🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators with no network calls, shell executions, or credential risks. However, the low metadata score due to the maintainer's limited presence and lack of a GitHub repository slightly increases uncertainty.

No network calls or shell executions detected.
Maintainer has only one package and no GitHub repository.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package and lacks a GitHub repository, which may indicate less transparency and community involvement.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: stu.sufe.edu.cn

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "FinPhd" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with AnomalyLab

Create a financial anomaly detection tool using the 'AnomalyLab' Python package. This tool will analyze historical stock market data to identify patterns that deviate from normal behavior, which could indicate potential investment opportunities or risks. The tool should be able to perform the following tasks:

1. **Data Importation**: Allow users to upload CSV files containing historical stock price data (e.g., Open, Close, High, Low prices, Volume).
2. **Preprocessing**: Clean and preprocess the imported data to ensure it's ready for analysis. This includes handling missing values, normalizing data, and possibly transforming it into a time series format.
3. **Anomaly Detection**: Utilize 'AnomalyLab' to detect anomalies in the stock price movements. The tool should offer different anomaly detection methods provided by the package, such as statistical tests, machine learning models, or any other relevant techniques available in 'AnomalyLab'.
4. **Visualization**: Provide visual representations of the detected anomalies alongside the original data. This could include line charts showing stock prices over time, scatter plots highlighting unusual data points, or heatmaps indicating periods of high anomaly activity.
5. **Report Generation**: Automatically generate a report summarizing the findings. The report should include key metrics, visualizations, and a brief explanation of the detected anomalies, along with their potential implications for investors.
6. **User Interface**: Develop a simple web-based user interface using Flask or Django that allows users to easily import data, select preprocessing options, choose anomaly detection methods, and view results.

For each feature, you should detail how 'AnomalyLab' is integrated into the process. For example, when detecting anomalies, describe how specific functions from 'AnomalyLab' are called to perform the analysis. Additionally, provide sample code snippets demonstrating how to use 'AnomalyLab' within the context of your application.