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 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 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.

npx skills add https://github.com/openai/plugins --skill research-note

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, ...}