Stock Valuation
by @xliucs
Generate comprehensive company valuation reports as polished HTML/PDF. Use when user asks for stock valuation, company analysis, investment thesis, or deep-d...
clawhub install stock-valuationπ About This Skill
name: stock-valuation description: "Generate comprehensive company valuation reports as polished HTML/PDF. Use when user asks for stock valuation, company analysis, investment thesis, or deep-dive on a ticker. Pipeline: (1) run_pipeline.py collects all quantitative data in parallel, (2) agent does web research for Seeking Alpha, X/Twitter, analyst PTs, revenue composition, catalysts/risks, (3) generate_report.py produces polished light-theme HTML with embedded charts, smart callouts, and detailed valuation framework. NOT for quick price checks, daily watchlist alerts, or real-time trading signals."
Stock Valuation Report Generator v3.0
Generate professional valuation reports with ONE command. The pipeline auto-detects peers, runs all 8 data scripts in parallel, and produces a polished HTML report.
Quick Start (One Prompt)
User: "Generate a valuation report for AAPL"
Agent steps:
Step 1: Run Data Pipeline
uv run --with yfinance,matplotlib,lxml python3 $SKILL_DIR/scripts/run_pipeline.py TICKER
Outputs: /tmp/TICKER_data.json
Options:
# Specify peers manually
uv run --with yfinance,matplotlib,lxml python3 $SKILL_DIR/scripts/run_pipeline.py AAPL --peers MSFT GOOG META
Step 2: Qualitative Research (MANDATORY)
Run these searches in parallel and collect the results into /tmp/TICKER_research.json:
#### 2a. Seeking Alpha Research
web_search "{TICKER} seekingalpha analysis 2025 2026"
web_search "seekingalpha {TICKER} strong buy OR turning bullish OR high growth"
#### 2b. X/Twitter Sentiment
# If bird CLI available:
bird search '$TICKER' -n 15 --plain
Otherwise:
web_search "{TICKER} stock twitter sentiment price target"
#### 2c. Analyst Consensus
web_search "{TICKER} analyst price target consensus 2026"
#### 2d. Earnings & Revenue
web_search "{TICKER} latest earnings call revenue composition segments"
web_fetch "BEST_EARNINGS_URL" --maxChars 6000
#### 2e. Catalysts & Risks Synthesize from all research above. Aim for 5-7 catalysts and 5-7 risks (with mitigants).
#### Save as Research JSON
Save to /tmp/TICKER_research.json:
{
"sa_articles": [
{"title": "Article Title", "date": "Dec 2025", "rating": "Strong Buy", "summary": "Key thesis in one sentence"}
],
"twitter_sentiment": [
{"user": "FinanceGuy", "stance": "Bullish", "summary": "Key argument or price target"}
],
"analyst_consensus": {"rating": "Strong Buy", "avg_pt": 200.0, "low_pt": 150.0, "high_pt": 250.0},
"catalysts": [
"Q4 earnings beat could trigger re-rating (reporting Mar 19)",
"Geographic expansion into new markets reducing concentration risk"
],
"risks": [
"Regulatory risk in key markets. Mitigant: diversified across 6+ jurisdictions",
"Trading volume cyclicality. Mitigant: interest income provides stable base"
],
"revenue_composition": [
{"stream": "Product Sales", "amount": "$50B", "pct": "52%", "trend": "Strong growth", "notes": "Core hardware"}
],
"geographic_data": [
{"market": "Americas", "new_accounts_pct": "45%", "avg_deposit": "$50K", "highlights": "Largest market"}
]
}
Step 3: Generate Report
uv run python3 $SKILL_DIR/scripts/generate_report.py /tmp/TICKER_data.json --research /tmp/TICKER_research.json
Outputs: /tmp/TICKER_report.html
Step 4: Convert to PDF & Deliver
/Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome \
--headless --disable-gpu --print-to-pdf=/tmp/TICKER_report.pdf \
--no-pdf-header-footer /tmp/TICKER_report.html
Script Reference
| Script | Deps | Output |
|--------|------|--------|
| run_pipeline.py TICKER [--peers P1 P2] [--output] | yfinance,matplotlib,lxml | Merged JSON β file |
| generate_report.py DATA.json [--output] [--research] | (none) | HTML report |
| fetch_fundamentals.py TICKER [PEERS...] | yfinance | Financials, ratios, peer data |
| fetch_technicals.py TICKER | yfinance | SMAs, RSI, MACD, 52W range |
| fetch_historical_valuation.py TICKER | yfinance,lxml | 5yr P/E history, percentile |
| dcf_model.py TICKER [--wacc] [--growth-*] | yfinance | 10yr DCF bear/base/bull |
| fetch_insiders.py TICKER | yfinance | Insider txns + institutional holders |
| fetch_options.py TICKER | yfinance | P/C ratio, IV, unusual vol |
| fetch_earnings_calendar.py TICKER | yfinance | Next earnings date |
| generate_charts.py TICKER | yfinance,matplotlib | 4 PNGs β /tmp/ |
| detect_peers.py TICKER [--count N] | yfinance | Auto-detected peers |
| filter_tweets.py (stdin) | (none) | Filtered tweet JSON |
Report Sections
The generated report includes all of these (in order): 1. Header (company name, ticker, date, data sources) 2. Earnings Badge (next earnings date) 3. KPI Cards (price, MCap, P/E, margins β 2 rows of 4) 4. Quarterly Trends (with QoQ growth arrows ββ/β/β/β) 5. Revenue Composition (segment breakdown table + insight callout) 6. Geographic Expansion (market-by-market table) 7. Technical Analysis (6-panel: RSI, SMAs, MACD, 52W range) 8. Charts (2Γ2 grid: price+SMA, revenue, margins, PE history) 9. Historical Valuation (5Y PE avg, range, percentile + mean reversion callout) 10. Peer Comparison (full table with highlight row + discount callout) 11. Options Sentiment (4 KPI cards + interpretation) 12. Insider Activity 13. Seeking Alpha Research (bullish/cautious grouping + consensus callout) 14. X/Twitter Sentiment (notable takes + consensus callout) 15. Catalysts (5-7 items, most important first) 16. Risks (5-7 items with mitigants) 17. Valuation Framework (P/E multiples, mean reversion, DCF) 18. Price Target Scenarios (bear/base/bull with math) 19. Investment Thesis (specific, opinionated verdict) 20. Disclaimer 21. Footer
Rules
uv run --with (no pip install needed)π Constraints
uv run --with (no pip install needed)