A stealth-oriented A-share market analysis tool designed for high-stability stock data harvesting and automated trend filtering.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install eni-predator-01
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 eni-predator-01 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 ENI-Oracle Predator v14.0?
ENI-Oracle Predator v14.0 is a specialized financial data intelligence tool built to integrate seamlessly with Openclaw Skills. It leverages the AkShare library to interface with A-share market data, providing developers and traders with a robust framework for tracking market momentum.
The skill is specifically engineered for reliability in production environments. By implementing sophisticated stealth mechanisms like randomized execution delays, it ensures consistent connection stability and avoids node fluctuations during heavy data harvesting sessions.
ENI-Oracle Predator v14.0 Use Cases
- Automated monitoring of the A-share Limit-Up stock pool.
- Identification of high-momentum assets with multi-day 'consecutive board' (streak) patterns.
- Systematic calculation of turnover entropy to assess market liquidity and sentiment.
- Integration into larger financial AI agent workflows for daily market reporting.
How ENI-Oracle Predator v14.0 Works
- The skill initializes a stealth delay using a randomized uniform distribution (5.0 to 10.0 seconds) to bypass basic anti-scraping triggers.
- It interfaces with the East Money (EM) data source via AkShare to fetch real-time or daily closing stock pool data.
- A filtering engine isolates the 'T-Squad'—stocks exhibiting a consecutive limit-up streak of 2 days or more.
- It performs post-processing calculations, such as deriving 'Turnover Entropy' from raw turnover rates.
- The refined dataset is formatted into a clean Markdown table for immediate consumption by an AI agent or end-user.
ENI-Oracle Predator v14.0 Setup
To deploy this skill within your environment, ensure you have Python installed and run the following commands:
pip install akshare pandas lxml
Place the predator.py script into your Openclaw Skills directory and ensure your network environment has access to Chinese financial data providers.
ENI-Oracle Predator v14.0 Data Schema & Taxonomy
The skill outputs a structured Markdown table with the following taxonomy:
| Field | Description | Type |
|---|---|---|
| 代码 (Code) | The unique stock identifier | String |
| 名称 (Name) | The registered name of the security | String |
| 连板天数 | Number of consecutive days at the price limit | Integer |
| 所属行业 | Industry classification | String |
| 换手熵 | Derived metric representing turnover efficiency | Float |
ENI-Oracle Predator v14.0 Advanced Features
- Stealth Execution: Built-in randomized sleep intervals to maintain long-term node health.
- T-Squad Filtering: Automated logic to identify market leaders based on streak duration.
- Empty State Handling: Intelligent checks for post-market data availability with user-friendly notifications.
- Multi-Agent Compatibility: Output is optimized for LLM readability, making it a perfect addition to any Openclaw Skills ecosystem.
SKILL.md
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