What problem does it solve? Comparing two or more stocks rigorously requires aligned financial data, consistent valuation dates, comparable metrics, and verifiable evidence; ad-hoc comparisons often mix periods, currencies, and sources, producing misleading rankings. This Skill enforces a disciplined cross-company research workflow with data alignment, evidence ledgers, quality gates, and structured delivery. ## Core Features & Use Cases - Capability-based analysis: Selects from 15 research capabilities (business model, operating fundamentals, valuation, price behavior, A/H relative value, portfolio fit, and more) based on the comparison question. - Auditable data foundation: Builds a core financial snapshot with aligned periods, currencies, and units, validated by audit_ledger.py to catch mismatched comparisons and fabricated sources. - Gated Lark document delivery: Routes output to plain text or a Feishu (Lark) document with K-line charts, heatmaps, and quadrant visuals, enforced by delivery_gate.py and editorial_gate.py. - Use Case: Ask whether Company A or Company B is the better investment; the Skill retrieves comparable financials, analyzes differences dimension by dimension, generates K-line charts, and delivers a verified Lark document with conclusions first. ## Quick Start Compare the fundamentals, valuation, and recent price performance of Company A and Company B and deliver the analysis as a Feishu document.
