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financial-researcher

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1|1|Updated Jan 24, 2026
Search Tags:#financial-analysis#LLM-prompting#Piotroski#AltmanZ#Beneish#OwnerEarnings
One-click install
npx skills add https://github.com/CinderZhang/FAskills --skill financial-researcher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: financial-researcher
Source: https://github.com/CinderZhang/FAskills/tree/main/financial-researcher
Command: npx skills add https://github.com/CinderZhang/FAskills --skill financial-researcher

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

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.

Frequently Asked Questions about financial-researcher

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate Piotroski F-score and Altman Z-score for investment analysis?▼

You can calculate Piotroski F-score and Altman Z-score by routing raw financial data through this skill's Python processing layer, which pre-computes these metrics alongside Beneish M-score and Owner Earnings for institutional-grade analysis.

What is multi-expert financial analysis and how does it evaluate a stock?▼

Multi-expert financial analysis evaluates a stock by generating LLM-ready prompts for seven legendary investor perspectives, including Buffett, Graham, and Lynch. It assembles pre-calculated metrics and contextual data from SEC filings into one coherent, actionable investment report.

How do I generate an investment report combining SEC filings and 13-F holdings data?▼

You generate an investment report by fetching data from SEC filings and 13-F holdings through a DRIVER-guided orchestration pipeline. This process formats the data into per-expert prompts with pre-calculated metrics like ROIC and EVA for high-quality outputs.

Can I use LLM prompting to automate fundamental stock analysis?▼

Yes, you can use LLM prompting to automate fundamental stock analysis by loading pre-calculated financial metrics into expert-specific prompts. This skill orchestrates data collection and formats the context for seven guru experts to deliver institution-grade evaluations.

Does this financial analysis approach work with raw data from financial datasets MCP?▼

Yes, this financial analysis approach works directly with raw data from financial datasets MCP. It fetches the required data and routes it to a Python processing layer to compute named metrics before assembling the final multi-expert investment report.

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