🤖 AI Analysis

Final verdict: SAFE

The package appears safe with no detected network or shell risks. The metadata quality is low, but there are no clear signs of malicious activity.

No network or shell risks detected
Low metadata quality, but no signs of malicious intent

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
Metadata: The maintainer seems new and there's low metadata quality, but no clear signs of malicious intent.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ 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

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: cqm.nl>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Pepijn Wissing" 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 algomancy-utils

Create a data analysis and visualization tool using the 'algomancy-utils' package in Python. This tool will allow users to upload datasets, perform basic statistical analyses, generate visualizations, and save the results. Here are the key steps and features for your project:

1. **Setup**: Begin by installing 'algomancy-utils' along with other necessary packages like pandas, matplotlib, and seaborn.
2. **Data Upload**: Implement functionality to allow users to upload CSV files containing their dataset.
3. **Basic Statistics**: Utilize 'algomancy-utils' to compute basic statistics such as mean, median, mode, standard deviation, etc., on numerical columns of the uploaded dataset.
4. **Visualization**: Use 'algomancy-utils' to create various types of plots including histograms, box plots, scatter plots, etc., to visually represent the data.
5. **Interactive Features**: Add interactive elements where users can select specific columns and apply filters to view subset statistics and visualizations.
6. **Save Results**: Provide an option for users to download the generated visualizations and statistical reports in formats like PDF or PNG.
7. **Documentation**: Ensure all functions are well-documented and include a README file explaining how to use the tool.

Remember, 'algomancy-utils' offers several utilities that simplify common tasks in data analysis and visualization, making it easier to implement these features efficiently.