initiate
GitHub针对指定公司启动全面覆盖，一次性获取财务数据并生成研究笔记HTML及Excel模型。先查公司信息与市场数据，再收集多期利润表、资产负债表等核心指标，确保引用准确且避免重复采集。
Trigger Scenarios
Install
npx skills add openai/plugins --skill initiate -g -y
SKILL.md
Frontmatter
{
"name": "initiate",
"description": "Initiate coverage — generate both research note (HTML) and Excel model (.xlsx)"
}
Initiate coverage on the company 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 the capstone skill that produces both a research note (styled HTML) and an Excel model (.xlsx) from a single comprehensive data gathering pass.
Strategy
Rather than running the research-note and build-model skills 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_idlatest_calendar_quarter— anchor for all period calculations (see../data-access.mdSection 1.5)latest_fiscal_quarter- Firm name for report attribution (default: "Daloopa") — see
../data-access.mdSection 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/probabilitycontext.investment_thesis— variant perception, thesis pillars, catalystscontext.company_description— 2-3 sentence business description
News & Catalysts via WebSearch
Run 4 WebSearch queries to gather recent external context:
"{TICKER} {company_name} news {year}"— recent headlines and developments"{TICKER} analyst upgrade downgrade price target"— sell-side sentiment shifts"{TICKER} catalysts risks"— forward-looking events and risk factors"{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 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:
- "[Bullish factor] vs [Bearish factor]" — cite the specific metric
- "[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 tablecontext.financials_table— [{metric, q1, q2, ...}] for the financial analysis sectioncontext.segments_table,context.geo_table,context.shares_outstanding_tablecontext.opex_breakdown_table— [{metric, q1, q2, ...}] for R&D, SG&A, % of revenue rowscontext.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 the .xlsx file directly using the best available spreadsheet-generation workflow. For Codex, prefer bundled spreadsheet tooling or Python/openpyxl when available. The workbook should:
- 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
- Apply
../design-system.mdformatting 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
- Save the workbook as
reports/{TICKER}_model.xlsx
Output
Present both deliverables to the user:
Research Note (HTML):
- Save the styled HTML report to
reports/{TICKER}_initiate_report.html. - Tell the user where the HTML file was saved and that it can be opened in a browser for full formatting.
Excel Model:
- Save the generated Excel model to
reports/{TICKER}_model.xlsx. - Tell the user where the
.xlsxfile was saved. - 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
Version History
- 11c74d6 Current 2026-07-19 09:32
