Agent Skillsginlix-ai/LangAlpha › earnings-preview

earnings-preview

GitHub

生成财报前分析报告,整合共识预期、关键指标及牛/基/熊情景。用于准备定位笔记,识别驱动股价因素,输出包含情景推演和催化剂清单的一页纸预览。

skills/earnings-preview/SKILL.md ginlix-ai/LangAlpha

Trigger Scenarios

earnings preview what to watch for [company] earnings pre-earnings earnings setup preview Q[X] for [company]

Install

npx skills add ginlix-ai/LangAlpha --skill earnings-preview -g -y
More Options

Use without installing

npx skills use ginlix-ai/LangAlpha@earnings-preview

指定 Agent (Claude Code)

npx skills add ginlix-ai/LangAlpha --skill earnings-preview -a claude-code -g -y

安装 repo 全部 skill

npx skills add ginlix-ai/LangAlpha --all -g -y

预览 repo 内 skill

npx skills add ginlix-ai/LangAlpha --list

SKILL.md

Frontmatter
{
    "name": "earnings-preview",
    "license": "Derived from anthropics\/financial-services-plugins (Apache-2.0). Modified for langalpha.",
    "description": "Pre-earnings analysis: consensus estimates, key metrics to watch, bull\/base\/bear scenarios"
}

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}/

Version History

  • b544c1b Current 2026-07-05 09:23

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Metadata

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Version
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Hash
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Indexed
2026-07-05 09:23

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