--- name: initiate description: Initiate coverage — generate both research note (HTML) and Excel model (.xlsx) argument-hint: TICKER --- Initiate coverage on 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 the capstone skill that produces both a research note (styled HTML) and an Excel model (React artifact with SheetJS) from a single comprehensive data gathering pass. ## Strategy Rather than running `/research-note` and `/build-model` independently (which would duplicate data gathering), this skill gathers a superset of data once, then renders both outputs. ## Phase 1 — 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 market data using the 3-step resolution: (1) MCP market data tools if available, (2) web search, (3) sensible defaults (see data-access.md Section 2): - Current price, market cap, shares outstanding, beta - Trading multiples (P/E, EV/EBITDA, P/S, P/B) - Risk-free rate (for DCF) Initialize context: `context = {company_name, ticker, date, price, market_cap, firm_name, ...}` ## Phase 2 — Comprehensive Data Gathering Calculate 8-16 quarters backward from `latest_calendar_quarter`. Pull: **Income Statement — search and pull all available:** - Revenue / Net Sales - Cost of Revenue / COGS - Gross Profit - Research & Development - Selling, General & Administrative - Total Operating Expenses - Operating Income - Interest Expense / Income - Pre-tax Income - Tax Expense - Net Income - Diluted EPS - Diluted Shares Outstanding - EBITDA (or compute from Op Income + D&A, label "(calc.)") - D&A **Balance Sheet — search and pull all available:** - Cash and Equivalents - Short-term Investments - Accounts Receivable - Inventory - Total Current Assets - PP&E (net) - Goodwill - Total Assets - Accounts Payable - Short-term Debt - Long-term Debt - Total Liabilities - Total Equity **Cash Flow — search and pull all available:** - Operating Cash Flow - Capital Expenditures - Depreciation & Amortization - Acquisitions - Dividends Paid - Share Repurchases - Free Cash Flow (compute if not direct: OCF - CapEx, label "(calc.)") **Segments:** - Revenue by segment - Operating income by segment (if available) **Geographic:** - Revenue by geography **KPIs:** - All company-specific operating metrics (subscribers, units, ARPU, retention, etc.) **Guidance:** - All guidance series and corresponding actuals **Share Activity:** - Share count, buyback amounts **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 both outputs. Compute margins, YoY growth rates, and ratios for each quarter. ### Cost Structure & Margin Analysis After the core financial pull: - **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. - **OpEx breakdown**: Pull R&D and SG&A separately. Compute R&D % of revenue and SG&A % of revenue trends. - **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. ## Phase 3 — Industry-Specific Deep Dive 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. Build `context.industry_deep_dive` (string) — sector-specific analysis narrative with Daloopa citations, organized by the relevant template above. ## Phase 4 — Peer Analysis Identify 5-8 comparable companies. 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 NTM estimates. Pull peer fundamentals from Daloopa where available (revenue growth, margins). Build `context.comps` and `context.comps_table`. ## Phase 5 — Projections Build forward estimates using the following methodology: - **Revenue:** Start with latest guidance (if available), then decay to long-term growth rate (industry average or historical trend). Apply quarterly seasonality patterns from trailing data. - **Gross Margin:** Mean-revert to trailing 8-quarter average, with adjustment for recent trends or guidance commentary. - **Operating Expenses:** Project as % of revenue, trending toward trailing averages. R&D and SG&A may have different trajectories. - **CapEx:** Project as % of revenue based on trailing 4-8 quarter average and guidance. - **D&A:** Project based on trailing average as % of revenue or PP&E. - **Tax Rate:** Use trailing effective tax rate or guidance. - **Share Count:** Project dilution/buyback based on trailing trends and guidance. - **Working Capital:** Project DSO, DIO, DPO based on trailing averages. Calculate all quarterly projections, then sum to annual. Project 4-8 quarters forward. Describe methodology inline and perform calculations directly. ## Phase 6 — DCF Valuation Calculate: - **WACC:** Use CAPM for cost of equity (Rf + Beta × ERP, where ERP = 6.0%). Cost of debt = Interest Expense / Total Debt. WACC = (E/V × Re) + (D/V × Rd × (1 - Tax Rate)). - **5-year FCF projections:** Annualize from quarterly projections (FCF = Op Cash Flow - CapEx). - **Terminal Value:** Use perpetuity growth at 2.5-3.0%. - **Implied Share Price:** (PV of FCFs + Terminal Value - Net Debt) / Shares Outstanding - **Sensitivity Matrix:** WACC (7 values: -3% to +3% from base) × Terminal Growth (6 values: 1.5% to 4.0%). Build `context.dcf` and `context.dcf_summary` (set `context.has_dcf = true`). ## Phase 7 — 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: - `context.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. - `context.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 - `context.policy_backdrop` (string) — Macro/regulatory context affecting the company. Tariffs, regulation, interest rates, sector-specific policy. Leave empty string if not material. ## Phase 8 — Guidance Track Record Search for guidance series ("guidance", "outlook", "forecast", "estimate", "target"). Pull guidance and corresponding actuals. Apply +1 quarter offset rule for quarterly guidance, same-year rule for annual guidance from Q1/Q2/Q3, next-year rule for annual guidance from Q4. Compute beat/miss rates and patterns. Build `context.guidance` and `context.guidance_table` (set `context.has_guidance = true/false`). ## Phase 9 — What You Need to Believe Build falsifiable bull/bear beliefs: ### 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 Build `context.bull_beliefs`, `context.bull_target`, `context.bear_beliefs`, `context.bear_target`, `context.risk_reward_assessment`. ## Phase 10 — Capital Allocation Pull buyback, dividend, share count, FCF data. Compute shareholder yield, FCF payout ratio, net leverage. Build `context.capital_allocation_commentary`. ## Phase 11 — 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 Build structured tables for both outputs: - `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 12 — Render Research Note (HTML) 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 (8-16 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` **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. ## Phase 13 — Render Excel Model Generate a React artifact that builds the .xlsx file using SheetJS (xlsx library). The artifact should: 1. Create 8 tabs with the following structure: **Tab 1: Income Statement** - Rows: Revenue, COGS, Gross Profit, R&D, SG&A, Total OpEx, Op Income, Interest, Pre-Tax Income, Tax, Net Income, Diluted EPS, Shares - Columns: Historical periods (8-16Q) + Projected periods (4-8Q) - Sub-rows: YoY growth %, margin % where applicable - Header: Company name, ticker, report date - Formatting: Numbers with commas/decimals, percentages, bold headers, frozen panes **Tab 2: Balance Sheet** - Rows: Assets section (Cash, Investments, AR, Inventory, Current Assets, PP&E, Goodwill, Total Assets), Liabilities section (AP, ST Debt, LT Debt, Total Liabilities, Equity) - Columns: Historical + Projected periods - Sub-rows: % of Total Assets for key line items - Same formatting standards **Tab 3: Cash Flow** - Rows: Op Cash Flow, CapEx, Free Cash Flow, Acquisitions, Dividends, Buybacks, Net Change in Cash - Columns: Historical + Projected periods - Sub-rows: FCF yield %, CapEx as % Revenue - Same formatting standards **Tab 4: Segments** - Rows: Revenue by segment, Op Income by segment (if available) - Columns: Historical + Projected periods - Sub-rows: Segment as % of total, segment growth rates - Same formatting standards **Tab 5: KPIs** - Rows: All company-specific operating metrics discovered - Columns: Historical + Projected periods - Sub-rows: YoY growth or relevant unit economics - Same formatting standards **Tab 6: Projections** - Editable assumption inputs (yellow highlighting): Revenue growth %, Gross margin %, Op margin %, CapEx % revenue, Tax rate %, Buyback rate QoQ - Calculated outputs: Projected P&L, BS, CF driven by assumptions - Commentary box explaining methodology - Same formatting standards **Tab 7: DCF** - Inputs: WACC, Terminal Growth, Risk-Free Rate, ERP, Beta, Cost of Debt - FCF Projection (5 years annualized) - Terminal Value calculation - PV calculations - Enterprise Value → Equity Value → Implied Share Price - Sensitivity table: WACC (rows) × Terminal Growth (cols) showing implied price - Color scale: green (upside) to red (downside) vs current price - Same formatting standards **Tab 8: Summary** - Company overview (name, ticker, sector, description) - Current market data (price, market cap, shares, beta) - Valuation summary: DCF implied price, peer-implied range, current price, upside/downside % - Peer trading multiples table - Key model outputs: Trailing revenue, Projected revenue growth, Trailing/Projected margins - Same formatting standards 2. Apply design-system.md formatting conventions: - Number format: $X.Xbn for large numbers, X.X% for percentages, X.Xx for multiples - Color palette: Navy #1B2A4A (headers), Steel Blue #4A6FA5 (sub-headers), Gold #C5A55A (highlights), Green #27AE60 (positive), Red #C0392B (negative) - Bold headers, frozen top row and left column - Yellow fill (#FFEB3B) for editable input cells 3. Include a download button that triggers the .xlsx file download with filename: `{TICKER}_model.xlsx` ## Output Present both deliverables to the user: **Research Note (HTML):** - Present the styled HTML report directly in the response - Tell the user to save the HTML and open in browser for full formatting **Excel Model (React Artifact):** - Present the React artifact with SheetJS - Tell the user to click the download button to save the .xlsx file - Note that yellow cells in the Projections tab are editable inputs **Summary:** - 3-4 sentence executive summary - Key valuation range (DCF implied price + comps range) - Top 3 findings - Bull upside % vs bear downside % risk/reward assessment All financial figures must use Daloopa citation format: [$X.XX million](https://daloopa.com/src/{fundamental_id})