What problem does it solve? Valuation and corporate-finance analyses fail silently when inputs arrive without provenance, consistent units, or a documented substitute for missing data. This Skill defines the input contract for a valuation pipeline: what to fetch, from which source, in what units, how stale it may be, whether a user may supply it directly, and what to substitute when it cannot be found. ## Core Features & Use Cases - Staged collection plan: Eight stages (A1–A8) covering mandate and identity, financial statements, footnotes, market data, macro and currency data, reference-table loads, ownership and governance, and comparable firms, plus conditional branches for banks, young, distressed, cyclical, emerging-market, private, and multi-business firms. - Provenance and gap tracking: Produces raw-financials.json, market-data.json, sources.md, and gaps.json, with a status vocabulary (found, user, derived, fallback, stale, missing) tracing every field to the stage it constrains. - Consistency and single-count rules: Enforces currency, claimholder, basis, vintage, and debt-convention matching rules plus ten single-count rules that prevent double-counting tax shields, country risk, options, cash, and control value. - Use Case: Before running a DCF on an emerging-market manufacturer, use this Skill to collect ten years of statements, walk the riskfree-rate fallback ladder, load the Damodaran reference tables at one consistent vintage, and pass the G1_data gate with a fully documented gaps file. ## Quick Start Use the financial-data-sourcing skill to collect and source every input needed to value a publicly traded company, producing the raw-financials, market-data, sources, and gaps artifacts.
