Best use case
AlphaEar Stock Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Overview
Teams using AlphaEar Stock Skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/alphaear-stock/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How AlphaEar Stock Skill Compares
| Feature / Agent | AlphaEar Stock Skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
## Overview
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AlphaEar Stock Skill
## Overview
Search A-Share/HK/US stock tickers and retrieve historical price data (OHLCV).
## Capabilities
### 1. Stock Search & Data
Use `scripts/stock_tools.py` via `StockTools`.
- **Search**: `search_ticker(query)`
- Fuzzy search by code or name (e.g., "Moutai", "600519").
- Returns: List of `{code, name}`.
- **Get Price**: `get_stock_price(ticker, start_date, end_date)`
- Returns DataFrame with OHLCV data.
- Dates format: "YYYY-MM-DD".
## Dependencies
- `pandas`, `requests`, `akshare`, `yfinance`
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