What problem does it solve? It turns raw earnings report numbers into causal explanations—why revenue, margins, or cash flow changed and whether the company can reverse the change—while enforcing that every figure in the output is traceable to a named, dated source. ## Core Features & Use Cases - Two analysis modes: A report mode for full earnings reviews and a focused mode for specific questions like "why did gross margin fall", routed automatically by question scope. - Fact verification pipeline: A facts.json registry plus Python scripts (check_facts.py, lint_report.py, finalize_report.py) that validate claims, block unsupported "beat/miss expectations" wording, and convert internal fact bindings into numbered source citations. - Market-aware conventions: Separate disclosure calibers for A-shares, Hong Kong stocks, and US-listed Chinese companies, with industry playbooks covering product, financial, project-delivery, and recurring-revenue business models. - Use Case: Ask "why did Company X's Q3 gross margin decline" and receive a sourced causal analysis with confidence-graded conclusions, a follow-up watchlist, and a Feishu document deliverable. ## Quick Start Ask the assistant to analyze why a specific listed company's gross margin declined in its latest quarterly report and deliver the result as a Feishu document.