comp-sheet

name	comp-sheet
description	Build an industry comp sheet Excel model with deep operational KPIs
argument-hint	TICKER

Build a multi-company industry comp sheet Excel model for the company specified by the user: $ARGUMENTS

This produces an interactive .xlsx workbook — the kind of comp sheet every analyst on a coverage team maintains. Multi-company, multi-tab, with deep operational KPIs alongside standard financials.

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.

Follow these steps:

1. Company & Peer Setup

Look up the target company by ticker using discover_companies. Capture company_id, latest_calendar_quarter (anchor for all period calculations — see ../data-access.md Section 1.5), and latest_fiscal_quarter. Note the firm name for report attribution (default: "Daloopa") — see ../data-access.md Section 4.5.

Then identify 6-10 comparable companies using the same logic as /comps:

• Direct competitors in the same market
• Business model peers (similar revenue model)
• Size peers (similar market cap range)
• Growth profile peers (similar growth rate)

Look up all peer company_ids via Daloopa. If a peer isn't available in Daloopa, include it with market data only and note the limitation.

List the full peer group with brief justification for each.

2. Deep Data Gathering

For each company (target + all peers), pull from Daloopa:

Calculate 8 quarters backward from latest_calendar_quarter. Pull financials:

• Revenue, Gross Profit, Operating Income, Net Income, Diluted EPS
• Operating Cash Flow, Capital Expenditures, D&A
• Free Cash Flow (compute as OCF - CapEx)
• R&D Expense, SG&A (where available)

Segment revenue breakdown (all available segments, 8 quarters)

Company-specific operational KPIs — use the 9-sector taxonomy to know what to search for:

• SaaS/Cloud: ARR, net revenue retention, RPO/cRPO, customers >$100K, cloud gross margin
• Consumer Tech: DAU/MAU, ARPU, engagement metrics, installed base, paid subscribers
• E-commerce/Marketplace: GMV, take rate, active buyers/sellers, order frequency
• Retail: same-store sales, store count, average ticket, transactions
• Telecom/Media: subscribers, churn, ARPU, content spend
• Hardware: units shipped, ASP, attach rate, installed base
• Financial Services: AUM, NIM, loan growth, credit quality metrics, fee income ratio
• Pharma/Biotech: pipeline stage, patient starts, scripts, market share
• Industrials/Energy: backlog, book-to-bill, utilization, production volumes, reserves

Stock prices & valuation multiples: Use get_stock_prices (see ../data-access.md Section 1.7) to pull prices for ALL companies in a single batch call. Get:

• Current price: dates = 3 most recent calendar days for all company_ids
• Quarter-end prices: dates = quarter-end dates matching the financial periods (for historical multiples)

Then compute valuation metrics by combining stock prices with Daloopa fundamentals:

• Market Cap = Close price × Diluted shares outstanding
• Enterprise Value = Market Cap + Total Debt - Cash
• P/E (trailing) = Market Cap / Net Income (trailing 4Q)
• EV/EBITDA = EV / EBITDA (trailing 4Q)
• P/S = Market Cap / Revenue (trailing 4Q)
• P/B = Market Cap / Total Equity
• EV/FCF = EV / Free Cash Flow (trailing 4Q)
• FCF Yield = FCF (trailing 4Q) / Market Cap
• Dividend Yield = Dividends Paid (trailing 4Q) / Market Cap

For beta, use infra scripts or web search (see ../data-access.md Section 2). For forward multiples, use consensus estimates if available (Section 3).

3. KPI Discovery & Mapping

After pulling data, build the KPI mapping:

• Which KPIs are available for which companies? Build a coverage matrix.
• Group KPIs into categories:
  • Segment Revenue: product/service line breakdowns
  • Growth KPIs: subscriber growth, unit growth, same-store sales growth
  • Unit Economics: ARPU, ASP, take rate, retention
  • Efficiency: R&D % of revenue, SBC % of revenue, CapEx % of revenue
  • Engagement: DAU/MAU, retention, churn
• Flag KPIs that are comparable across peers vs company-specific

4. Compute Derived Metrics

For each company, calculate:

Margins:

• Gross Margin, Operating Margin, Net Margin, FCF Margin (each quarter)

Growth rates:

• Revenue YoY, EPS YoY, segment revenue YoY (each quarter where year-ago data exists)

Capital metrics:

• Net Debt (Total Debt - Cash)
• Net Debt/EBITDA
• Shareholder Yield (Buybacks + Dividends) / Market Cap

Historical multiples (from quarter-end prices pulled in Section 2):

• Compute P/E, EV/EBITDA, P/S, EV/FCF at each quarter-end to show how multiples have trended
• This lets the reader see whether the current multiple is elevated or depressed vs. the company's own history

Implied valuation:

• For each valuation methodology (P/E, EV/EBITDA, P/S, EV/FCF):
  • Peer median multiple × target metric = implied value
  • Convert to implied share price
• Compute median implied price across methodologies

5. Build Context JSON

Structure the data as a multi-company context JSON for the comp_builder:

{
  "target_ticker": "AAPL",
  "as_of_date": "YYYY-MM-DD",
  "companies": [
    {
      "ticker": "AAPL",
      "name": "Apple Inc.",
      "is_target": true,
      "market_data": {
        "price": ..., "market_cap": ..., "enterprise_value": ...,
        "shares_outstanding": ..., "beta": ...,
        "trailing_pe": ..., "forward_pe": ...,
        "ev_ebitda": ..., "price_to_sales": ...,
        "ev_fcf": ..., "dividend_yield": ...
      },
      "periods": ["2024Q1", "2024Q2", ...],
      "financials": {
        "Revenue": {"2024Q1": ..., ...},
        "Gross Profit": {...}, ...
      },
      "margins": {
        "Gross Margin": {"2024Q1": ..., ...}, ...
      },
      "growth": {
        "Revenue Growth YoY": {"2024Q1": ..., ...}, ...
      },
      "kpis": {
        "iPhone Revenue": {"2024Q1": ..., ...}, ...
      },
      "kpi_categories": {
        "Segment Revenue": ["iPhone Revenue", "Services Revenue", ...],
        "Growth KPIs": ["Services Growth YoY"],
        "Efficiency": ["R&D % Revenue", "SBC % Revenue"]
      }
    },
    ...more companies...
  ],
  "implied_valuation": {
    "pe_implied": ...,
    "ev_ebitda_implied": ...,
    "ps_implied": ...,
    "ev_fcf_implied": ...,
    "median_implied": ...
  }
}

Save to reports/.tmp/{TICKERS}_comp_context.json.

6. Render Excel

Build the comp sheet workbook (see ../data-access.md Section 5 for infrastructure): python3 infra/comp_builder.py --context reports/.tmp/{TICKERS}_comp_context.json --output reports/{TICKERS}_comp_sheet.xlsx

The builder creates 8 tabs:

1. Comp Summary — one-pager with all companies, multiples, implied valuation
2. Revenue Drivers — unit economics decomposition per company (trailing 4Q)
3. Operating KPIs — cross-company KPI comparison matrix
4. Financial Summary — side-by-side income statements (trailing 4Q)
5. Growth & Margins — trend analysis (up to 8Q)
6. Valuation Detail — implied prices by methodology, premium/discount
7. Balance Sheet & Capital — leverage and capital returns
8. Raw Data — full quarterly appendix for each company

7. Output

Tell the user where the .xlsx was saved.

Highlight in your summary:

• Target positioning vs peers: Where does it rank on growth, margins, and valuation?
• Most differentiated KPIs: Which operational metrics set the target apart (positive or negative)?
• Implied valuation range: What does the peer group suggest the stock is worth?
• Key risk: What's the biggest vulnerability the comp sheet reveals (e.g., premium valuation with decelerating KPIs, margins below peers, etc.)?

All financial figures in the summary must use Daloopa citation format: $X.XX million