🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of risk with no obfuscation or credential harvesting detected. The metadata and network/shell risks are within acceptable levels for a legitimate package.

No obfuscation or credential harvesting detected
Network and shell risks are moderate but within expected bounds for a legitimate package

Per-check LLM notes

Network: The network calls seem to be part of normal HTTP request handling, possibly for fetching external resources or updates.
Shell: Subprocess calls to create virtual environments and install dependencies appear standard for package management tasks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting the package does not pose a risk for secret theft.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

""" try: return requests.get(url, timeout=timeout) except requests.RequestException:
} response = requests.post(self.__url, data=payload, timeout=(5, 15)) if respo

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

") try: subprocess.run([sys.executable, "-m", "venv", str(venv_path)], check=True)
("bin") / "pip") subprocess.run([str(pip_executable), "install", "-r", str(requirements_path

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: alchimiedatasolutions.com

✓ 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 "Olivier Siguré" 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 adstoolbox

Create a simple yet powerful data analysis tool using Python's 'adstoolbox' package. This tool will serve as a quick way to analyze datasets, perform basic statistical operations, and visualize data trends. Here’s a detailed breakdown of the project requirements and steps to achieve it:

1. **Project Overview**: Develop a command-line interface (CLI) tool named 'DataAnalyzer'. This tool should allow users to load CSV files, perform statistical analyses on numerical columns, and generate visualizations.

2. **Features**:
- **Data Loading**: Implement functionality to read CSV files into pandas DataFrames.
- **Statistical Analysis**: Utilize 'adstoolbox' to calculate mean, median, mode, standard deviation, variance, and correlation coefficients for selected columns.
- **Visualization**: Use matplotlib or seaborn to create histograms, scatter plots, and line graphs based on user input.
- **Interactive Mode**: Provide an interactive shell where users can explore the loaded dataset further without restarting the program.

3. **Utilizing 'adstoolbox'**:
- For statistical analysis, use 'adstoolbox.stats' to handle all statistical computations efficiently.
- Ensure that 'adstoolbox' simplifies the process of accessing and manipulating data within the DataFrame.

4. **Implementation Steps**:
- Step 1: Set up the project environment and install necessary packages including 'pandas', 'matplotlib', 'seaborn', and 'adstoolbox'.
- Step 2: Design the CLI interface using argparse for handling commands and arguments.
- Step 3: Implement data loading functionality that supports CSV files.
- Step 4: Integrate 'adstoolbox.stats' for performing statistical calculations on the loaded data.
- Step 5: Add visualization capabilities allowing users to choose between different types of plots based on their needs.
- Step 6: Create an interactive mode that allows users to interact with the loaded data directly from the command line.

5. **Testing and Documentation**:
- Write unit tests for each feature to ensure reliability.
- Document the code thoroughly and provide a README file explaining how to install and use the tool.