Deep Research Skill

Parse $ARGUMENTS to determine research type and target, then run the following command.

Execution Command

python3 /Users/kikuchihiroyuki/stock-skills/.claude/skills/market-research/scripts/run_research.py <command> <target>

Natural Language Routing

For natural language → skill selection, see .claude/rules/intent-routing.md.

Output by Research Type

stock (stock research)

• Basic info + valuation (yfinance)
• Latest news (yfinance)
• X sentiment (Grok API)
• Deep analysis: news, earnings catalysts, analyst views, competitive comparison (Grok API)

industry (industry research)

• Trends, key players, growth drivers, risks, regulatory landscape (Grok API)

market (market overview)

• Price movement, macro factors, sentiment, notable events, sector rotation (Grok API)

business (business model analysis)

• Business overview (how the company makes money)
• Business segment breakdown (segment names, revenue share, overview)
• Revenue model (recurring/transactional/subscription, etc.)
• Competitive advantages (entry barriers, brand, technology, moat)
• Key KPIs (metrics investors should focus on)
• Growth strategy (mid-term business plan, M&A, new businesses)
• Business risks (structural risks, dependencies)

About the APIs

Grok API

• Only uses Grok API when XAI_API_KEY environment variable is set
• When not set, generates report from yfinance data only (for stock)

2-Layer Structure

1. Layer 1 (yfinance): Always available (fundamentals, price data)
2. Layer 2 (Grok API): When XAI_API_KEY is set (X posts, web search for deep analysis)

• industry / market / business require Layer 2. Displays a message when not set

API Status Summary (KIK-431)

Displays Grok API status at the end of each report:

Output Supplement

After displaying the script output as-is, Claude should add:

For stock

• Check consistency between fundamentals data and Grok research
• Point out any divergence between value score and market sentiment
• Add additional context relevant to investment decisions

For industry

• Supplement Japan-specific circumstances (regulatory environment, entry barriers, etc.)
• Suggest related stock screening (/screen-stocks integration)

For market

• Estimate impact on portfolio (/stock-portfolio integration)
• Mention comparable past cases if available

For business

• Consider relationship between segment composition and stock valuation
• Revenue model sustainability (recurring is stable, transactional has higher cyclicality, etc.)
• Confirm whether competitive advantages appear in actual financial metrics (ROE, margins, etc.)
• Supplement fundamentals consistency using /stock-report results

Execution Examples

# Stock research
python3 .../run_research.py stock 7203.T
python3 .../run_research.py stock AAPL

# Industry research
python3 .../run_research.py industry semiconductors
python3 .../run_research.py industry "Electric Vehicles"

# Market research
python3 .../run_research.py market nikkei
python3 .../run_research.py market "S&P500"

# Business model analysis
python3 .../run_research.py business 7751.T
python3 .../run_research.py business AAPL

Knowledge Integration Rules (KIK-466)

When get_context.py output contains the following, integrate with research results:

• Relation to held stocks: If the research target sector includes PF holdings, "7203.T is affected → health check recommended"
• Past research (SUPERSEDES): Compare with previous research on the same target. "2 weeks ago: neutral sentiment → now: slightly bullish"
• Investment notes: If there are concerns or thesis for the target stock, cross-check with research results and suggest updates
• Watchlist: If the research target is on the watchlist, add context: "Watching → material for buy timing decision"

Prompting to Record Analysis Conclusions

When a response for stock/business/industry research contains specific investment opinions or thesis-level conclusions:

💡 This analysis has not yet been recorded as an investment note. Would you like to record it as a thesis or concern?