yfinance-data

himself65/finance-skills · updated May 22, 2026

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$npx skills add https://github.com/himself65/finance-skills --skill yfinance-data
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summary

Fetches financial and market data from Yahoo Finance using the yfinance Python library.

skill.md

yfinance Data Skill

Fetches financial and market data from Yahoo Finance using the yfinance Python library.

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


Step 1: Ensure yfinance Is Available

Current environment status:

!`python3 -c "import yfinance; print('yfinance ' + yfinance.__version__ + ' installed')" 2>/dev/null || echo "YFINANCE_NOT_INSTALLED"`

If YFINANCE_NOT_INSTALLED, install it before running any code:

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

If yfinance is already installed, skip the install step and proceed directly.


Step 2: Identify What the User Needs

Match the user's request to one or more data categories below, then use the corresponding code from references/api_reference.md.

User Request Data Category Primary Method
Stock price, quote Current price ticker.info or ticker.fast_info
Price history, chart data Historical OHLCV ticker.history() or yf.download()
Balance sheet Financial statements ticker.balance_sheet
Income statement, revenue Financial statements ticker.income_stmt
Cash flow Financial statements ticker.cashflow
Dividends Corporate actions ticker.dividends
Stock splits Corporate actions ticker.splits
Options chain, calls, puts Options data ticker.option_chain()
Earnings, EPS Analysis ticker.earnings_history
Analyst price targets Analysis ticker.analyst_price_targets
Recommendations, ratings Analysis ticker.recommendations
Upgrades/downgrades Analysis ticker.upgrades_downgrades
Institutional holders Ownership ticker.institutional_holders
Insider transactions Ownership ticker.insider_transactions
Company overview, sector General info ticker.info
Compare multiple stocks Bulk download yf.download()
Screen/filter stocks Screener yf.Screener + yf.EquityQuery
Sector/industry data Market data yf.Sector / yf.Industry
News News ticker.news

Step 3: Write and Execute the Code

General pattern

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

import yfinance as yf

ticker = yf.Ticker("AAPL")
# ... use the appropriate method from the reference

Key rules

  1. Always wrap in try/except — Yahoo Finance may rate-limit or return empty data
  2. Use yf.download() for multi-ticker comparisons — it's faster with multi-threading
  3. For options, list expiration dates first with ticker.options before calling ticker.option_chain(date)
  4. For quarterly data, use quarterly_ prefix: ticker.quarterly_income_stmt, ticker.quarterly_balance_sheet, ticker.quarterly_cashflow
  5. For large date ranges, be mindful of intraday limits — 1m data only goes back ~7 days, 1h data ~730 days
  6. Print DataFrames clearly — use .to_string() or .to_markdown() for readability, or select key columns

Valid periods and intervals

Periods 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
Intervals 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo

Step 4: Present the Data

After fetching data, present it clearly:

  1. Summarize key numbers in a brief text response (current price, market cap, P/E, etc.)
  2. Show tabular data formatted for readability — use markdown tables or formatted DataFrames
  3. Highlight notable items — earnings beats/misses, unusual volume, dividend changes
  4. Provide context — compare to sector averages, historical ranges, or analyst consensus when relevant

If the user seems to want a chart or visualization, combine with an appropriate visualization approach (e.g., generate an HTML chart or describe the trend).


Reference Files

  • references/api_reference.md — Complete yfinance API reference with code examples for every data category

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

how to use yfinance-data

How to use yfinance-data on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add yfinance-data
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/himself65/finance-skills --skill yfinance-data

The skills CLI fetches yfinance-data from GitHub repository himself65/finance-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/yfinance-data

Reload or restart Cursor to activate yfinance-data. Access the skill through slash commands (e.g., /yfinance-data) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.839 reviews
  • Pratham Ware· Dec 28, 2024

    Registry listing for yfinance-data matched our evaluation — installs cleanly and behaves as described in the markdown.

  • James Farah· Dec 24, 2024

    yfinance-data is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Emma Ndlovu· Dec 20, 2024

    Useful defaults in yfinance-data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Diya Johnson· Dec 12, 2024

    Solid pick for teams standardizing on skills: yfinance-data is focused, and the summary matches what you get after install.

  • Ama Wang· Nov 19, 2024

    Keeps context tight: yfinance-data is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Layla Chen· Nov 15, 2024

    Solid pick for teams standardizing on skills: yfinance-data is focused, and the summary matches what you get after install.

  • Layla Yang· Nov 7, 2024

    I recommend yfinance-data for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mia Okafor· Nov 3, 2024

    yfinance-data is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Shikha Mishra· Oct 26, 2024

    yfinance-data has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ira Jain· Oct 26, 2024

    yfinance-data reduced setup friction for our internal harness; good balance of opinion and flexibility.

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