initial-max

What problem does it solve?

Manual equity research is time-intensive, inconsistent, and rarely meets the rigorous quality standards required for high-stakes investment decisions, while one-shot AI research tools produce shallow, unverified reports that lack the depth and sourcing needed to support seven-figure investment choices.

Core Features & Use Cases

• Zhang Lei Framework Scoring: Uses the Hillhouse four-dimension investment framework (Environment, Business, Organization, People) to automatically score research quality and identify gaps iteratively.
• Automated Gap-Filling: Automatically sources verifiable data, embeds direct management quotes, and builds out missing research sections until the report hits a 95/100 quality bar (with minimum per-dimension scores and mandatory DCF valuation).
• Institutional-Grade Reporting: Produces a single, readable, narrative-style investment report structured like professional institutional research, with full DCF models, segment revenue breakdowns, and downloaded management interview transcripts.
• Use Case: An investor can run full deep research on a new stock ticker like NVDA or BABA, and receive a complete, sourced, institutional-grade report in hours instead of weeks, with all scoring and sourcing requirements automatically validated.

Quick Start

Use the initial-max skill to run a full deep research report on the stock ticker AAPL.