🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks primarily due to low maintainer activity and poor metadata quality, despite having no direct evidence of malicious activities.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: The presence of network calls is common for packages that require external data sources or APIs but needs further investigation to ensure legitimacy and security.
Shell: No shell execution patterns detected, which is positive from a security perspective.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential risk.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

Test runner config found: conftest.py
Test runner config found: pyproject.toml
10 test file(s) detected (e.g. conftest.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (19703 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

194 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

("/") self._session = requests.Session() self._tickers_cache: list[TickerInfo] | None = Non

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aipriceaction

Create a real-time stock analysis tool using the 'aipriceaction' Python package. This tool will focus on providing in-depth analysis of stock prices across multiple markets including Vietnamese stocks, US stocks, cryptocurrencies, and commodities. The app should integrate AI context building and LLM capabilities to provide insights beyond just numerical data.

**Steps to Build the Application:**
1. **Setup Environment**: Ensure Python is installed along with necessary libraries such as 'aipriceaction'.
2. **Data Collection**: Use 'aipriceaction' to fetch OHLCV (Open, High, Low, Close, Volume) data for selected assets.
3. **AI Context Building**: Implement functionality to analyze the fetched data using AI models integrated within 'aipriceaction'. This includes sentiment analysis, trend prediction, and more.
4. **LLM Integration**: Integrate Large Language Models to interpret the analyzed data and generate human-readable summaries and predictions.
5. **User Interface**: Develop a simple yet effective UI where users can select assets, view real-time data, and read insights generated by the LLMs.
6. **Testing and Deployment**: Test the application thoroughly and deploy it online for public use.

**Suggested Features**:
- Real-time data streaming for selected assets.
- Historical data analysis with AI-driven insights.
- Sentiment analysis based on news articles and social media.
- Trend prediction using machine learning algorithms.
- User-friendly dashboard with charts and graphs.
- Notifications for significant price movements or trends.
- Customizable alerts based on user-defined conditions.

The 'aipriceaction' package will be central to fetching market data, building AI contexts, and integrating LLMs for insightful analysis.