🤖 AI Analysis

Final verdict: SAFE

The package has low risks across all categories except metadata, where it needs further investigation due to the maintainer's newness and lack of PyPI classifiers.

No network or shell risks detected
Low obfuscation and credential risks
Metadata risk due to maintainer's newness and missing classifiers

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function.
Shell: No shell execution detected, which is typical and does not suggest any immediate risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low risk but requires further investigation due to the maintainer's newness and lack of PyPI classifiers.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (2316 chars)

○ 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-data

Create a data management tool called 'AlgoDataHub' that leverages the 'algomancy-data' package to provide users with an efficient way to manage, manipulate, and analyze datasets. This tool should allow users to import datasets from various sources (CSV, Excel, SQL databases), clean and preprocess data, apply basic statistical analysis, and visualize the data through charts and graphs. Additionally, the tool should support exporting processed data back into different formats or directly to a database. Here are the specific steps and features for your application:

1. **Setup Environment**: Begin by setting up a Python environment with the necessary libraries including 'algomancy-data', pandas, numpy, matplotlib, seaborn, and sqlalchemy.
2. **User Interface**: Design a simple yet intuitive command-line interface (CLI) or a basic GUI using Tkinter for ease of use.
3. **Data Import**: Implement functionality to import datasets from CSV files, Excel spreadsheets, and SQL databases. Use 'algomancy-data' to streamline the process of handling these different data sources.
4. **Data Cleaning**: Provide options to clean the imported data. This includes removing duplicates, handling missing values, and converting data types as needed. Utilize 'algomancy-data' for its data cleaning utilities.
5. **Statistical Analysis**: Offer basic statistical operations like calculating mean, median, mode, standard deviation, etc., on the cleaned data. Leverage 'algomancy-data' for any advanced statistical functions it provides.
6. **Data Visualization**: Integrate visualization capabilities to plot histograms, scatter plots, bar charts, etc., based on user selections. Ensure that 'algomancy-data' is used for any specialized visualizations it supports.
7. **Export Options**: Enable users to export their processed data back into CSV, Excel, or SQL databases. Again, utilize 'algomancy-data' for any additional export formats it supports.
8. **Documentation & Testing**: Write comprehensive documentation explaining how to use each feature and include unit tests for all major functionalities to ensure reliability.

This project aims to showcase the versatility and power of 'algomancy-data' in real-world applications, providing a valuable tool for data enthusiasts and professionals alike.