research-note

Generate a professional research note (HTML report) for the company specified by the user named in the user's request. If no ticker or company is provided, ask for one before proceeding. **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 report using the HTML Report Template from `../design-system.md` (full CSS inlined, zero dependencies). ## 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} using the 3-step resolution: (1) MCP market data tools if available, (2) web search, (3) sensible defaults (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 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 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](link) 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 ## 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 using the 3-step resolution: (1) MCP market data tools if available, (2) web search, (3) sensible defaults (see `../data-access.md` Section 2) - Calculate WACC using CAPM - Project FCF 5 years manually (describe methodology inline and perform calculations directly) - 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 using the 3-step resolution: (1) MCP market data tools if available, (2) web search, (3) sensible defaults (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 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 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 — Charts Present all chart data in well-formatted tables. No chart generation needed. ## 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 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 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 template: - `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 HTML Report Using the HTML Report Template from `../design-system.md`, generate a styled HTML report with full CSS inlined. The report should include: **Header Section:** - Company name and ticker - Report date and firm attribution - Five Key Tensions (numbered list) **Section 1: Executive Summary** - Key metrics table - Executive summary narrative - Variant perception **Section 2: Company Overview** - Business description - Investment thesis **Section 3: Recent News & Catalysts** - News timeline - Forward catalysts - Policy backdrop **Section 4: Financial Analysis** - Financials table (8 quarters) - Cost structure & margin analysis - OpEx breakdown table - Segment and geographic tables - Share count table **Section 5: Industry-Specific Analysis** - Industry deep dive narrative **Section 6: Guidance Track Record** - Guidance table and beat/miss analysis (if available) **Section 7: What You Need to Believe** - Bull beliefs with valuation target - Bear beliefs with valuation target - Risk/reward assessment **Section 8: Catalysts** - Forward catalysts - Policy backdrop **Section 9: Capital Allocation** - Capital allocation commentary **Section 10: Valuation** - DCF summary and sensitivity (if available) - Comps commentary (if available) **Section 11: Risks** - Risks summary **Section 12: Monitoring Framework** - Quantitative monitors - Qualitative monitors **Appendix:** - Additional context or data ### 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:** `financials_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`, `has_comps`, `comps_commentary` **Risks:** `risks_summary` **Monitoring:** `monitoring_quantitative`, `monitoring_qualitative` **Appendix:** `appendix_content` ## Output Save the styled HTML report as a local file and summarize the output. Tell the user: - A 3-4 sentence executive summary of the research note - Key findings and valuation range - Tell them where the HTML file was saved and that it can be opened in a browser for full formatting **Citation enforcement:** Every financial figure from Daloopa in the HTML report 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.