financial-researcher

What problem does it solve?

This Skill delivers professional-grade autonomous financial analysis by integrating 7 legendary investor perspectives (Buffett, Graham, Lynch, Wood, Soros, Dalio, Burry) with a Python processing layer that pre-computes institutional-grade metrics. It enables faster, more rigorous evaluations for internal decision-making and reduces reliance on ad-hoc, manual analyses.

Core Features & Use Cases

• Multi-expert framework: Simultaneous analysis from seven renowned investing schools, combined into one coherent workflow.
• Python processing layer: Pre-calculates Piotroski F-score, Altman Z-score, Beneish M-score, Owner Earnings, ROIC, EVA, and other named metrics.
• DRIVER-guided orchestration: Follows the DISCOVER → REPRESENT → IMPLEMENT → VALIDATE → EVOLVE → REFLECT stages to structure work.
• Data routing & on-demand resources: Fetches data from financial datasets MCP, 13-F holdings, news, SEC filings, and formats data for expert prompts.
• LLM-ready context: Generates per-expert prompts with pre-calculated metrics and contextual data for high-quality outputs.

Quick Start

Use the financial-researcher skill to analyze ticker AAPL with a full multi-expert report.