A professional-grade financial data retrieval tool for AI agents to monitor real-time stock prices and fundamental metrics.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install stock-quote
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-quote 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 Quote Skill?
The Stock Quote Skill is a robust data acquisition utility designed for the Openclaw Skills ecosystem. It empowers AI agents to retrieve live market data, including current prices, percentage changes, and essential fundamental indicators like P/E ratios and dividend yields. This skill acts as a bridge between raw market feeds and agentic workflows, providing the high-fidelity information required for automated financial analysis.
By leveraging multiple data providers, this skill ensures high availability and accuracy for developers building within Openclaw Skills. Whether you are creating a personal trading assistant or a complex portfolio management agent, the Stock Quote Skill provides the necessary infrastructure to stay updated with global market movements without manual intervention.
Stock Quote Skill Use Cases
- Fetching real-time stock quotes for individual or multiple tickers during an AI-driven research session.
- Automating the calculation of portfolio valuations by updating asset prices in a central memory store.
- Validating trading strategies by checking fundamental metrics like 52-week highs and price-to-earnings ratios.
- Monitoring stop-loss levels and sending automated alerts when price thresholds are breached.
- Generating daily market heartbeat reports that summarize performance across a watchlist.
How Stock Quote Skill Works
- The AI agent or user invokes the skill by providing one or more stock ticker symbols.
- The skill executes a Python-based retrieval script that prioritizes data sources based on availability and API constraints.
- It first attempts to fetch data via Stooq (free, no key required), followed by yfinance, and then Financial Modeling Prep (FMP).
- If API limits are reached, the skill can fallback to web scraping Yahoo Finance as a final redundancy measure.
- The raw data is parsed and normalized into a standardized format containing fields like price, volume, and market cap.
- The processed data is returned to the agent in either a human-readable summary or a structured JSON object for further automation.
Stock Quote Skill Setup
To get started with this skill in your Openclaw Skills setup, install the required Python dependencies:
pip install yfinance requests
You can verify the installation by running a simple quote request from your terminal:
# Fetch a single stock quote
python ~/.openclaw/skills/stock-quote/scripts/quote.py NVDA
# Fetch multiple quotes in JSON format
python ~/.openclaw/skills/stock-quote/scripts/quote.py AAPL MSFT GOOGL --json
Stock Quote Skill Data Schema & Taxonomy
The Stock Quote Skill organizes market data into a clean, tabular, or JSON structure. Key data points include:
| Field | Technical Description |
|---|---|
| price | The most recent traded market price |
| change_percent | The percentage move from the previous close |
| market_cap | Total market capitalization in USD (or local currency) |
| pe_ratio | Trailing 12-month Price-to-Earnings ratio |
| week_52_high/low | The highest and lowest prices recorded over the last year |
| volume | Total shares traded during the current session |
This schema allows other Openclaw Skills to easily consume and interpret market data for secondary analysis.
Stock Quote Skill Advanced Features
- Intelligent multi-source fallback logic (Stooq -> yfinance -> FMP -> Web Scrape) to ensure data continuity.
- Bulk symbol processing for high-efficiency updates of large watchlists.
- Support for specific data source forcing using the --source flag for debugging or precision requirements.
- Seamless integration with the elite-memory skill for persistent portfolio tracking.
- Automated rule-checking capabilities for identifying 52-week range percentiles and PE violations.
SKILL.md
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