Earnings Preview

---
name: earnings-preview
description: "Use this skill when the task requires earnings preview capabilities."
executor: LLM_BEHAVIOR
skill_id: finance.equity_research.earnings_preview_2
status: ADOPTED
security: {level: high, pii: false, approval_required: true}
anchors:
  - finance
  - ai_ml
  - reporting
  - design
tier: 2
input_schema:
  - name: code_or_task
    type: string
    description: "Code snippet, script, or task description to process"
    required: true
  - name: context
    type: string
    description: "Additional context or background information"
    required: false
output_schema:
  - name: report
    type: string
    description: "Analysis report or summary from earnings preview"
---
# 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
- Pull consensus estimates via web search (revenue, EPS, key segment metrics)
- Find the earnings date and time (pre-market vs. after-hours)
- Review the company's prior quarter earnings call for any guidance or commentary

### 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

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 (search for "[company] earnings reaction history")
- Options-implied move tells you what the market expects — compare to your scenarios

---

## Why This Skill Exists

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 [compan

<!-- SR_40: auto-generated from frontmatter `purpose`/`description` (OPP-Phase3). Expand with domain-specific rationale. -->

## When to Use

Use this skill when the task requires earnings preview capabilities.

<!-- SR_40: auto-generated from frontmatter `when`/`description` (OPP-Phase3). -->

## What If Fails

If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.

<!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->