# Earnings Preview

> Pre-earnings analysis: consensus estimates, key metrics to watch, bull/base/bear scenarios

- Skill: `gabrielmoreira/earnings-preview` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/earnings-preview`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/earnings-preview/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: Derived from anthropics/financial-services-plugins (Apache-2.0).
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/earnings-preview

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# 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_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}/`