stock-analysis▌
gracefullight/stock-checker · updated May 16, 2026
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$22
Stock Analysis (v5.0)
Analyze US stocks and cryptocurrencies using Yahoo Finance data. Includes portfolio management, crypto support, and periodic analysis.
Quick Start
IMPORTANT: Pass ONLY the stock ticker symbol(s) as arguments. Do NOT add extra text, headers, or formatting in the command.
Analyze a single ticker:
uv run {baseDir}/scripts/analyze_stock.py AAPL
uv run {baseDir}/scripts/analyze_stock.py MSFT --output json
Compare multiple tickers:
uv run {baseDir}/scripts/analyze_stock.py AAPL MSFT GOOGL
Cryptocurrency Analysis (v5.0)
Analyze top 20 cryptocurrencies by market cap:
uv run {baseDir}/scripts/analyze_stock.py BTC-USD
uv run {baseDir}/scripts/analyze_stock.py ETH-USD SOL-USD
Supported Cryptos: BTC-USD, ETH-USD, BNB-USD, SOL-USD, XRP-USD, ADA-USD, DOGE-USD, AVAX-USD, DOT-USD, MATIC-USD, LINK-USD, ATOM-USD, UNI-USD, LTC-USD, BCH-USD, XLM-USD, ALGO-USD, VET-USD, FIL-USD, NEAR-USD
Crypto Analysis Dimensions:
- Market cap (large/mid/small classification)
- Category (Smart Contract L1, DeFi, Payment, etc.)
- BTC correlation (30-day)
- Momentum (RSI, price range)
- Market context (VIX, general market regime)
Portfolio Management (v5.0)
Create and manage portfolios with mixed assets (stocks + crypto):
# Create portfolio
uv run {baseDir}/scripts/portfolio.py create "My Portfolio"
# Add assets
uv run {baseDir}/scripts/portfolio.py add AAPL --quantity 100 --cost 150.00
uv run {baseDir}/scripts/portfolio.py add BTC-USD --quantity 0.5 --cost 40000 --portfolio "My Portfolio"
# View holdings with current P&L
uv run {baseDir}/scripts/portfolio.py show
# Update/remove assets
uv run {baseDir}/scripts/portfolio.py update AAPL --quantity 150
uv run {baseDir}/scripts/portfolio.py remove BTC-USD
# List/delete portfolios
uv run {baseDir}/scripts/portfolio.py list
uv run {baseDir}/scripts/portfolio.py delete "My Portfolio"
Portfolio Storage: ~/.clawdbot/skills/stock-analysis/portfolios.json
Portfolio Analysis (v5.0)
Analyze all assets in a portfolio with optional period returns:
# Analyze portfolio
uv run {baseDir}/scripts/analyze_stock.py --portfolio "My Portfolio"
# With period returns (daily/weekly/monthly/quarterly/yearly)
uv run {baseDir}/scripts/analyze_stock.py --portfolio "My Portfolio" --period weekly
uv run {baseDir}/scripts/analyze_stock.py -p "My Portfolio" --period monthly
Portfolio Summary includes:
- Total cost, current value, P&L
- Period return (if specified)
- Concentration warnings (>30% in single asset)
- Recommendation summary (BUY/HOLD/SELL counts)
Examples:
- ✅ CORRECT:
uv run {baseDir}/scripts/analyze_stock.py BAC - ✅ CORRECT:
uv run {baseDir}/scripts/analyze_stock.py BTC-USD - ❌ WRONG:
uv run {baseDir}/scripts/analyze_stock.py === BANK OF AMERICA (BAC) - Q4 2025 EARNINGS === - ❌ WRONG:
uv run {baseDir}/scripts/analyze_stock.py "Bank of America"
Use the ticker symbol only (e.g., BAC, not "Bank of America"). For crypto, use the -USD suffix (e.g., BTC-USD).
Analysis Components
The script evaluates eight key dimensions:
- Earnings Surprise (30% weight): Actual vs expected EPS, revenue beats/misses
- Fundamentals (20% weight): P/E ratio, profit margins, revenue growth, debt levels
- Analyst Sentiment (20% weight): Consensus ratings, price target vs current price
- Historical Patterns (10% weight): Past earnings reactions, volatility
- Market Context (10% weight): VIX, SPY/QQQ trends, market regime
- Sector Performance (15% weight): Stock vs sector comparison, sector trends
- Momentum (15% weight): RSI, 52-week range, volume, relative strength
- Sentiment Analysis (10% weight): Fear/Greed Index, short interest, VIX term structure, insider trading, put/call ratio
Sentiment Sub-Indicators:
- Fear & Greed Index (CNN): Contrarian signal (extreme fear = buy opportunity, extreme greed = caution)
- Short Interest: High shorts + squeeze potential = bullish; justified shorts = bearish
- VIX Term Structure: Contango = complacency/bullish; backwardation = stress/bearish
- Insider Activity: Net buying/selling from SEC Form 4 filings (90-day window)
- Put/Call Ratio: High ratio = excessive fear/bullish; low ratio = complacency/bearish
Weights auto-normalize if some components unavailable.
Special Timing Checks:
- Pre-earnings warning (< 14 days): Recommends HOLD instead of BUY
- Post-earnings spike detection (> 15% in 5 days): Flags "gains priced in"
- Overbought conditions (RSI > 70 + near 52w high): Reduces confidence
Timing Warnings & Risk Flags
The script detects high-risk scenarios:
Earnings Timing
- Pre-Earnings Period: If earnings < 14 days away, BUY signals become HOLD
- Post-Earnings Spike: If stock up > 15% in 5 days after earnings, warns "gains may be priced in"
Technical Risk
- Overbought Conditions: RSI > 70 + near 52-week high = high-risk entry
Market Risk
- High VIX: Market fear (VIX > 30) reduces confidence in BUY signals
- Risk-Off Mode (v4.0.0): When safe-havens (GLD, TLT, UUP) rise together, reduces BUY confidence by 30%
- Detects flight to safety across gold, treasuries, and USD
- Triggers when GLD ≥ +2%, TLT ≥ +1%, UUP ≥ +1% (5-day change)
Sector Risk
- Sector Weakness: Stock may look good but sector is rotating out
Geopolitical Risk (v4.0.0)
The script now scans breaking news (last 24h) for crisis keywords and automatically flags affected stocks:
- Taiwan Conflict: Semiconductors (NVDA, AMD, TSM, INTC, etc.) → 30% confidence penalty
- China Tensions: Tech/Consumer (AAPL, QCOM, NKE, SBUX, etc.) → 30% confidence penalty
- Russia-Ukraine: Energy/Materials (XOM, CVX, MOS, CF, etc.) → 30% confidence penalty
- Middle East: Oil/Defense (XOM, LMT, RTX, etc.) → 30% confidence penalty
- Banking Crisis: Financials (JPM, BAC, WFC, C, etc.) → 30% confidence penalty
If a ticker is not in the affected list but its sector is exposed, applies a 15% confidence penalty.
Example Alert:
⚠️ SECTOR RISK: Tech supply chain and consumer market exposure (detected: china, tariff)
Breaking News Alerts (v4.0.0)
- Scans Google News RSS for crisis keywords (war, recession, sanctions, disasters, etc.)
- Displays up to 2 breaking news alerts in caveats (last 24 hours)
- Uses 1-hour cache to avoid excessive API calls
Output Format
Default (text): Concise buy/hold/sell signal with 3-5 bullet points and caveats
JSON: Structured data with scores, metrics, and raw data for further analysis
Limitations
- Data freshness: Yahoo Finance may lag 15-20 minutes
- Sentiment data staleness:
- Short interest data lags ~2 weeks (FINRA reporting schedule)
- Insider trades may lag filing by 2-3 days
- VIX term structure only updates during futures trading hours
- Breaking news limitations (v4.0.0):
- Google News RSS may lag by 15-60 minutes
- Keyword matching may have false positives/negatives
- Does not analyze sentiment, only detects keywords
- 1-hour cache means alerts may be slightly stale
- Missing data: Not all stocks have analyst coverage, options chains, or complete fundamentals
- Execution time: 3-5s per stock with async parallel fetching and caching (shared indicators cached for 1h)
- Disclaimer: All outputs include prominent "not financial advice" warning
- US markets only: Non-US tickers may have incomplete data
Error Handling
The script gracefully handles:
- Invalid tickers → Clear error message
- Missing analyst data → Signal based on available metrics only
- API failures → Retry with exponential backoff, fail after 3 attempts
How to use stock-analysis on Cursor
AI-first code editor with Composer
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 stock-analysis
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches stock-analysis from GitHub repository gracefullight/stock-checker and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate stock-analysis. Access the skill through slash commands (e.g., /stock-analysis) 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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★28 reviews- ★★★★★Chaitanya Patil· Dec 8, 2024
stock-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Piyush G· Nov 27, 2024
Registry listing for stock-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ishan Anderson· Oct 22, 2024
stock-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Shikha Mishra· Oct 18, 2024
stock-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Camila Sethi· Sep 5, 2024
stock-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Camila Reddy· Sep 1, 2024
stock-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Diego Mensah· Aug 24, 2024
Keeps context tight: stock-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Camila Harris· Aug 20, 2024
We added stock-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Chinedu Martinez· Jul 27, 2024
Useful defaults in stock-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Tariq Robinson· Jul 15, 2024
stock-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
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