quarter
- Use `get_company_overview` tool — includes earnings history (actual vs estimate), analyst consensus, price targets, rating distribution
- Use `get_stock_daily_prices` tool for recent price history and to identify the earnings date window
- Use `get_sec_filing` tool — auto-attaches earnings call transcript for 10-K/10-Q filings (review prior quarter for guidance or commentary)
- Use `WebSearch` / `WebFetch` for recent news and sentiment heading into earnings

### Step 2: Key Metrics Framework

Build a "what to watch" framework specific to the company:

**Financial Metrics:**
- Revenue vs. consensus (total and by segment)
- EPS vs. consensus
- Margins (gross, operating, net) — expanding or contracting?
- Free cash flow
- Forward guidance vs. consensus

**Operational Metrics** (sector-specific):
- Tech/SaaS: ARR, net retention, RPO, customer count
- Retail: Same-store sales, traffic, basket size
- Industrials: Backlog, book-to-bill, price vs. volume
- Financials: NIM, credit quality, loan growth, fee income
- Healthcare: Scripts, patient volumes, pipeline updates

### Step 3: Scenario Analysis

Build 3 scenarios with stock price implications:

| Scenario | Revenue | EPS | Key Driver | Stock Reaction |
|----------|---------|-----|------------|----------------|
| Bull | | | | |
| Base | | | | |
| Bear | | | | |

For each scenario:
- What would need to happen operationally
- What management commentary would signal this
- Historical context — how has the stock moved on similar prints?

### Step 4: Catalyst Checklist

Identify the 3-5 things that will determine the stock's reaction:

1. [Metric] vs. [consensus/whisper number] — why it matters
2. [Guidance item] — what the buy-side expects to hear
3. [Narrative shift] — any strategic changes, M&A, restructuring

### Step 5: Output

Save all deliverables to `$WORK_DIR/work/{task}/`. One-page earnings preview with:
- Company, quarter, earnings date
- Consensus estimates table
- Key metrics to watch (ranked by importance)
- Bull/base/bear scenario table
- Catalyst checklist
- Trading setup: recent stock performance, implied move from options

## Important Notes

- Consensus estimates change — always note the source and date of estimates
- "Whisper numbers" from buy-side surveys are often more relevant than published consensus
- Historical earnings reactions help calibrate expectations — use `get_company_overview` for historical actual vs estimate data
- Options-implied move tells you what the market expects — compare to your scenarios
- Save all output files to `$WORK_DIR/work/{task}/`