# Initiate

> Initiate coverage — generate both research note (.docx) and Excel model (.xlsx)

- Skill: `daloopa/initiate` (Agent Skill)
- Install (CLI): `npx skillmds@latest add daloopa/initiate`
- Raw SKILL.md: https://api.skillmd.com/api/skills/daloopa/initiate/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: daloopa (https://skillmd.com/u/daloopa)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/daloopa/initiate

---


Initiate coverage on the company specified by the user: $ARGUMENTS

**Before starting, read `../data-access.md` for data access methods and `../design-system.md` for formatting conventions.** Follow the data access detection logic and design system throughout this skill.

This is the capstone skill that produces both a research note and an Excel model from a single comprehensive data gathering pass.

## Strategy
Rather than running `/research-note` and `/build-model` independently (which would duplicate data gathering), this skill gathers a superset of data once, then renders both outputs.

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

Get market data (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)

## Phase 2 — Comprehensive Data Gathering
Follow the `/build-model` skill's Phase 2 data pull (the most comprehensive). Calculate 8-16 quarters backward from `latest_calendar_quarter`. Pull:
- Full Income Statement (Revenue through EPS, including D&A for EBITDA calc)
- Full Balance Sheet (Cash through Equity)
- Full Cash Flow Statement (OCF, CapEx, FCF, Dividends, Buybacks)
- Segment revenue and operating income breakdowns
- Geographic revenue breakdown
- All company-specific operating KPIs
- All guidance series and corresponding actuals
- Share count, buyback amounts

## Phase 3 — Peer Analysis
Identify 5-8 comparable companies.
Get peer trading multiples (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).

## Phase 4 — Projections
If a projection engine is available (see ../data-access.md Section 5), use it. Otherwise project manually.
Write historical data to `reports/.tmp/{TICKER}_initiate_input.json` for reuse.

## Phase 5 — DCF Valuation
- Calculate WACC (CAPM)
- Project 5-year FCFs
- Terminal value
- Implied share price
- Sensitivity table (WACC × terminal growth)

## Phase 6 — Qualitative Research
Search SEC filings comprehensively:
- Risk factors, growth drivers, competitive dynamics
- Management outlook and guidance language
- Capital allocation strategy
- Company-specific strategic topics
Extract business description, risks (ranked), investment thesis, catalysts.

## Phase 7 — What You Need to Believe
Build falsifiable bull/bear beliefs (follows /research-note methodology):
- 4-6 numbered bull beliefs with evidence and Daloopa citations — each testable in 6 months
- 4-6 numbered bear beliefs with evidence and Daloopa citations — each testable in 6 months
- Valuation math for each side: forward multiple × earnings estimate = price target
- Risk/reward asymmetry assessment (bull upside % vs bear downside %)

## Phase 8 — Synthesis & Charts
Write the executive summary, variant perception, and key findings.

If chart generation is available (see ../data-access.md Section 5), generate charts:
1. Revenue time-series
2. Margin time-series
3. Segment pie
4. Scenario bar (bull/base/bear)
5. DCF sensitivity heatmap

Skip any charts that fail; note which were generated.

## Phase 9 — Render Both Outputs

**Research Note (.docx):**
1. Build the research note context with all gathered data, charts, narrative sections
2. Write to `reports/.tmp/{TICKER}_context.json`
3. Run: `python infra/docx_renderer.py --template templates/research_note.docx --context reports/.tmp/{TICKER}_context.json --output reports/{TICKER}_research_note.docx`

**Excel Model (.xlsx):**
1. Build the model context with all financial data, projections, DCF, comps
2. Write to `reports/.tmp/{TICKER}_model_context.json`
3. Run: `python infra/excel_builder.py --context reports/.tmp/{TICKER}_model_context.json --output reports/{TICKER}_model.xlsx`

## Output
Tell the user:
- Research note saved to: `reports/{TICKER}_research_note.docx`
- Excel model saved to: `reports/{TICKER}_model.xlsx`
- Context files saved to: `reports/.tmp/` (for future updates)
- 3-4 sentence executive summary
- Key valuation range (DCF implied price + comps range)
- Top 3 findings
- Remind user that yellow cells in the Excel model's Projections tab are editable inputs

All financial figures must use Daloopa citation format: [$X.XX million](https://daloopa.com/src/{fundamental_id})