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earnings-preview · anthropics — Claude Code skill: pre-earnings analysis

Turns a quarterly report into a one-page preview: consensus estimates, bull/base/bear scenarios and the metrics that will move the stock.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add anthropics/financial-services --skill earnings-preview -a claude-code
Open on GitHub

What earnings-preview does

The skill walks the agent through five steps. It starts by pinning down the company and quarter, pulling consensus figures through web search, checking whether the release lands pre-market or after-hours, and reading the previous earnings call for guidance. From there it builds a watch list tuned to the company: revenue and EPS against consensus, margins, free cash flow, forward guidance, plus sector metrics such as ARR and net retention for SaaS, same-store sales for retail, backlog for industrials.

Step three lays out three scenarios with stock implications. Each one spells out what has to happen operationally, which management comments would confirm it, and how the shares reacted to similar prints before. A catalyst checklist follows — three to five items that decide the market's response. The output is a single page: consensus table, metrics ranked by importance, scenario table, catalysts, and a trading setup with recent performance and the options-implied move.

Consensus numbers shift, so the skill insists on recording the source and date behind every estimate. Whisper numbers from buy-side surveys often matter more than the published consensus, and the options-implied move shows what the market already expects — line it up against your scenarios. Web search access is what makes step one work; without it the consensus gathering stalls.

What it helps with

  • pull consensus revenue and EPS ahead of a print
  • map a report into three scenarios with stock reactions
  • pick sector metrics for a specific company
  • draft a 3–5 item catalyst checklist
  • compare the options-implied move against your scenarios

Example prompts for the agent

  • Build an earnings preview for NVDA Q3 with consensus and scenarios
  • What to watch for in the AAPL earnings — gather metrics and catalysts
  • Prepare a pre-earnings setup with a bull/base/bear table

How to install earnings-preview

1Skills CLI (Vercel)

npx skills add anthropics/financial-services --skill earnings-preview -a claude-code

2Manually: put the files in place

~/.claude/skills/earnings-preview/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/earnings-preview/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about earnings-preview

How do I install earnings-preview in Claude Code?

Run npx skills add anthropics/financial-services --skill earnings-preview -a claude-code. The skill lands at ~/.claude/skills/earnings-preview/SKILL.md.

What does earnings-preview do?

It assembles a pre-earnings brief: consensus estimates, sector metrics, three scenarios with stock reactions, and a catalyst checklist. The result is a one-page preview with a trading setup.

Does it need web search access?

Yes. The skill pulls consensus estimates and the release date through web search, so the first step depends on it.

Source text

SKILL.md · Apache-2.0
---
name: 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]".
---

# Earnings Preview

## 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

© anthropics · Apache-2.0 · anthropics/financial-services

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