Agent Skillshimself65/finance-skills › earnings-preview

earnings-preview

GitHub

基于Yahoo Finance数据生成股票财报前简报,涵盖预期、历史表现及分析师情绪。

plugins/market-analysis/skills/earnings-preview/SKILL.md himself65/finance-skills

Trigger Scenarios

用户询问特定股票的财报预告或预期 用户提及股票代码并关联 earnings, beat/miss, estimates 等关键词

Install

npx skills add himself65/finance-skills --skill earnings-preview -g -y
More Options

Non-standard path

npx skills add https://github.com/himself65/finance-skills/tree/main/plugins/market-analysis/skills/earnings-preview -g -y

Use without installing

npx skills use himself65/finance-skills@earnings-preview

指定 Agent (Claude Code)

npx skills add himself65/finance-skills --skill earnings-preview -a claude-code -g -y

安装 repo 全部 skill

npx skills add himself65/finance-skills --all -g -y

预览 repo 内 skill

npx skills add himself65/finance-skills --list

SKILL.md

Frontmatter
{
    "name": "earnings-preview",
    "description": "Generate a pre-earnings briefing for any stock using Yahoo Finance data. Use this skill whenever the user wants to prepare for an upcoming earnings report, understand what analysts expect, review a company's beat\/miss track record, or get a quick overview before an earnings call. Triggers include: \"earnings preview for AAPL\", \"what to expect from TSLA earnings\", \"MSFT reports next week\", \"earnings preview\", \"pre-earnings analysis\", \"what are analysts expecting for NVDA\", \"earnings estimates for\", \"will GOOGL beat earnings\", \"earnings beat\/miss history\", \"upcoming earnings\", \"before earnings\", \"earnings setup\", \"consensus estimates\", \"earnings whisper\", \"EPS expectations\", \"what's the street expecting\", \"earnings season preview\", any mention of preparing for or previewing an earnings report, or any request to understand expectations ahead of a company's earnings date. Always use this skill when the user mentions a ticker in context of upcoming earnings, even if they don't say \"preview\" explicitly."
}

Earnings Preview Skill

Generates a pre-earnings briefing using Yahoo Finance data via yfinance. Pulls together upcoming earnings date, consensus estimates, historical accuracy, analyst sentiment, and key financial context — everything you need before an earnings call.

Important: Data is for research and educational purposes only. Not financial advice. yfinance is not affiliated with Yahoo, Inc.


Step 1: Ensure yfinance Is Available

Current environment status:

!`python3 -c "exec('try:\n import yfinance\n print(\'yfinance \' + yfinance.__version__ + \' installed\')\nexcept Exception:\n print(\'YFINANCE_NOT_INSTALLED\')')"`

If YFINANCE_NOT_INSTALLED, install it:

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

If already installed, skip to the next step.


Step 2: Identify the Ticker and Gather All Data

Extract the ticker symbol from the user's request. If they mention a company name without a ticker, look it up. Then fetch all relevant data in one script to minimize API calls.

import yfinance as yf
import pandas as pd
from datetime import datetime

ticker = yf.Ticker("AAPL")  # replace with actual ticker

# --- Core data ---
info = ticker.info
calendar = ticker.calendar

# --- Estimates ---
earnings_est = ticker.earnings_estimate
revenue_est = ticker.revenue_estimate

# --- Historical track record ---
earnings_hist = ticker.earnings_history

# --- Analyst sentiment ---
price_targets = ticker.analyst_price_targets
recommendations = ticker.recommendations

# --- Recent financials for context ---
quarterly_income = ticker.quarterly_income_stmt
quarterly_cashflow = ticker.quarterly_cashflow

What to extract from each source

Data Source Key Fields Purpose
calendar Earnings Date, Ex-Dividend Date When earnings are and key dates
earnings_estimate avg, low, high, numberOfAnalysts, yearAgoEps, growth (for 0q, +1q, 0y, +1y) Consensus EPS expectations
revenue_estimate avg, low, high, numberOfAnalysts, yearAgoRevenue, growth Revenue expectations
earnings_history epsEstimate, epsActual, epsDifference, surprisePercent Beat/miss track record
analyst_price_targets current, low, high, mean, median Street price targets
recommendations Buy/Hold/Sell counts Sentiment distribution
quarterly_income_stmt TotalRevenue, NetIncome, BasicEPS Recent trajectory

Step 3: Build the Earnings Preview

Assemble the data into a structured briefing. The goal is to give the user everything they need in one glance.

Section 1: Earnings Date & Key Info

Report the upcoming earnings date from calendar. Include:

  • Company name, ticker, sector, industry
  • Upcoming earnings date (and whether it's before/after market)
  • Current stock price and recent performance (1-week, 1-month)
  • Market cap

Section 2: Consensus Estimates

Present the current quarter estimates from earnings_estimate and revenue_estimate:

Metric Consensus Low High # Analysts Year Ago Growth
EPS $1.42 $1.35 $1.50 28 $1.26 +12.7%
Revenue $94.3B $92.1B $96.8B 25 $89.5B +5.4%

If the estimate range is unusually wide (high/low spread > 20% of consensus), note that as a sign of high uncertainty.

Section 3: Historical Beat/Miss Track Record

From earnings_history, show the last 4 quarters:

Quarter EPS Est EPS Actual Surprise Beat/Miss
Q3 2024 $1.35 $1.40 +3.7% Beat
Q2 2024 $1.30 $1.33 +2.3% Beat
Q1 2024 $1.52 $1.53 +0.7% Beat
Q4 2023 $2.10 $2.18 +3.8% Beat

Summarize: "AAPL has beaten EPS estimates in 4 of the last 4 quarters by an average of 2.6%."

Section 4: Analyst Sentiment

From recommendations and analyst_price_targets:

  • Current recommendation distribution (Strong Buy / Buy / Hold / Sell / Strong Sell)
  • Price target range: low, mean, median, high vs. current price
  • Implied upside/downside from mean target

Section 5: Key Metrics to Watch

Based on the quarterly financials, highlight 3-5 things the market will focus on:

  • Revenue growth trend (accelerating or decelerating?)
  • Margin trajectory (expanding or compressing?)
  • Any notable line items that changed significantly quarter-over-quarter
  • Segment breakdowns if available in the data

This section requires judgment — think about what matters for this specific company/sector.


Step 4: Respond to the User

Present the preview as a clean, structured briefing:

  1. Lead with the headline: "AAPL reports earnings on [date]. Here's what to expect."
  2. Show all 5 sections with clear headers and tables
  3. End with a brief summary: 2-3 sentences capturing the overall setup (bullish/bearish lean based on estimates, track record, and sentiment — frame as "the street expects" not personal recommendation)

Caveats to include

  • Estimates can change up until the report date
  • Historical beats don't guarantee future beats
  • Yahoo Finance data may lag real-time consensus by a few hours
  • This is not financial advice

Reference Files

  • references/api_reference.md — Detailed yfinance API reference for earnings and estimate methods

Read the reference file when you need exact method signatures or edge case handling.

Version History

  • 81e1f50 Current 2026-08-20 05:22

    修复跨Shell(Bash/PowerShell)动态探针回退机制,确保Python运行时探测兼容性与稳定性。

  • fa526ce 2026-07-25 10:59

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2026-07-25 10:59

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