Agent skill

valuation-analysis

Constructs valuation models and price targets for a company using DCF, multiples, and scenario analysis. Use this when asked about "fair value", "target price", "is it cheap/expensive", or "valuation".

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Install this agent skill to your Project

npx add-skill https://github.com/hck717/FYP-Prep/tree/main/skills/valuation

SKILL.md

Valuation Analysis

Usage

To generate a valuation model for a company, execute the Python script in this directory.

Command:

bash
python skills/valuation/run_valuation.py --ticker <TICKER> --horizon "<TIME_HORIZON>"

Parameters:

  • ticker: The stock symbol (e.g., AAPL).
  • horizon: Time period (e.g., "1 year", "18 months"). Default: "1 year".

Output

Returns a JSON object with:

  • valuation_range: Low, Base, and High price targets with methodology.
  • assumptions: Key assumptions driving the model (e.g., revenue growth rates, discount rates) with evidence citations.
  • sensitivity: A sensitivity matrix showing how price targets change under different scenarios.

Example

bash
python skills/valuation/run_valuation.py --ticker AAPL --horizon "1 year"

Environment Requirements

  • Python 3.12+
  • Access to research.db SQLite database
  • Neo4j GraphRAG instance running (default: bolt://localhost:7687)
  • Environment variables: NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD (optional, defaults provided)

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