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investor-panel

Generate a structured investor panel verdict for a ticker using 65 investors across 9 investment schools.

6.1k|868|Updated Apr 16, 2026
Search Tags:#pydantic#finance#voting#signal#data-validation#stock-analysis#investor-panel
One-click install
npx skills add https://github.com/wbh604/UZI-Skill --skill investor-panel-wbh604
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: investor-panel
Source: https://github.com/wbh604/UZI-Skill/tree/main/skills/investor-panel
Command: npx skills add https://github.com/wbh604/UZI-Skill --skill investor-panel-wbh604

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill consolidates investment opinions from a large panel of expert investors to generate a structured, consensus-like verdict for a given ticker, helping users quickly gauge market sentiment and potential actions.

Core Features & Use Cases

  • Panel-based scoring: aggregates 65 investors across 9 schools of thought into 50 signals per ticker.
  • Historical context: references a knowledge base of famous investors and peer methodologies to justify conclusions.
  • Use Case: an analyst evaluates a stock by invoking the skill to obtain a formal buy/hold/sell signal and reasoning aligned with multiple investment paradigms.

Quick Start

Input a ticker (e.g., AAPL) and run the skill to generate the investor panel verdict.

Frequently Asked Questions about investor-panel

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

FAQPage Schema
How do I generate a consensus stock analysis signal from multiple investor paradigms?▼

You need to provide a stock ticker and ensure the prerequisite files exist in the .cache directory. The skill reads dimensions.json and raw_data.json from .cache/{ticker}/ to process the investor panel evaluation.

What is an investor panel verdict and how does it validate stock analysis data?▼

An investor panel verdict aggregates opinions from 65 historical investors across 9 schools of thought. It synthesizes 50 individual signals into a final consensus, validated against a Pydantic schema for reliable structured data output.

Can I use Pydantic schemas to validate structured finance signals for stock analysis?▼

Yes, the skill uses a Pydantic schema to validate structured finance signals. It ensures the generated JSON panel adheres to strict data-validation rules across bullish, bearish, and neutral stock analysis verdicts.

What raw data format is required to run a multi-school investor panel evaluation?▼

The skill requires JSON data files located at .cache/{ticker}/dimensions.json and .cache/{ticker}/raw_data.json. These inputs provide the foundational stock metrics needed to evaluate the ticker across multiple investing paradigms.

What are the limitations of using a pre-configured investor panel for stock signal generation?▼

The panel is limited to 65 fixed investors across 9 predefined schools of thought and relies entirely on pre-existing .cache JSON files. It cannot dynamically fetch live market data or accommodate custom investor methodologies outside its database.

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