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Initiate

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initiate

Initiate is an Agent Skill for documents published in the daloopa/investing repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/daloopa/investing/blob/HEAD/.claude/skills/initiate/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/initiate, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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