A professional investment analysis tool that evaluates companies using Peter Lynch's 13 principles and PEG-based valuation metrics.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install stock-picker
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install stock-picker using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Stock Picker?
The Stock Picker skill is a comprehensive analytical framework designed for Openclaw Skills to help investors identify high-potential stocks. Based on the philosophy from 'One Up on Wall Street,' it focuses on categorizing companies into six distinct types—such as fast growers or stalwarts—to apply the correct investment strategy.
By combining quantitative metrics like the PEG ratio with qualitative 'life observation' techniques, this skill enables AI agents to provide structured investment evaluations. It bridges the gap between raw financial data and actionable insights, ensuring that users don't just find good companies, but find them at the right price.
Stock Picker Use Cases
- Screening individual stocks based on fundamental growth and valuation metrics.
- Categorizing companies into growth types to determine expected return profiles.
- Performing PEG (Price/Earnings-to-Growth) valuation to identify undervalued opportunities.
- Validating consumer-facing brands through real-world life observation and child-approved trends.
How Stock Picker Works
- The skill first classifies the target company into one of six categories: Slow Growers, Stalwarts, Fast Growers, Cyclicals, Turnarounds, or Asset Plays.
- It executes a comprehensive 13-point checklist inspired by Peter Lynch to assess business simplicity, institutional ownership, and internal buy-ins.
- The agent calculates the PEG ratio by dividing the PE ratio by the earnings growth rate to determine if the stock is undervalued (PEG < 1).
- It integrates user-provided life observations to cross-verify financial data with market sentiment.
- Finally, it generates a structured investment score and recommendation (e.g., Strong Buy, Watch, or Avoid) based on the combined analysis.
Stock Picker Setup
To integrate this investment analysis capability into your Openclaw Skills environment, ensure you have the required templates in your references directory.
# Clone the skill into your local AI agent library
mcp install stock-picker
# Ensure reference files are accessible
touch references/lynch-principles.md templates/stock-analysis-template.md
Stock Picker Data Schema & Taxonomy
The skill utilizes a structured input/output schema to maintain consistency across financial reports.
| Field | Type | Description |
|---|---|---|
| company_name | string | The official name or ticker of the stock. |
| business_description | string | Summary of the core business model. |
| pe_ratio | number | Current Price-to-Earnings ratio. |
| growth_rate | number | Annual earnings growth rate percentage. |
| observation | string | Qualitative data from real-world usage or trends. |
Results are outputted as a JSON object containing lynch_13_check results and a final investment_score out of 10.
Stock Picker Advanced Features
- Multi-Agent Synergy: Works alongside value-analyzer and industry-analyst skills for deeper due diligence.
- Dynamic Valuation Tiers: Automatically adjusts buy/sell recommendations based on fluctuating PEG thresholds.
- Error Mitigation: Built-in logic to detect common pitfalls like over-diversification and misuse of one-time earnings in PEG calculations.
- Template-Driven Reporting: Exports findings into standardized markdown templates for easy portfolio tracking.
SKILL.md
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