earnings-preview
Build a pre-earnings briefing for a stock from Yahoo Finance data (yfinance): the upcoming report date and timing, consensus EPS and revenue estimates with their range, the beat/miss track record, analyst ratings and price targets, and what to watch in the print. Use this skill whenever the user is preparing for an upcoming earnings report or asks what the street expects — consensus or whisper numbers, EPS expectations, whether a company will beat, an earnings setup, or an earnings-season preview — and whenever a ticker comes up in the context of upcoming earnings, even without the word "preview". For results that are already out, use earnings-recap.
- Installs
- 3.3K
- Last 24h
- +45
- Trending rank
- #3592
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/himself65/finance-skills --skill earnings-preview- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads earnings-preview automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Earnings Preview Skill
If your host has not already executed the shell probes below (for example, in Codex), run the commands inside the ! wrappers with its shell tool before choosing a setup path. Use the actual output; if shell access is unavailable, report that limitation.
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
ticker = yf.Ticker("AAPL") # replace with actual ticker
# --- Core data ---
info = ticker.info
calendar = ticker.calendar
earnings_dates = ticker.get_earnings_dates(limit=8) # report timestamps; the upcoming one has no Reported EPS yet
hist = ticker.history(period="1mo") # recent price performance
# --- 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 |
get_earnings_dates() | Earnings Date (tz-aware timestamp), EPS Estimate, Reported EPS | Report timing: the upcoming row has no Reported EPS; a time at or after 16:00 ET means after the close, earlier times mean before the open |
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 (indexed by fiscal quarter-end, oldest first) |
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
The briefing should let the user see the setup at a glance. Cover these five areas; if the data for one is missing, say so in a line rather than dropping it.
- Date and context — company, ticker, sector and industry; the report date and whether it lands before the open or after the close; current price with 1-week and 1-month performance; market cap.
- Consensus estimates — a table of this quarter's EPS and revenue consensus with low, high, analyst count, year-ago value, and expected growth. A high/low spread wider than about 20% of consensus signals unusual uncertainty; say so when you see it.
- Beat/miss track record — the last four quarters of estimated vs actual EPS with surprise %, summarized as a beat count and average surprise.
- Analyst sentiment — the rating distribution (strong buy through strong sell) and the price-target range (low, mean, median, high), with the implied upside or downside from the mean target.
- What to watch — the few things the market will focus on in this print, chosen for this company and sector: revenue growth accelerating or decelerating, margins expanding or compressing, line items that moved sharply quarter over quarter, and segment trends where the data has them. This is the judgment part of the briefing.
Step 4: Respond to the User
Open with the headline — the report date and a one-line read of the setup — then the five areas above, using tables where they help. Close with a short read of the overall setup drawn from the estimates, track record, and sentiment, framed as what the street expects rather than a recommendation.
Include the caveats that apply: estimates can change until the report date, past beats don't guarantee future ones, Yahoo Finance consensus can lag real-time providers by a few hours, and 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.
FAQ
What does the earnings-preview skill do?
Build a pre-earnings briefing for a stock from Yahoo Finance data (yfinance): the upcoming report date and timing, consensus EPS and revenue estimates with their range, the beat/miss track record, analyst ratings and price targets, and what to watch in the print. Use this skill whenever the user is preparing for an upcoming earnings report or asks what the street expects — consensus or whisper numbers, EPS expectations, whether a company will beat, an earnings setup, or an earnings-season preview — and whenever a ticker comes up in the context of upcoming earnings, even without the word "preview". For results that are already out, use earnings-recap.
How do I install earnings-preview?
Run npx skills add https://github.com/himself65/finance-skills --skill earnings-preview in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/earnings-preview/ (personal) or .claude/skills/earnings-preview/ (project).
Which AI agents can use earnings-preview?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is earnings-preview safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains earnings-preview?
It is published by himself65 in himself65/finance-skills. It has 3,256 recorded installs.
More from himself65/finance-skills
yfinance-data
himself65/finance-skills
Fetch financial and market data with the yfinance Python library (Yahoo Finance). Use this skill whenever the user wants stock data: current quotes and price history, financial statements (income statement, balance sheet, cash flow), options chains, dividends and splits, earnings and analyst estimates, price targets and ratings, institutional and insider holdings, news, multi-ticker comparisons, stock screens, or sector and industry data. Use it even when the user gives only a ticker symbol (AAPL, MSFT, TSLA) and the intent has to be inferred. For earnings previews or recaps, estimate revisions, valuation, correlation, liquidity, or ETF premium analysis, prefer the dedicated skill.
stock-correlation
himself65/finance-skills
Analyze how stocks move together using Yahoo Finance price history (yfinance): find correlated peers for a ticker, measure correlation, beta, and spread between specific tickers, cluster a group into a correlation matrix, and track rolling or regime-dependent correlation. Use this skill whenever the user asks what moves with a stock, what else drops when it drops, related tickers or sympathy plays, sector or supply-chain peers, pair trading or hedging pairs, beta or relative performance, correlation matrices, co-movement, or rolling/realized correlation — including well-known pairs like AMD/NVDA, GOOGL/AVGO, or LITE/COHR. With a single ticker, assume the user wants its correlated peers.
options-payoff
himself65/finance-skills
Render an interactive options payoff chart — expiry P&L plus Black-Scholes theoretical value — with sliders for strikes, premium, IV, DTE, and spot. Use this skill whenever the user describes or shares an options position or strategy and wants to see how it makes or loses money: vertical, calendar, diagonal, or ratio spreads, butterflies, condors and iron condors, straddles, strangles, covered calls, protective or naked puts, or any multi-leg structure. This includes pasted strikes, premiums, and expiries, requests like "show me the payoff" or "draw the P&L curve", and broker screenshots (IBKR, TastyTrade, Robinhood). Use it even when details are partial — defaults fill the gaps.
earnings-recap
himself65/finance-skills
Analyze a company's most recent (or a specified past) earnings report from Yahoo Finance data (yfinance): actual vs estimated EPS, surprise size, revenue and margin trends, and the stock's price reaction. Use this skill whenever the user asks how earnings went — beat or miss, earnings surprise, quarterly results, the post-earnings move, or an earnings call recap — including casual references to a past report such as "AMZN reported last night" or "how did they do". For an upcoming report, use earnings-preview.
generative-ui
himself65/finance-skills
Design system and templates for interactive HTML/SVG visuals in Claude and Codex, using an available inline renderer or a standalone HTML file. Use this skill whenever the user wants visual or interactive output rather than plain text or markdown: charts and dashboards, diagrams, flowcharts and architecture drawings, mockups, comparison grids, illustrations, interactive explainers ("explain how X works" with a visual), or tools with sliders, toggles, and live-updating displays — including financial data shown visually and requests phrased as "show me", "visualize", "draw", or "chart this".
hormuz-strait
himself65/finance-skills
Check the live status of the Strait of Hormuz from the Hormuz Strait Monitor dashboard: open, restricted, or closed status, tanker transits vs normal, stranded vessels, Brent price impact, war-risk insurance levels, cargo throughput, tanker freight rates, diplomatic developments, and global trade impact. Use this skill whenever the user asks about the Strait of Hormuz or the Persian Gulf in the context of oil, shipping, or geopolitical risk — whether Hormuz is open, chokepoint or transit disruption, Gulf tanker traffic, war-risk premiums, Middle East shipping routes, or energy supply-chain risk.
Popular docs & writing skills
grill-with-docs
mattpocock/skills
A relentless interview to sharpen a plan or design, which also creates docs (ADR's and glossary) as we go.
lark-markdown
open.feishu.cn
飞书 Markdown：查看、创建、上传、编辑和比较飞书中的原生 Markdown 文件。当用户要操作飞书 Markdown 文件，或比较其远端版本及本地草稿时使用。纯本地 Markdown 文件操作不触发本 skill。不负责将 Markdown 导入为飞书在线文档，也不负责文件搜索、权限、评论、移动、删除等云空间管理操作。
writing-beats
mattpocock/skills
Writing, exploit; assemble raw material into a journey of beats, grounding each term before a beat leans on it.
writing-shape
mattpocock/skills
Writing, exploit: shape raw material into an article, paragraph by paragraph.
writing-fragments
mattpocock/skills
Writing, explore: mine raw fragments, no structure yet.
writing-for-agents
mattpocock/skills
Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.