initiate

name	initiate
description	Initiate coverage — generate both research note (.docx) and Excel model (.xlsx)
argument-hint	TICKER

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