Daloopa

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---
name: build-model
description: Build a multi-tab Excel financial model
---

Build a comprehensive Excel financial model (.xlsx) for 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 skill gathers all available financial data and builds a multi-tab Excel model saved as `reports/{TICKER}_model.xlsx`.

## 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 current stock price, market cap, shares outstanding, beta, and trading multiples for {TICKER}. Use the 3-step resolution: (1) MCP market data tools if available, (2) web search, (3) sensible defaults (see `../data-access.md` Section 2).

## Phase 2 — Comprehensive Data Pull
Calculate periods backward from `latest_calendar_quarter`. Pull as much data as Daloopa has for this company. Target 8-16 quarters.

**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)
- 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)

**Segments:**
- Revenue by segment
- Operating income by segment (if available)

**KPIs:**
- All company-specific operating metrics

**Guidance:**
- All guidance series and corresponding actuals

## Phase 3 — Market Data & Peers
- Identify 5-8 peers and get their trading multiples using the same 3-step resolution: (1) MCP market data tools, (2) web search, (3) sensible defaults
- Get risk-free rate using the same 3-step resolution
- If consensus forward estimates are available (`../data-access.md` Section 3), include NTM estimates for peers

## Phase 4 — 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.

## Phase 5 — DCF Inputs
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 

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---
name: bull-bear
description: Bull/bear/base case scenario framework for a given company
---

Build a bull/bear/base case scenario framework for 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.

Follow these steps:

## 1. Company Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 1b. Current Stock Price
Get the current stock price using `get_stock_prices` (see `../data-access.md` Section 1.7). Pass `company_id` and `dates` for the 3 most recent calendar days — use the most recent returned close price. This is the anchor for scenario comparison: each scenario's implied value will be compared against this price to show upside/downside.

## 2. Historical Financial Baseline
Calculate 8 quarters backward from `latest_calendar_quarter`. Pull:
- Revenue
- Gross Profit / Gross Margin %
- Operating Income / Operating Margin %
- EBITDA (if not reported, compute as Operating Income + D&A — label "(calc.)")
- Net Income
- Diluted EPS
- Operating Cash Flow
- CapEx
- Free Cash Flow (compute as OCF - CapEx — label "(calc.)")
- Segment-level revenue breakdowns
- Geographic revenue breakdowns

Compute trailing 4-quarter totals for revenue, EBITDA, net income, EPS, and FCF — these are the baseline the scenarios build from.

Flag any one-time items that distort quarters.

## 3. Key Operating KPIs
First, think about what the most important KPIs are for THIS specific company based on its business model and what drives its valuation. For example:
- **SaaS/cloud**: ARR, net revenue retention, RPO/cRPO, customers >$100K
- **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
- **Financial services**: AUM, NIM, loan growth, credit quality metrics
- **Pharma/biotech**: pipeline stage, patient starts, scripts, market share

Search for those specific KPIs by name and pull them. These are the building blocks for bottoms-up scenario math.

Also pull capital allocation data: share count, buyback amounts, dividends.

## 4. Qualitative Research
Search SEC filings/documents across multiple queries. If any search returns empty, try alternative keywords before giving up.
- **Risk factors**: Try "risk", "uncertainty", "challenge"; fallback to "adverse", "headwind"
- **Growth drivers**: Try "growth", "opportunity", "expansion"; fallback to "momentum", "strong demand"
- **Competitive dynamics**: Try "competition", "market share"; fallback to "competitive"
- **Management outlook**: Try "outlook", "guidance", "expect"; fallback to "anticipate", "forward"
- **Capital allocation**: Try "repurchase", "dividend"; fallback to "buyback", "capital return"
- **Macro/regulatory**: Try "tariff", "regulatory"; fallback to "geopolitical", "compliance"

## 5. Consensus Positioning (if available)
If consensus estimates are available (see `../data-access.md` Section 3), note:
- Where consensus revenue/EPS sits relative to your base case
- Whether the market is positioned closer to your bull or bear case
- Recent estimate revision trends (optimistic vs pessimistic drift)

If consensus data is not available, skip this section.

## 6. Construct Three Scenarios

For each scenario, build a **bottoms-up revenue model** showing key segment or product-level assumpti

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---
name: capital-allocation
description: Deep dive into capital deployment, buybacks, dividends, and shareholder
  yield
---

Perform a deep dive into capital allocation for 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.

Follow these steps:

## 1. Company Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 2. Market Data
Get the current stock price, market cap, and shares outstanding for {TICKER} (see `../data-access.md` Section 2 for how to source market data in your environment).
- This is needed to compute yields and per-share metrics

If market data is unavailable, note that market-derived metrics (yields, etc.) cannot be computed and proceed with Daloopa data only.

## 3. Capital Allocation Data
Calculate 8 quarters backward from `latest_calendar_quarter`. Pull:

**Share Count & Buybacks:**
- Diluted shares outstanding
- Share repurchase amounts (dollars)
- Shares retired/repurchased (units, if available)

**Dividends:**
- Dividends per share
- Total dividend payments
- Special dividends (if any)

**Cash Flow:**
- Operating Cash Flow
- Capital Expenditures
- Free Cash Flow (compute as OCF - CapEx, label "(calc.)")
- D&A (for reference)

**Balance Sheet:**
- Cash and equivalents
- Short-term investments / marketable securities
- Total debt (short + long term)
- Net debt (compute as Total Debt - Cash - Investments, label "(calc.)")

**M&A / Investments:**
- Search for "acquisition", "purchase of business", "investment" in series
- Pull any available M&A-related series

## 4. Compute Capital Allocation Metrics
Calculate for each quarter where data is available:

**Shareholder Returns:**
- Total Buyback Amount
- Total Dividend Amount
- Total Shareholder Return = Buybacks + Dividends
- Shareholder Yield = (Buybacks + Dividends) / Market Cap (annualized)
- Buyback Yield = Buybacks / Market Cap (annualized)
- Dividend Yield = Dividends / Market Cap (annualized)

**FCF Deployment:**
- FCF Payout Ratio = Total Shareholder Return / FCF
- CapEx as % of Revenue
- CapEx as % of OCF
- FCF Margin = FCF / Revenue

**Leverage:**
- Net Debt / EBITDA (if EBITDA available; compute from Operating Income + D&A if needed)
- Net Debt / Equity
- Interest Coverage = Operating Income / Interest Expense (if available)
- Cash as % of Market Cap

**Share Count Dynamics:**
- QoQ share count change
- YoY share count change
- Implied buyback rate (QoQ % reduction)
- At current buyback rate, years to retire X% of shares

## 5. Qualitative Research
Search SEC filings for capital allocation strategy and context. Try multiple searches:
- **Buyback program**: Try "repurchase program", "share repurchase"; fallback to "buyback", "authorization"
- **Dividend policy**: Try "dividend", "capital return"; fallback to "distribution", "payout"
- **M&A strategy**: Try "acquisition", "strategic"; fallback to "purchase", "investment"
- **Capital priorities**: Try "capital allocation", "priorities"; fallback to "deploy", "balance sheet"
- **Debt management**: Try "debt", "refinance"; fallback to "leverage", "maturity"

Extract:
- Board-authorized buyback programs (remaining authorization amount)
- Dividend policy (commitment to growth, payout ratio targets)
- M&A philosophy (bolt-on vs transformational, deal pipeline commentary)
- Management's stated capital allocation framework and priorities
- Any changes in capital allocation strategy
- Direct quotes with document citations

## 6. Historical Analysis & Value Judgment
Analyze the 8-quarter t

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---
name: comps
description: Trading comparables analysis with peer multiples and implied valuation
---

Build a trading comparables analysis for 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.

Follow these steps:

## 1. Company Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 2. Identify Peer Group
Based on the company's business model, sector, size, and competitive landscape, identify 5-10 comparable companies. Consider:
- **Direct competitors** in the same market
- **Business model peers** (similar revenue model even if different sector)
- **Size peers** (similar market cap range)
- **Growth profile peers** (similar growth rate)

Prioritize relevance over size matching. A direct competitor at a different scale is more useful than a similar-sized company in a different industry.

List the peer tickers and briefly justify each selection (1 sentence).

## 3. Target Company Fundamentals
Calculate 4 quarters backward from `latest_calendar_quarter`. Pull from Daloopa for the target company:
- Revenue (compute trailing 4Q total)
- EBITDA (compute trailing 4Q; if not available, use Op Income + D&A, label "(calc.)")
- Net Income (trailing 4Q)
- Diluted EPS (trailing 4Q sum)
- Free Cash Flow (trailing 4Q; compute as OCF - CapEx, label "(calc.)")
- Revenue YoY growth (most recent quarter)
- Operating Margin (most recent quarter)
- Net Margin (most recent quarter)

## 4. Stock Prices & Valuation Multiples
Use `get_stock_prices` (see `../data-access.md` Section 1.7) to pull current prices for the target AND all peers in a single batch call — pass all `company_ids` together with `dates` = 3 most recent calendar days.

Compute valuation multiples by combining stock prices with the fundamentals pulled in Sections 3 and 5:
- **Market Cap** = Close price × Diluted shares outstanding
- **Enterprise Value** = Market Cap + Total Debt - Cash (from Daloopa balance sheet if available)
- **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
- **FCF Yield** = FCF (trailing 4Q) / Market Cap
- **Dividend Yield** = Dividends Paid (trailing 4Q) / Market Cap

For beta, PEG ratio, and forward multiples, use infra scripts, consensus data, or web search (see `../data-access.md` Sections 2-3).

If a peer isn't in Daloopa (no `company_id`), fall back to `../data-access.md` Section 2 resolution order for market data. If a peer ticker fails (delisted, no data), drop it and note why.

## 5. Peer Fundamentals from Daloopa
For each peer that is available in Daloopa:
- Look up the company
- Calculate 4 quarters backward from `latest_calendar_quarter`. Pull revenue, operating income, net income for those periods.
- Compute revenue growth YoY, operating margin, net margin

For peers not in Daloopa, rely on market data multiples only (see `../data-access.md` Section 2) and note the data source limitation.

## 5.5. Peer Operational KPIs

For each company (target + all peers available in Daloopa), discover and pull company-specific operational KPIs. Use the sector taxonomy below 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 co

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---
name: comp-sheet
description: Build an industry comp sheet Excel model with deep operational KPIs
---

Build a multi-company industry comp sheet Excel model for the company named in the user's request. If no ticker or company is provided, ask for one before proceeding.

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 the comps skill:
- **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 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 

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---
name: dcf
description: Discounted cash flow valuation with sensitivity analysis
---

Build a discounted cash flow (DCF) valuation for 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.

Follow these steps:

## 1. Company Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 2. Market Data
Get market-side inputs for {TICKER} (see `../data-access.md` Section 2 for how to source market data in your environment):
- Current price, market cap, shares outstanding, beta
- 10Y Treasury yield (risk-free rate for WACC)

If market data is unavailable, use reasonable defaults: beta=1.0, risk-free rate=4.5%, and note the assumptions.

## 3. Historical Financials from Daloopa
Calculate 8 quarters backward from `latest_calendar_quarter`. Pull:
- Revenue
- Operating Income
- Net Income
- Diluted EPS
- Operating Cash Flow
- Capital Expenditures
- Free Cash Flow (compute as OCF - CapEx, label "(calc.)")
- Depreciation & Amortization
- Tax expense and pre-tax income (for effective tax rate)
- Interest expense (for cost of debt)
- Total debt (short + long term)
- Cash and equivalents
- Shares outstanding

Also pull segment revenue and any available guidance series.

## 4. Calculate WACC

**Cost of Equity (CAPM):**
- Risk-free rate (Rf) = 10Y Treasury from market data
- Equity risk premium (ERP) = 5.5% (standard assumption)
- Beta from market data (or 1.0 default)
- Cost of equity = Rf + Beta × ERP

**Cost of Debt:**
- If interest expense and total debt available: Cost of debt = Interest Expense / Average Total Debt
- After-tax cost of debt = Cost of debt × (1 - effective tax rate)
- If not available, use 5.0% pre-tax as default

**Capital Structure:**
- Market cap for equity weight
- Total debt for debt weight
- WACC = (E/V) × Re + (D/V) × Rd × (1-t)

Show all inputs and the resulting WACC clearly.

## 5a. KPI-Driven Revenue Build (Preferred)

Before projecting top-down, attempt a bottoms-up revenue build using operational KPIs. This produces a significantly more defensible DCF — a top-down trend decay is a guess; a bottoms-up KPI build is analysis.

**Discover segment and KPI data:**
Pull segment revenue breakdown + segment-specific KPIs for the target company. Use the 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

**Build bottoms-up projections per segment:**
For each segment with KPI data, project revenue using unit economics:
- Hardware segments: projected units × projected ASP
- Subscription segments: projected subscribers × projected ARPU (net of churn)
- Marketplace segments: projected GMV × projected take rate
- Services/recurring: apply growth rate informed by retention metrics and customer adds

Sum segment projections to get total revenue for each of 5 years. Show the build clearly so the reader can chal

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---
name: earnings-flash
description: Rapid first-read earnings flash for a given company
---

Generate a rapid earnings flash 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.

This is a lightweight, speed-focused version of the earnings-review skill — designed for a quick first read within minutes of a filing. It pulls just enough context from Daloopa to frame BEAT/MISS verdicts, then focuses on what's new and surprising.

**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.

## 1. Company Lookup

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

## 2. Prior Quarter Context (4 Quarters)

Calculate 4 quarters backward from `latest_calendar_quarter`. Search for and pull these core metrics:

**Income Statement:**
- Revenue / Net Sales
- Gross Profit
- Operating Income / EBIT
- Net Income
- Diluted EPS

**Cash Flow:**
- Operating Cash Flow
- Free Cash Flow (or CapEx to compute it)

This is lighter than the earnings-review skill (4Q vs 8Q, no cost structure breakdown). The goal is just enough history to frame the latest quarter's results — not a full trend analysis.

## 3. Company-Specific KPIs

Think about the 3-5 most important KPIs for THIS company based on its business model. Search for those specific KPIs and pull for the same 4-quarter period. Also search for:
- Segment/product revenue breakdown
- Geographic revenue breakdown (if material)

Keep this targeted — discover the critical operating metrics, not everything available.

## 4. Guidance Series

Search for guidance series (revenue guidance, EPS guidance, margin guidance, any KPI guidance). If available, pull guidance data for the latest 2 quarters so you can compare the most recent actual results against what management guided.

CRITICAL: Apply +1 quarter offset — guidance from Q(N) applies to Q(N+1) results.

## 5. Get the Earnings Document

Use `search_documents` to find the most recent earnings-related filing. Search strategy:
1. Search for keywords `["results", "earnings"]` in the latest 1-2 calendar quarters
2. If that returns nothing, try `["revenue"]` or `["financial"]` as broader terms

Read the document content from the search results. Focus on:
- **Earnings transcripts**: Full document (management commentary, prepared remarks, Q&A)
- **10-Q / 10-K**: Financial statements and MD&A sections
- **8-K**: Full document (short event-driven filings)

If no document is found, proceed with the MCP fundamentals data only and note "No earnings document found — analysis based on financial data only."

## 5b. Stock Price Context
Get the current stock price using `get_stock_prices` (see `../data-access.md` Section 1.7) — pass `company_id` and `dates` for the 3 most recent calendar days. Also pull prices around the earnings date (1 day before to 3 days after the `latest_calendar_quarter` end + ~30-45 days) to compute the post-earnings reaction. Include the next-day move percentage in the Executive Flash section.

## 6. Executive Flash

Write 3-5 bullet-point verdicts. Each bullet MUST compare the latest quarter's results against prior periods from Step 2 and/or guidance from Step 4. Format:

**[BEAT/MISS/INLINE/MIXED] | Key number (YoY change) | One-sentence context**

Examples:
- **BEAT | Revenue $95.4bn (+6.1% YoY) | Acceleration from +4.8% last quarter driven by iPhone 16 cycle**
- **MISS | EPS $1.46 vs $1.52 prior year | Higher opex from AI investments weighed on margins**
- **GUIDANCE UP | FY2026 revenue guided $400-405bn | Management raised full-year outlook on cloud strength**

Use Dal

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---
name: earnings-prep
description: Pre-earnings preparation report for the night before a company reports
---

Generate a pre-earnings preparation 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.

This is the note a L/S equity analyst reads the night before a company reports — it tells them exactly what to focus on when the print drops.

**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 Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

Determine the **upcoming quarter** — the one AFTER `latest_calendar_quarter`. This is the quarter the company is about to report. All analysis is oriented around preparing the analyst for this print.

## 2. Last Quarter Recap
Pull the most recent quarter's full financials from Daloopa. Calculate 4 quarters backward from `latest_calendar_quarter` (for YoY context).

**Pull:**
- Revenue, Gross Profit, Operating Income, EBITDA, Net Income, Diluted EPS
- Operating Cash Flow, CapEx, FCF (calc.)
- Segment/product revenue breakdown
- Company-specific KPIs (use the business-model taxonomy: SaaS → ARR/NRR/RPO; Consumer → DAU/ARPU; E-commerce → GMV/take rate; etc.)

**Summarize the story of last quarter in 3-5 bullets:**
- What beat expectations (guidance or consensus)?
- What missed or disappointed?
- What was the stock reaction? (use `get_stock_prices` per `../data-access.md` Section 1.7 to get the actual next-day move; supplement with WebSearch for narrative context if needed)
- What narrative emerged from the call? (e.g., "AI monetization acceleration," "margin expansion story intact," "consumer weakness")
- What was the single most debated metric?

This is the baseline everyone on the upcoming call will be anchoring to.

## 3. Outstanding Guidance for Upcoming Quarter
Search for ALL guidance series using keywords: "guidance", "outlook", "estimate", "forecast", "target". Apply the +1 quarter offset to identify which guidance applies to the upcoming print:
- CRITICAL: Guidance from Q(N) earnings call applies to Q(N+1) results
- The guidance issued during the `latest_calendar_quarter` earnings call is what applies to the upcoming quarter

**Pull and present:**
- Revenue guidance (point estimate or range)
- EPS guidance
- Margin guidance (gross, operating, EBITDA)
- CapEx guidance
- Segment-level guidance (if available)
- KPI guidance (subscriber adds, unit volumes, ARPU targets, etc.)

**Search filings for directional/qualitative guidance:**
- Search documents for: "expect", "anticipate", "similar to", "consistent with"
- Search documents for: "low single digit", "mid single digit", "double digit", "sequential"
- Search documents for: "headwind", "tailwind", "conservatively", "assumes"
- Capture exact management quotes with document citations

**Flag any guidance updates between quarters:**
- Search for "pre-announce", "update", "revise" in the most recent quarter's filings
- Check if the company issued an 8-K updating guidance after the last earnings call

Present all guidance in a single table: Metric | Guidance Value | Source Quarter | Type (Quantitative/Directional).

## 4. Guidance Credibility & Whisper Number
This section MUST be built entirely from Daloopa data — guidance series AND actual result series pulled via `get_company_fundamentals`. Do not use web search or estimates for this analysis.

**Step 1: Pull 8 quarters of guidance data.**
You already discovered guidance series in Section 3. Now pull ALL of those guidance series for the last 8 quarters

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---
name: earnings-review
description: Full earnings analysis with guidance tracking for a given company
---

Perform a comprehensive earnings analysis for 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.

Follow these steps:

## 1. Company Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 2. Core Financial Metrics
Calculate 8 quarters backward from `latest_calendar_quarter`. Search for these metrics, then pull:

**Income Statement:**
- Revenue / Net Sales
- Gross Profit
- Operating Income / EBIT
- EBITDA (if not reported, compute as Operating Income + D&A — label it "EBITDA (calc.)")
- Net Income
- Diluted EPS
- Operating Expenses (SG&A, R&D where available)

**Cash Flow & Balance Sheet:**
- Operating Cash Flow
- CapEx (Purchases of property, plant and equipment)
- Free Cash Flow (compute as Operating Cash Flow - CapEx — label it "FCF (calc.)")
- D&A (needed for EBITDA calc if not directly reported)

For any derived/computed metric, mark it with "(calc.)" so the reader knows it's not directly sourced.

Flag any one-time items that distort a quarter (e.g., tax charges, impairments, litigation settlements) with a footnote so YoY comparisons aren't misleading.

## 3. Company-Specific KPIs
First, think about what the most important KPIs are for THIS specific company based on its business model and what drives its valuation. For example:
- **SaaS/cloud**: ARR, net revenue retention, RPO/cRPO, customers >$100K
- **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
- **Financial services**: AUM, NIM, loan growth, credit quality metrics
- **Pharma/biotech**: pipeline stage, patient starts, scripts, market share

Then search for those specific KPIs by name, plus cast a wider net for anything else available. Also search for:
- Segment/product revenue breakdown
- Geographic revenue breakdown

Pull for the same 8-quarter period. If some KPIs only have data for recent quarters, include what's available and note the gap.

## 4. Growth & Margins
Calculate and present:
- YoY revenue growth for each of the last 4 quarters (not just one)
- Gross margin, operating margin, EBITDA margin, net margin trends over 8 quarters
- EPS growth YoY for each of the last 4 quarters
- Segment revenue YoY growth for the most recent quarter
- Geographic revenue YoY growth for the most recent quarter
- KPI growth rates where applicable

If the company has strong seasonality (e.g., retail Q4 holiday, back-to-school, cyclical patterns), add a note so the reader interprets QoQ swings correctly.

## 4.5. Cost Structure & Margin Drivers
Decompose what's driving margin trends. This turns the margin table from Section 4 into an analytical narrative.

**COGS Analysis:**
- Pull product COGS and services COGS (or equivalent cost breakdown) if available
- Identify the 3-5 biggest cost line items and their YoY trends
- Is COGS growing faster or slower than revenue? If slower, what's driving the efficiency — input costs, mix shift, pricing power, or scale leverage?

**OpEx Breakdown:**
- Pull R&D and SG&A separately for the last 8 quarters
- Compute R&D % of revenue and SG&A % of revenue trends
- Is the company investing more in R&D (growth mode) or cu

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---
name: guidance-tracker
description: Track management guidance accuracy over time for a given company
---

Track management guidance accuracy for 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.

Follow these steps:

## 1. Company Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 2. Discover Guidance Series
Search for series with keywords like "guidance", "outlook", "estimate", "forecast", "target" to find all available guidance metrics. Common guidance series include:

**Financial guidance:**
- Revenue guidance (quarterly and/or annual)
- EPS guidance
- Operating income / margin guidance
- EBITDA guidance
- Segment-level revenue guidance
- CapEx guidance
- Free Cash Flow guidance

**Operational KPI guidance** — many companies guide on KPIs, and tracking these beats/misses is often more informative than financial guidance:
- Subscriber / user count guidance (e.g., "we expect to add X million subscribers")
- Unit shipment guidance (e.g., "iPhone units", "deliveries")
- ARPU / ASP guidance
- Same-store sales guidance
- GMV / bookings guidance
- Net revenue retention guidance
- Store openings / closings guidance
- Production volume / capacity guidance

Search explicitly for KPI-specific guidance series using terms like "subscriber guidance", "unit guidance", "ARPU guidance", "same-store sales outlook", "deliveries forecast", "bookings target". These are separate from financial guidance and often reside in different series.

## 3. Pull Guidance Data
Calculate 8+ quarters backward from `latest_calendar_quarter`. Pull all discovered guidance series for those periods.

## 4. Pull Actual Results
For each guidance metric, pull the corresponding actual result series for the same periods.

## 5. Build Guidance vs Actuals Tracker
CRITICAL OFFSET RULES:
- **Quarterly guidance**: Guidance from Q(N) earnings call applies to Q(N+1) results. Compare Q(N) guidance -> Q(N+1) actual.
- **Annual guidance from Q1/Q2/Q3**: Applies to current fiscal year. Compare to FY actual.
- **Annual guidance from Q4**: Applies to NEXT fiscal year. Compare to next FY actual.

For each guidance-actual pair, calculate:
- Guidance value
- Actual value
- Delta (Actual - Guidance)
- Beat/Miss % ((Actual - Guidance) / |Guidance| x 100)
- Classification: Beat / In-line / Miss (use +/-1% threshold for in-line)

## 6. Pattern Analysis
Analyze the guidance track record:
- Overall beat rate (% of quarters where actual > guidance)
- Average beat/miss magnitude
- Trend in guidance accuracy (getting tighter? more conservative? less reliable?)
- Any metrics where management is notably conservative or aggressive
- Guidance range width trends (if range guidance is given)

**Management credibility assessment:**
- If the company consistently beats by a similar margin, call out sandbagging — this suggests management is deliberately setting low bars, which can mask underlying deceleration. A 100% beat rate is not necessarily bullish; it may mean guidance is uninformative.
- If guidance has been cut or missed, assess whether management acknowledged the miss honestly or buried it in adjusted metrics.
- Flag any pattern where qualitative language ("strong demand," "robust pipeline") didn't translate to actual results.

## 7. Commentary from Filings
Search SEC filings/documents across multiple queries to build a complete picture of guidance practices. If any search returns empty, try alternative keywords before giving up.

- **Explicit guidance language**: T

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---
name: ib-deck
description: Generate an institutional-grade investment banking pitch deck (HTML)
---

Build an institutional-grade pitch deck for the company named in the user's request. If no ticker or company is provided, ask for one before proceeding.

**Before starting, read `../design-system.md` for formatting conventions and `../data-access.md` for data access methods.** Also read the reference files in this skill's `references/` directory for slide templates and components.

This skill generates a self-contained HTML presentation that can be opened in a browser and printed to PDF if needed.

## Phase 1 — Requirements

Determine the deck category and scope:

**Category** (infer from context, or default to IB Advisory):
- **IB Advisory** — M&A advisory, fairness opinions, board presentations. Navy/steel/gold palette. "CONFIDENTIAL" marking.
- **Activist / L-S Equity** — Shareholder campaigns, investment memos as decks. Navy/blue/orange or navy/sky/green palette.

**Firm Attribution:**
- Firm name defaults to "Daloopa". If the user specifies a firm name in their prompt, use that instead.
- **NEVER hallucinate a firm name** (Goldman Sachs, Morgan Stanley, JPMorgan, etc.). See `../data-access.md` Section 4.5.
- Include firm name on the cover slide and in all slide footers.

**Gather from the user or infer:**
- Target company (ticker)
- Purpose (M&A pitch, fairness opinion, investment memo, activist campaign)
- Key thesis or strategic rationale
- Specific slides needed (or use the default 14-slide deck)

## Phase 2 — Data Gathering

Look up the company by ticker using `discover_companies`. Capture `company_id`, `latest_calendar_quarter`, and `latest_fiscal_quarter`. Use `latest_calendar_quarter` to anchor all period calculations (see `../data-access.md` Section 1.5).

Use Daloopa MCP for all financial data. Target comprehensive coverage:
- **5+ years of quarterly financials** — calculate 20+ quarters backward from `latest_calendar_quarter` (income statement, balance sheet, cash flow)
- **Segment and geographic breakdowns**
- **All company-specific operating KPIs**
- **6-10 peers** — get trading multiples and fundamentals from Daloopa + market data (see `../data-access.md` Section 2)
- **Guidance and consensus** (see `../data-access.md` Section 3)
- **SEC filings** — risk factors, growth drivers, M&A commentary, strategic language

Get market data for the target and all peers:
- Current price, market cap, shares outstanding, beta, trading multiples
- Historical price data for TSR comparison

Market data resolution order (see `../data-access.md` Section 2):
1. MCP market data tools (if available)
2. Web search for current quotes, multiples, and historical data
3. Sensible defaults (industry-average multiples if specific data unavailable)

## Phase 3 — Analysis

Run the core analyses needed for the deck:
- **Valuation**: DCF (WACC, 5Y FCF projections, terminal value, sensitivity), comps table, implied valuation range
- **Scenario analysis**: Bull/base/bear with bottoms-up segment builds — be honest about which scenario is most likely
- **Capital allocation**: Buybacks, dividends, shareholder yield, leverage — flag any value-destructive patterns
- **Financial projections**: 3-5 year forward estimates — challenge assumptions, don't just extrapolate

**DCF Methodology** (inline calculation):
- Project 5 years of unlevered free cash flows (UFCF = NOPAT + D&A - CapEx - ΔWC)
- Discount at WACC (beta-based or peer-median if unavailable)
- Terminal value using perpetuity growth method (TGR 2-3%)
- PV of FCFs + PV of TV = EV → subtract net debt → equity value → per-share price

**Critical assessment:** The deck should present an honest analytical view, not a promotional pitch. If the valuation looks stretched, say so. If growth is decelerating, show it clearly. If risks are material, give them proper weight. Institutional investors will dismiss analysis that reads as advocacy rather than research.

## Phase 4 — Build Presentation

Gen

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---
name: industry
description: Cross-company industry comparison across multiple tickers
---

Perform an industry comparison across the companies named in the user's request. If no ticker or company is provided, ask for one before proceeding.

The user will provide multiple tickers separated by spaces (e.g., "AAPL MSFT GOOG AMZN").

**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 Lookups
Look up all provided tickers using `discover_companies`. For each company, capture:
- `company_id`
- `latest_calendar_quarter` — use the earliest `latest_calendar_quarter` across all companies as the anchor for period calculations (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Note each company's fiscal year end — this is critical for calendar quarter alignment
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 2. Comparable Financial Metrics
Calculate 8 quarters backward from the anchor `latest_calendar_quarter`. For each company, find and pull these metrics:

**Income Statement:**
- Revenue
- Gross Profit / Gross Margin
- Operating Income / Operating Margin
- EBITDA (if not reported, compute as Operating Income + D&A — label "(calc.)")
- Net Income / Net Margin
- Diluted EPS
- R&D Expense
- Stock-Based Compensation (SBC)

**Cash Flow:**
- Operating Cash Flow
- CapEx (Purchases of property, plant and equipment)
- Free Cash Flow (compute as OCF - CapEx — label "(calc.)")
- D&A (needed for EBITDA calc if not directly reported)

For any derived/computed metric, mark it with "(calc.)" so the reader knows it's not directly sourced.

## 3. Company-Specific KPIs
First, think about what KPIs matter for the specific industry being compared. Use the full sector taxonomy to guide discovery:

- **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

For each company, discover and pull the most relevant KPIs. Note which KPIs are common across the group (apples-to-apples comparison) and which are unique to specific companies. For mixed-sector comparisons, focus on the KPIs that apply to the largest revenue segments of each company.

## 4. Normalize & Compare
- **Calendar quarter alignment is critical.** Ensure all companies are compared on the same calendar quarters. Note each company's fiscal year end and map fiscal quarters to calendar quarters.
- Build side-by-side comparison tables
- Calculate margins for ALL 4 recent quarters (not just the latest) to show trends
- Calculate YoY growth rates for each of the last 4 quarters

## 5. Ranking & Analysis
- Rank companies on each key metric (revenue growth, margins, FCF yield, etc.)
- Identify the leader and laggard for each metric
- Flag notable outliers (unusually high/low margins, accelerating/decelerating growth)
- Note any divergence in KPIs or business model differences
- Compute R&D as % of revenue and SBC as % of revenue for each company — these reveal structural differences in how each company invests and compensates
- Show YoY segment growth rates for the most recent quarter, not just absolute segment revenue
- Flag one-time items that distort any quarter's comparison

## 6. Document Search
F

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---
name: inflection
description: Auto-detect biggest acceleration/deceleration inflections across all
  metrics
---

Detect the biggest financial and operating inflections for 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.

Follow these steps:

## 1. Company Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 2. Broad Series Discovery
Cast a wide net to discover ALL available series for this company. Search with multiple keyword sets to maximize coverage:
- Financial: "revenue", "income", "profit", "margin", "eps", "cash flow"
- Operating: "subscriber", "user", "customer", "unit", "arpu", "retention"
- Segment: "segment", "product", "service", "geographic"
- Balance sheet: "debt", "asset", "equity", "cash"
- Other: "backlog", "bookings", "pipeline", "store", "employee"

Collect all unique series IDs. The goal is comprehensiveness — capture every metric Daloopa tracks for this company.

## 3. Pull 8 Quarters of Data
Calculate 8 quarters backward from `latest_calendar_quarter`. Pull all discovered series for those periods. This gives enough history to compute both QoQ and YoY rates plus their second derivatives.

## 4. Compute Growth Rates and Inflections
For each series with sufficient data (at least 5 quarters):

**YoY Growth Rate** for each quarter:
- growth_t = (value_t - value_{t-4}) / |value_{t-4}|

**YoY Acceleration (second derivative):**
- accel_t = growth_t - growth_{t-1}
- Positive = accelerating, Negative = decelerating

**QoQ Sequential Growth** (for non-seasonal metrics):
- seq_growth_t = (value_t - value_{t-1}) / |value_{t-1}|

**QoQ Acceleration:**
- seq_accel_t = seq_growth_t - seq_growth_{t-1}

Skip series where values are too small (< 1% of revenue) or where data is sparse. For margin/ratio series (values between 0-1 or percentages), compute change in basis points rather than % change.

## 5. Rank Inflections
Rank all series by the magnitude of their most recent acceleration/deceleration:

**Top 10 Accelerating** — series with the largest positive acceleration in the most recent quarter. These are metrics that are improving faster than before.

**Top 10 Decelerating** — series with the largest negative acceleration (or deceleration). These are metrics where momentum is fading.

For each inflection, note:
- Series name
- Most recent value (with Daloopa citation)
- Current YoY growth rate
- Prior-quarter YoY growth rate
- Acceleration (the delta)
- Whether this is a new trend (1Q) or sustained (2-3Q in same direction)

## 6. Contextualize Key Inflections
For the top 5 most significant inflections (by magnitude and importance to the business):
- Search SEC filings for context on what's driving the change
- Try keywords related to the specific metric (e.g., if "Services Revenue" is accelerating, search for "services", "subscription", "recurring")
- Extract management commentary explaining the inflection
- Note whether the inflection aligns with or contradicts management guidance

## 7. Synthesize
Identify the narrative:
- Is the company broadly accelerating or decelerating?
- Are there divergent trends (e.g., revenue accelerating but margins decelerating)?
- Which inflections matter most for the investment case?
- Are operating KPIs leading or lagging the financial inflections?

**Critically assess sustainability:**
- For positive inflections: Is this a durable trend change or a one-time comp effect? Will it persist next quarter when the base normalizes? Is it driven by organic strength

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---
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_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

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---
name: precedent-transactions
description: Precedent M&A transactions analysis with deal multiples and acquisition
  history
---

Build a precedent transactions analysis for the company named in the user's request. If no ticker or company is provided, ask for one before proceeding.

This is the third pillar of valuation (alongside trading comps and DCF) — it answers: what have acquirers actually paid for businesses like this one? The output is two tables: comparable M&A transactions with deal multiples, and the subject company's own acquisition history.

**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 Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

Identify:
- Full legal company name
- Primary stock exchange and reporting currency
- Country of domicile and primary operations
- Industry and sub-sector
- Approximate revenue and EBITDA scale (to calibrate comparable deal sizing)

## 2. Subject Company Financials
Calculate 4 quarters backward from `latest_calendar_quarter`. Pull from Daloopa:
- Revenue (compute trailing 4Q / LTM total)
- EBITDA (compute trailing 4Q; if not available, use Operating Income + D&A, label "(calc.)")
- Operating Income
- Net Income
- Free Cash Flow (OCF - CapEx, label "(calc.)")

These serve as the reference point for comparing deal multiples — what would an acquirer be paying relative to this company's current financials?

## 3. Identify Comparable Precedent Transactions
Find 8-15 completed M&A transactions from the last 7-10 years involving target companies comparable to the subject. "Comparable" means:
- Same industry and sub-sector
- Similar business model (e.g., SaaS, semiconductor IP, consumer internet, industrials)
- Roughly comparable scale — within ~0.5x-4x of the subject's revenue
- Completed transactions only (not rumored, not pending)

**Research sources in priority order:**
1. **SEC EDGAR** (for US targets) — SC TO, DEFM14A, 8-K filings disclose EV and deal terms
2. **Equivalent regulators for non-US targets:** FCA (UK), EDINET (Japan), HKEx (Hong Kong), SEDAR+ (Canada), ASX (Australia)
3. **Official investor relations press releases** from acquirer or target
4. **Reputable financial news:** Reuters, Bloomberg, Wall Street Journal, Financial Times

Use web search to identify deals: `"{industry} acquisitions {sub-sector} last 10 years"`, `"{TICKER} comparable M&A transactions"`, `"{sector} deal comps precedent transactions"`.

**Do NOT use:** finance blogs, Seeking Alpha, Reddit, anonymous wiki contributions, or aggregators without a traceable primary source.

For each transaction, capture:
- Announcement date
- Acquirer name
- Target name
- Transaction Enterprise Value
- Deal consideration (All Cash / All Stock / Cash + Stock)
- Source (press release URL, SEC filing, or regulatory filing)

## 4. Source Target Financials via Daloopa
For each target company in the precedent transactions table, source LTM Revenue and EBITDA from Daloopa:

1. **Look up the target** using `discover_companies` with the target's ticker or name
2. **Find relevant series** using `discover_company_series` with keywords `["revenue", "EBITDA"]` and the appropriate period (the last complete fiscal year before the deal announcement)
3. **Pull the data** using `get_company_fundamentals` with the discovered series IDs
4. For EBITDA, look for series containing "Adjusted EBITDA", "EBITDA", or fall back to "Operating Income" + D&A
5. If a target is not in Daloopa (e.g., pre-IPO targets, private companies), fall back to SEC filings, press releases, or regulatory filings

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---
name: research-note
description: Generate a professional Word document 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, in

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---
name: setup
description: Verify Daloopa MCP connection and show available skills
---

Walk the user through verifying their Daloopa setup for Codex or ChatGPT. Be conversational and helpful.

## Step 1: Verify Runtime
Confirm the user is working in Codex, ChatGPT, or another OpenAI environment that can use skills. Explain that this skill checks whether the Daloopa MCP tools are available.

## Step 2: Verify MCP Connection
This plugin connects to two Daloopa MCP servers:
- **daloopa** (`mcp.daloopa.com/server/mcp`) - Financial data (fundamentals, KPIs, SEC filings)
- **daloopa-docs** (`docs.daloopa.com/mcp`) - Daloopa knowledgebase (API docs, how-tos, usage help)

Run a quick test by calling `discover_companies` with a well-known ticker like "AAPL" to confirm the data MCP server is connected and responding. Show the user the result.

If this fails:
- Check that `.mcp.json` is present and configured for the Daloopa MCP servers.
- In Codex, reinstall or reload the plugin after changing MCP configuration.
- In ChatGPT, verify that the Daloopa MCP connector or equivalent tool access is enabled.
- If the server returns `401` or `Reauthentication required`, restart the Daloopa OAuth/login flow in the current environment.
- On first use, OAuth may open a browser window for Daloopa login.

## Step 3: Quick Tour
Tell the user about the available analysis skills. Use natural-language examples such as:
- "Create a tearsheet for AAPL."
- "Review MSFT earnings and guidance."
- "Build a DCF valuation for NVDA."
- "Create an industry comp sheet for AAPL and peers."

Each reporting skill saves generated files to the `reports/` directory when file access is available.

## Step 4: Note on Enhanced Features
For file-heavy workflows such as Word research notes, Excel models, and pitch decks, use the local document, spreadsheet, or presentation generation workflow available in the current OpenAI environment. If a file cannot be generated in the current environment, provide the complete structured content and explain the limitation.

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---
name: supply-chain
description: Interactive supply chain dashboard mapping suppliers, customers, and
  financial interdependencies
---

Generate an interactive supply chain dashboard for 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 skill maps the upstream (supplier) and downstream (customer) relationships for a target company, quantifying financial interdependencies in both directions. The output enables an analyst to understand: Who are the critical suppliers and customers? Where is concentration risk on both sides? Which suppliers depend heavily on this company for revenue? Which customers depend on this company's products as critical inputs? How does a shock propagate both upstream (demand shock to suppliers) and downstream (supply disruption to customers)?

## Output Format

The final deliverable is a **single self-contained HTML file** with:
- Embedded CSS and JavaScript (no external dependencies)
- **Tier-grouped Canvas network visualization** — columns: Tier 3 → Tier 2 → Tier 1 → Target → Customers, with connection lines. Clickable nodes open detail overlays.
- **Inventory Health Overview table** — for all suppliers: RM%, WIP%, FG% of total inventory shown as stacked colored bars, plus latest total inventory value
- **Supplier cards grouped by tier** — Tier 1 (Critical/Sole-source), Tier 2 (Major Component), Tier 3 (Specialty) with click-to-expand detail overlays
- **Detail overlays** for each supplier containing:
  - 10-quarter financial table (Revenue, Gross Profit, Net Income, Gross Margin %)
  - 10-quarter inventory breakdown table (Raw Materials, WIP, Finished Goods, Total, RM%, WIP%, FG%)
  - Canvas chart: stacked bar chart of inventory composition with Gross Margin % line overlay
  - Business description and relationship to target company
- **Customer cards grouped by category** — Channel Partners, Enterprise/B2B, End-Market Exposure — with click-to-expand detail overlays matching supplier depth
- **Detail overlays** for each customer containing:
  - 10-quarter financial table (Revenue, Gross Profit, Net Income, Gross Margin %)
  - 10-quarter inventory breakdown table (Raw Materials, WIP, Finished Goods, Total, RM%, WIP%, FG%)
  - Canvas chart: stacked bar chart of inventory composition with Gross Margin % line overlay
  - Business description and relationship to target company
- **Upstream Shock Analysis** section — narrative analysis of how a demand/supply shock to the target company ripples upstream through the supplier chain, with an impact matrix table (Revenue Impact, Margin Impact, Overall Risk per supplier)
- **Downstream Shock Analysis** section — narrative analysis of how a supply disruption at the target company ripples downstream through the customer chain, with an impact matrix table (Input Criticality, Switching Cost, Revenue at Risk, Overall Disruption Risk per customer)
- All financial figures hyperlinked to Daloopa source citations
- A "Download as PDF" button (uses `window.print()`)

**DOM Safety**: All JavaScript MUST use `createElement()` + `textContent` + `appendChild()` for DOM construction. NEVER use `innerHTML`, `outerHTML`, or any HTML-string injection methods. Use helper functions like `ce(tag)`, `ca(el, attrs)`, `cA(parent, children)` to keep code compact.

Save to `reports/{TICKER}_supply-chain.html` and open it with `open`.

---

## RESEARCH WORKFLOW

This is a multi-phase research process. Each phase builds on the previous one. Maximize parallelism across independent API calls.

### Phase 1: Target Company Identification

1. Use `discover_companies` with the ticker symbol to get the `company_id`, `latest_calendar_quarter`, and `latest_fiscal_quarter`. Note the firm name for report attribution (default: "Daloop

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---
name: tearsheet
description: Quick one-page company overview and snapshot
---

Generate a concise company tearsheet 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.

This should be a quick, one-page overview — the kind of snapshot an analyst pulls up before a meeting.

**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 Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 1b. Current Stock Price
Get the current stock price using `get_stock_prices` (see `../data-access.md` Section 1.7). Pass `company_id` and `dates` for the 3 most recent calendar days — use the most recent returned close price. Include the price, date, and a simple context line (e.g., 52-week range or YTD change if you have enough history from a quick `start_date`/`end_date` pull of the last 12 months). Display this prominently at the top of the report next to the company name.

## 2. Key Financials
Calculate periods backward from `latest_calendar_quarter` (8 quarters total: last 4 + year-ago for each to enable YoY):
Pull:
- Revenue
- Gross Profit
- Operating Income
- EBITDA (if not reported, compute as Operating Income + D&A — label it "EBITDA (calc.)" in the report)
- Net Income
- Diluted EPS
- Operating Cash Flow
- CapEx (Purchases of property, plant and equipment)
- Free Cash Flow (compute as Operating Cash Flow - CapEx — label it "FCF (calc.)" in the report)

For any derived/computed metric, mark it with "(calc.)" so the reader knows it's not directly sourced.

## 3. Key Operating KPIs
This section is strictly for **business-driver metrics** — the operational numbers that actually move revenue and earnings. Do NOT put financial statement items (D&A, share count, buybacks, dividends) here — those belong in the financials or capital return sections.

First, think about what the most important KPIs are for THIS specific company based on its business model and what drives its valuation. For example:
- **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, products vs services gross margin split
- **Financial services**: AUM, NIM, loan growth, credit quality metrics
- **Pharma/biotech**: pipeline stage, patient starts, scripts, market share
- **Industrials/energy**: backlog, book-to-bill, utilization, production volumes

Then search for those specific KPIs by name, plus cast a wider net for anything else Daloopa has. Also search for:
- Segment/product revenue breakdown
- Geographic revenue breakdown

**If the company discloses few operational KPIs** (e.g., Apple stopped reporting iPhone units in 2019), acknowledge the disclosure gap explicitly rather than padding the section with financial metrics. A short note like "Apple does not disclose unit volumes or ASPs; segment revenue is the finest granularity available" is more informative than showing D&A and buybacks as fake KPIs.

**Always search broadly** — companies often disclose more KPIs than you'd expect. For Apple, beyond segment revenue, Daloopa also has: installed base active devices (~2.5bn), products gross margin vs services gross margin (the mix shift story), and 

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---
name: unit-economics
description: Bottoms-up unit economics decomposition for any public company
---

Perform a bottoms-up unit economics decomposition for 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.

Follow these steps:

## 1. Company Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 2. Series Discovery & Business Archetype Detection
Cast a wide net to discover ALL available series for this company. Search with multiple keyword sets to maximize coverage:
- Financial: "revenue", "income", "profit", "margin", "eps", "cost"
- Operating KPIs: "subscriber", "user", "customer", "unit", "arpu", "retention", "churn"
- Segment/Product: "segment", "product", "service", "geographic"
- Business-specific: "store", "gmv", "order", "booking", "backlog", "premium", "loan", "aum", "room", "seat", "bed", "acreage"

Collect all unique series IDs. Read every series name and description returned. **This is how you learn what kind of business this is and what unit-level KPIs Daloopa tracks for it.**

Based on series availability, classify the business into one of these archetypes (or a hybrid). This classification drives the entire report structure:

| If you find series like... | Archetype | Unit = |
|---|---|---|
| ARR, MRR, net dollar retention, customers, ACV, churn, CAC, LTV | **SaaS / Subscription** | Customer or subscription |
| Store count, same-store sales, AUV, restaurant-level margin, new openings | **Unit-based retail / Restaurant** | Store or unit |
| GMV, take rate, orders, AOV, active buyers/sellers | **Marketplace / E-commerce** | Order or transaction |
| Subscribers, ARPU, churn, content spend per sub | **Consumer subscription (media/streaming)** | Subscriber |
| Premiums written, loss ratio, combined ratio, policies in force | **Insurance** | Policy |
| NIM, loans, deposits, provision for credit losses, NCOs | **Banking / Lending** | Loan or account |
| ASP, units shipped, cost per unit, gross margin per unit | **Hardware / Manufacturing** | Unit shipped |
| AUM, management fee rate, performance fees, fund flows | **Asset Management** | Dollar of AUM |
| Revenue per available room (RevPAR), occupancy, ADR | **Hospitality / Lodging** | Room night |
| RPM, RASM, CASM, load factor, ASMs | **Airlines / Transportation** | Available seat mile |
| Revenue per user, DAU, MAU, ARPU, engagement | **Digital platform / Advertising** | User |
| Beds, admissions, revenue per admission, case mix | **Healthcare facilities** | Admission or bed |
| Acreage, production per acre, realized price per unit | **Commodity / E&P** | Unit of production |

If the business is a hybrid or doesn't fit neatly, construct a custom framework from the available series. The archetype is a starting guide, not a constraint.

**Edge cases:**
- **Diversified / multi-segment companies**: Pick the largest or most analytically interesting segment for primary analysis. Note other segments briefly. If the user specifies a segment, focus there.
- **Pre-revenue / early-stage companies**: Focus on burn rate per unit of growth, cash efficiency, and path to unit profitability.
- **Financial companies (banks, insurance, asset managers)**: These have specialized unit economics. For banks, the "unit" is a dollar of assets — focus on NIM, fee income/assets, efficiency ratio, credit costs. For insurance, focus on the combined ratio decomposition. Don't force a SaaS or retail framework onto financials.
- **Companies with no obvious unit-level KPIs in

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---
name: working-capital
description: Cash conversion cycle, earnings quality, and working capital deep-dive
---

Perform a cash conversion cycle, earnings quality, and working capital deep-dive for 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.

Follow these steps:

## 1. Company Lookup
Look up the company by ticker using `discover_companies`. Capture:
- `company_id`
- `latest_calendar_quarter` — anchor for all period calculations below (see `../data-access.md` Section 1.5)
- `latest_fiscal_quarter`
- Firm name for report attribution (default: "Daloopa") — see `../data-access.md` Section 4.5

## 2. Series Discovery & Working Capital Profile Detection
Cast a wide net to discover ALL available series for this company. Search with multiple keyword sets to maximize coverage:
- Balance sheet: "receivable", "inventory", "payable", "deferred revenue", "contract", "prepaid", "accrued", "current asset", "current liabilit"
- Cash flow: "cash flow from operations", "working capital", "depreciation", "amortization", "stock-based comp", "capital expenditure", "free cash flow"
- Income statement: "revenue", "cost of goods", "cost of revenue", "operating income", "net income"
- Business-specific: "backlog", "billing", "remaining performance obligation", "provision", "allowance", "reserve", "unearned premium", "loss ratio"

Collect all unique series IDs. Read every series name carefully. You need to understand:
- What balance sheet line items are available (receivables, inventory, payables, deferred revenue, contract assets/liabilities, prepaid expenses, accrued liabilities, etc.)
- What cash flow statement detail exists (CFO, changes in working capital components, capex, stock-based comp, D&A, etc.)
- What business-specific KPIs exist that contextualize working capital (e.g., backlog for industrials, deferred revenue for SaaS, policy reserves for insurance, loan loss provisions for banks)

Based on series availability, classify the business's working capital profile:

| If you find series like... | Profile | Primary focus |
|---|---|---|
| Inventory, COGS, accounts payable, accounts receivable | **Inventory-intensive (manufacturing, retail, consumer goods)** | Full CCC decomposition: DIO + DSO − DPO. Inventory is the core risk. Watch for inventory-to-sales divergence, channel stuffing signals, obsolescence risk |
| Deferred revenue, contract liabilities, billings, remaining performance obligations | **Negative working capital / SaaS / subscription** | Deferred revenue is the key asset — cash collected before revenue recognized. Focus on billings vs. revenue spread, deferred revenue growth vs. revenue growth, and whether the cash-first dynamic is strengthening or weakening |
| Receivables and payables but minimal/no inventory | **Asset-light services (consulting, staffing, advertising, tech services)** | DSO is the main event. Receivables quality, aging, concentration. Unbilled receivables or contract assets as early warning. DPO as a secondary lever |
| Loans, deposits, allowance for credit losses, provision expense, net charge-offs | **Financial institutions (banks, specialty finance)** | Traditional CCC is meaningless. Focus on provision adequacy (allowance/loans, provision/NCOs, reserve coverage), deposit cost and mix, and the gap between provision expense and actual cash losses realized as the earnings quality signal |
| Policy reserves, loss reserves, unearned premiums, LAE | **Insurance** | Reserve adequacy is the working capital equivalent. Prior-year reserve development (favorable/adverse), loss ratio trends, reserve-to-premium ratios. Cash flow from underwriting vs. reported underwriting income |
| Contract assets, unbilled receivables, costs to obtain contracts, progr

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