Investing initiate
Initiate coverage — generate both research note (.docx) and Excel model (.xlsx)
git clone https://github.com/daloopa/investing
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"
.claude/skills/initiate/SKILL.mdInitiate coverage on the company specified by the user: $ARGUMENTS
Before starting, read
for data access methods and ../data-access.md
for formatting conventions. Follow the data access detection logic and design system throughout this skill.../design-system.md
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
— anchor for all period calculations (seelatest_calendar_quarter
Section 1.5)../data-access.mdlatest_fiscal_quarter- Firm name for report attribution (default: "Daloopa") — see
Section 4.5../data-access.md
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:
- Revenue time-series
- Margin time-series
- Segment pie
- Scenario bar (bull/base/bear)
- DCF sensitivity heatmap
Skip any charts that fail; note which were generated.
Phase 9 — Render Both Outputs
Research Note (.docx):
- Build the research note context with all gathered data, charts, narrative sections
- Write to
reports/.tmp/{TICKER}_context.json - 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):
- Build the model context with all financial data, projections, DCF, comps
- Write to
reports/.tmp/{TICKER}_model_context.json - 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:
(for future updates)reports/.tmp/ - 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