stock-copilot-pro

Name: stock-copilot-pro
Availability: InStock
Author: QVerisAI

Analyze stocks across US/HK/CN markets using multi-source data via QVeris MCP/API.

21|3|Updated Feb 13, 2026

Search Tags:#portfolio management #stock analysis #brief #watchlist #openclaw #radar #qveris api

Ready to run

npx skills add https://github.com/QVerisAI/open-qveris-skills --skill stock-copilot-pro

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stock-copilot-pro
Source: https://github.com/QVerisAI/open-qveris-skills/tree/main/stock-copilot-pro
Command: npx skills add https://github.com/QVerisAI/open-qveris-skills --skill stock-copilot-pro

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Stock Copilot Pro consolidates multi-source data (quote, fundamentals, technicals, sentiment, and X sentiment) into a single, structured analytical output, reducing the manual effort required for comprehensive stock research.

Core Features & Use Cases

End-to-end stock analysis across US/HK/CN markets with multi-source data fusion (quote, fundamentals, technicals, sentiment, and X sentiment).
Output in machine-friendly payloads for automated reporting and OpenClaw-driven decision making (briefs, radar, and watchlist interactions).
Use Case: analyze a portfolio of symbols to compare risk/reward and generate a narrative-ready report for investment decisions.

Quick Start

Provide a symbol and market to generate a comprehensive OpenClaw-ready stock analysis report.

Frequently Asked Questions about stock-copilot-pro

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

FAQPage Schema

How do I consolidate multi-source stock analysis data for US, HK, and CN markets?▼

Stock analysis data consolidation integrates quote, fundamentals, technicals, sentiment, and X sentiment data across US/HK/CN markets via QVeris API into a single structured payload, reducing manual research effort for investment decisions.

What's the best way to generate OpenClaw-ready reports for a watchlist portfolio?▼

Generating OpenClaw-ready reports uses deterministic tool chains to analyze watchlist symbols, producing structured payloads for briefs and radar interactions suitable for automated reporting and LLM-driven decision making.

Do I need a QVERIS_API_KEY to run stock analysis and manage watchlists?▼

Stock analysis requires a QVERIS_API_KEY and Node.js 18+ runtime to fetch quote and sentiment data via QVeris MCP/API, enabling watchlist management and structured payload generation.

Can I use X sentiment data alongside fundamentals for comprehensive stock analysis?▼

Stock analysis fuses X sentiment data with fundamentals, technicals, and quotes via QVeris API, delivering a multi-source analytical output that captures market sentiment for risk and reward comparison.

How does the QVeris API integrate quote and sentiment data for automated stock analysis?▼

QVeris API integration merges quote and sentiment data using deterministic tool chains, storing a lightweight evolution state to output machine-friendly payloads for OpenClaw-driven automated analysis.

Are there limitations when running stock analysis across US, HK, and CN markets?▼

Stock analysis across US/HK/CN markets requires a valid QVERIS_API_KEY and Node.js 18+ runtime; output is limited to structured payloads optimized for OpenClaw reports rather than direct execution.