Research Note

Generate a professional research note (.docx) for the company specified by the user: $ARGUMENTS

Before starting, read ../data-access.md for data access methods and ../design-system.md for formatting conventions. Follow the data access detection logic and design system throughout this skill.

This is an orchestrator skill that gathers comprehensive data, then renders a Word document. Work through each phase sequentially, building up a context object that gets written to JSON and rendered.

Phase A — Company Setup

Look up the company by ticker using discover_companies. Capture:

• company_id
• latest_calendar_quarter — anchor for all period calculations (see ../data-access.md Section 1.5)
• latest_fiscal_quarter
• Firm name for report attribution (default: "Daloopa") — see ../data-access.md Section 4.5

Get current stock price, market cap, shares outstanding, beta, and trading multiples for {TICKER} (see ../data-access.md Section 2 for how to source market data).

Initialize context: context = {company_name, ticker, date, price, market_cap, firm_name, ...}

Phase B — Core Financials + Cost Structure

Calculate 8 quarters backward from latest_calendar_quarter. Pull Income Statement metrics:

• Revenue, Gross Profit, Operating Income, Net Income, Diluted EPS
• EBITDA (compute as Op Income + D&A if not direct, label "(calc.)")
• Operating Expenses (SG&A, R&D where available)

Pull Cash Flow & Balance Sheet:

• Operating Cash Flow, CapEx, Free Cash Flow (OCF - CapEx, label "(calc.)")
• Cash, Total Debt, Net Debt
• D&A

For every value returned by get_company_fundamentals, record its fundamental_id (the id field). Store each data point as {value, fundamental_id} so citations can be rendered in the final document.

Compute margins and YoY growth rates for each quarter. Build context.financials with tables. Every Daloopa-sourced number must include its citation link: [$X.XX million](https://daloopa.com/src/{fundamental_id}).

Cost Structure & Margin Analysis (NEW)

After the core financial pull, add:

• COGS driver identification: Search for cost-related series ("cost of goods", "materials", "manufacturing", "input cost"). Identify 3-5 biggest cost line items and their trends over 8Q.
• OpEx breakdown: Pull R&D and SG&A separately. Compute R&D % of revenue and SG&A % of revenue trends over 8Q.
• Margin driver analysis: For each major margin (gross, operating, net), identify what's driving expansion or compression — pricing power, cost leverage, mix shift, or one-time items.

New context keys:

• cost_margin_analysis (string) — narrative explaining what's driving margins, with Daloopa citations
• opex_breakdown_table (dynamic table) — [{metric, Q1, Q2, ...}] rows for R&D, SG&A, Other OpEx, each with absolute values and % of revenue sub-rows

Phase C — KPIs, Segments & Industry Deep Dive

Think about what KPIs matter most for THIS company's business model. Search for:

• Company-specific operating KPIs (subscribers, units, ARPU, retention, etc.)
• Segment revenue breakdown
• Geographic revenue breakdown
• Share count and buyback activity

Pull the same 8 quarters (from latest_calendar_quarter). Build context.kpis and context.segments.

Industry-Specific Deep Dive (NEW)

After the KPI/segment pull, determine the company's sector and apply the relevant analysis template:

• Manufacturing/Industrial: Bookings & backlog, book-to-bill ratio, pipeline by geography, capacity utilization
• SaaS/Technology: ARR/MRR trajectory, net retention rate, customer cohort analysis, RPO/deferred revenue trends
• Retail/Consumer: Same-store sales, store count trajectory, traffic vs ticket decomposition, inventory health
• Financials/Banks: NIM trajectory, provision trends, loan growth by category, capital ratios (CET1, TCE)
• Healthcare/Pharma: Pipeline summary (drug, indication, phase, milestone), product revenue breakdown, patent cliff timeline
• Energy: Production volumes, realized pricing vs benchmark, proved reserves, breakeven analysis

Search for relevant series using discover_company_series with sector-appropriate keywords. Pull available data and build the narrative.

New context key:

• industry_deep_dive (string) — sector-specific analysis narrative with Daloopa citations, organized by the relevant template above

Phase D — Guidance Track Record (follows /guidance-tracker methodology)

Search for guidance series ("guidance", "outlook", "forecast", "estimate", "target"). Pull guidance and corresponding actuals. Apply +1 quarter offset rule. Compute beat/miss rates and patterns. Build context.guidance (set context.has_guidance = true/false).

Phase E — What You Need to Believe (replaces Scenario Analysis)

Using the financial baseline from Phase B:

• Compute trailing 4Q totals for key metrics (revenue, EBITDA, EPS, FCF)
• Analyze segment-level trends and inflections

Build falsifiable bull/bear beliefs instead of probability-weighted scenarios:

Bull Beliefs (To Go Long)

Write 4-6 numbered beliefs, each with:

• One bold statement (the belief itself)
• 2-3 sentences of evidence with Daloopa citations supporting why this could be true
• Each belief must be falsifiable — testable with observable data within 6 months

Example format: "1. Revenue growth re-accelerates to 15%+ as AI monetization scales. Cloud segment grew $X.Xbn last quarter, up X% YoY, with management noting..."

Bear Beliefs (To Go Short)

Same format — 4-6 numbered falsifiable beliefs with evidence for the downside case.

Valuation Math

For each side:

• Bull target: forward multiple × forward earnings estimate = price target. Show the math.
• Bear target: same structure with bear-case multiple and earnings.

Risk/Reward Assessment

• Compare bull upside % vs bear downside % from current price
• If asymmetry is significant (e.g., 30% upside vs 40% downside), flag it explicitly
• State which side has the better risk/reward and why

New context keys:

• bull_beliefs (string) — numbered falsifiable beliefs with evidence
• bear_beliefs (string) — numbered falsifiable beliefs with evidence
• bull_target (string) — price target + valuation math
• bear_target (string) — price target + valuation math
• risk_reward_assessment (string) — asymmetry analysis

Do NOT set these old keys (they are removed from the template): bull_probability, base_probability, bear_probability, bull_description, base_description, bear_description, scenario_chart, bull_price_target, base_price_target, bear_price_target.

Phase F — Capital Allocation (follows /capital-allocation methodology)

Pull buyback, dividend, share count, FCF data. Compute shareholder yield, FCF payout ratio, net leverage. Build context.capital_allocation.

Phase G — Valuation (follows /dcf + /comps methodology)

DCF:

• Get risk-free rate (see ../data-access.md Section 2)
• Calculate WACC using CAPM
• Project FCF 5 years (use projection engine if available per ../data-access.md Section 5, else manual)
• Compute terminal value, implied share price, sensitivity table
• Build context.dcf (set context.has_dcf = true)

Comps:

• Identify 5-8 peers
• Get peer trading multiples (see ../data-access.md Section 2)
• If consensus forward estimates are available (../data-access.md Section 3), include forward multiples
• Compute implied valuation range from peer multiples
• Build context.comps (set context.has_comps = true)

Phase H — Qualitative Research + News & Catalysts

SEC Filing Research

Search SEC filings across multiple queries:

• "risk" / "uncertainty" / "challenge" for risk factors
• "growth" / "opportunity" / "expansion" for growth drivers
• "competition" / "market share" for competitive dynamics
• "outlook" / "guidance" for management's forward view
• Company-specific strategic topics (e.g., "AI", "cloud", etc.)

Extract and organize into:

• context.risks — ranked list of risks with impact/probability
• context.investment_thesis — variant perception, thesis pillars, catalysts
• context.company_description — 2-3 sentence business description