Research note

Generate a professional research note (as a styled HTML report) 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 styled HTML research note. Work through each phase sequentially, building up a context object that gets rendered directly into the final report.

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 — describe the projection methodology inline (revenue build, margin assumptions, terminal growth) and perform the calculations directly; no external projection engine is used
• 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

News & Catalysts via WebSearch (NEW)

Run 4 WebSearch queries to gather recent external context:

1. "{TICKER} {company_name} news {year}" — recent headlines and developments
2. "{TICKER} analyst upgrade downgrade price target" — sell-side sentiment shifts
3. "{TICKER} catalysts risks" — forward-looking events and risk factors
4. "{company_name} industry outlook {sector}" — macro and industry trends

Source quality (MANDATORY): Follow data-access.md Section 2.5 — cite only primary sources (SEC filings, IR pages, press releases, transcripts) and Tier-1 financial press (Reuters, Bloomberg, WSJ, FT). Never use or cite Yahoo Finance editorial, Benzinga, Seeking Alpha, Motley Fool, Zacks, TipRanks, StockTwits, Reddit, or similar aggregators/blogs.

Organize results into three new context keys:

• news_timeline (string) — 6-10 key events from the last 6-12 months in reverse chronological order. Each event: date, headline, 1-sentence impact, sentiment tag (Positive / Negative / Mixed / Upcoming). Format as a numbered list.

• forward_catalysts (string) — Organized by timeframe:

  • Near-term (0-3 months, HIGH priority): earnings dates, product launches, regulatory decisions
  • Medium-term (3-12 months, MEDIUM priority): strategic milestones, contract renewals, industry events
  • Long-term (1-3 years, LOW priority): secular trends, market expansion, competitive dynamics

• policy_backdrop (string) — Macro/regulatory context affecting the company. Tariffs, regulation, interest rates, sector-specific policy. Leave empty string if not material.

Phase I — Data Presentation Tables

Present all trend and sensitivity data as well-formatted tables (no chart images are generated):

1. Revenue trend table — periods as columns, revenue level + YoY growth as rows
2. Margin trend table — periods as columns, gross/operating/net margin as rows
3. Segment mix table — segments as rows, periods as columns, with % of total mix sub-rows
4. DCF sensitivity table — WACC values as rows, terminal growth values as columns, implied share price at each intersection, current price called out for reference

Set the corresponding table context keys (e.g., context.revenue_trend_table, context.margin_trend_table, context.dcf_sensitivity_table) using the table conventions in design-system.md.

Phase J — Synthesis + Tensions + Monitoring

This is the most judgment-intensive step. Be honest and critical — the reader is a professional investor who needs your real assessment, not a balanced summary.

Core Synthesis

Write:

• Executive Summary: 3-4 sentence TL;DR covering current state, key thesis, valuation view. Include a clear directional view — is this stock attractive, fairly valued, or overvalued at the current price?
• Variant Perception: What does the market think vs what do you see in the data? Where is the consensus wrong? If you agree with consensus, say that too — but explain what could change.
• Key Findings: Top 3-5 most notable data points or trends — prioritize what changes the investment thesis, not just what's interesting
• Red Flags & Concerns: Any quality-of-earnings issues, sustainability questions, or risks the market may be underpricing
• Build context.executive_summary, context.variant_perception

Five Key Tensions (NEW)

Identify the 5 most critical bull/bear debates for this stock. Each tension is a single line that frames both sides. Alternate between bullish-leaning and bearish-leaning tensions. Every tension must reference a specific data point from the analysis.

Format as a numbered list:

1. "[Bullish factor] vs [Bearish factor]" — cite the specific metric
2. "[Bearish factor] vs [Bullish factor]" — cite the specific metric ...etc.

Build context.five_key_tensions (string).

Monitoring Framework (NEW)

Build two monitoring lists for ongoing tracking:

Quantitative Monitors — 5-7 specific metrics with explicit thresholds:

• Format: "Metric: current value → bull threshold / bear threshold"
• Example: "Gross Margin: 45.2% → above 46% confirms pricing power / below 43% signals cost pressure"

Qualitative Monitors — 5-7 factors to watch:

• Management tone shifts on earnings calls
• Competitive dynamics (new entrants, pricing pressure)
• Regulatory developments
• Customer concentration changes
• Capital allocation pivots

Build context.monitoring_quantitative and context.monitoring_qualitative (strings, numbered lists).

Structured Tables

Also build structured tables for the report:

• context.key_metrics_table — [{metric, value, vs_prior}] for the exec summary table
• context.financials_table — [{metric, q1, q2, ...}] for the financial analysis section
• context.segments_table, context.geo_table, context.shares_outstanding_table
• context.opex_breakdown_table — [{metric, q1, q2, ...}] for R&D, SG&A, % of revenue rows
• context.guidance_table, context.comps_table, etc.

Phase K — Render Report

Compile the full context into a single styled HTML research note using the HTML Report Template from design-system.md (full CSS inlined, zero dependencies). Never output raw markdown — the design system explicitly forbids it.

Present the complete HTML report directly in the response — do not save it to a file or serialize the context to disk.

Context Key Checklist

Verify these keys exist before rendering (set empty string if data unavailable):

Cover & Summary: company_name, ticker, date, price, market_cap, five_key_tensions, executive_summary, key_metrics_table

Thesis & Overview: investment_thesis, variant_perception, company_description

News: news_timeline

Financials: revenue_trend_table, financials_table, margin_trend_table, cost_margin_analysis, opex_breakdown_table, segments_table, geo_table, shares_outstanding_table

Industry: industry_deep_dive

Guidance: has_guidance, guidance_track_record

What You Need to Believe: bull_beliefs, bull_target, bear_beliefs, bear_target, risk_reward_assessment

Catalysts: forward_catalysts, policy_backdrop

Capital Allocation: capital_allocation_commentary

Valuation: has_dcf, dcf_summary, dcf_sensitivity_table, has_comps, comps_commentary

Risks: risks_summary

Monitoring: monitoring_quantitative, monitoring_qualitative

Appendix: appendix_content

Output

Present the complete HTML research note directly in the response, followed by:

• A 3-4 sentence executive summary of the research note
• Key findings and valuation range

Citation enforcement: Every financial figure from Daloopa in the report data AND the rendered HTML document must use citation format: [$X.XX million](https://daloopa.com/src/{fundamental_id}). If a number came from get_company_fundamentals, it must have a citation link. No exceptions. Before rendering, verify that every Daloopa-sourced value carries its fundamental_id through to the citation link.