# Earnings Preview

> Use when financial-services work requires earnings Preview.

- Skill: `changhochien/earnings-preview` (Agent Skill)
- Install (CLI): `npx skillmds@latest add changhochien/earnings-preview`
- Raw SKILL.md: https://api.skillmd.com/api/skills/changhochien/earnings-preview/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search, AI & ML
- License: MIT
- Author: changhochien (https://skillmd.com/u/changhochien)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/changhochien/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
- 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

## Hermes Profile Notes
This skill was packaged from `pi-financial-services` source path `plugins/vertical-plugins/equity-research/skills/earnings-preview` for the Hermes financial-services profile. Use institutional data connectors first when available, cite sources, and stage outputs for qualified human review.


## Common Pitfalls
1. Do not present drafts as investment, legal, tax, or accounting advice.
2. Do not use web search as the primary source when an institutional MCP/data connector is available.
3. Do not execute transactions, contact clients, post to a ledger, or approve onboarding. Stage outputs for review.


## Verification Checklist
- [ ] Inputs, assumptions, dates, and currencies are explicit.
- [ ] Numbers tie across tables, models, decks, and memos.
- [ ] Sources are cited and institutional data is preferred where available.
- [ ] Output is labeled draft / for human review where appropriate.