Investing initiate

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

install
source · Clone the upstream repo
git clone https://github.com/daloopa/investing
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/daloopa/investing "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/initiate" ~/.claude/skills/daloopa-investing-initiate && rm -rf "$T"
manifest: .claude/skills/initiate/SKILL.md
source content

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