🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network calls, shell execution, obfuscation, and credential harvesting. However, its metadata suggests it may be newly created with limited maintainer history, raising some suspicion.

Low risk in network calls, shell execution, obfuscation, and credential harvesting.
Suspicious metadata indicating new package with little maintainer history.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function.
Shell: No shell executions detected, indicating no immediate risk of command injection or unauthorized system access.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of being newly created and lacks maintainer history, which raises suspicion.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. test_tracking.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (5592 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

76 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

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: example.edu>

✓ 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

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 algo-learn

Create an educational web application using Flask that allows users to learn about sorting algorithms through interactive visualizations. The application will utilize the 'algo-learn' Python package to track and display each step of the sorting process in real-time. Users will be able to select different sorting algorithms (such as Bubble Sort, Quick Sort, Merge Sort, etc.), input their own array of numbers, and watch as the algorithm sorts the data step-by-step.

Key Features:
1. User Interface: A clean, user-friendly interface where users can choose from a variety of sorting algorithms and input their own array of integers.
2. Real-Time Visualization: Use 'algo-learn' to dynamically visualize each step of the sorting process, highlighting elements being compared and swapped.
3. Algorithm Explanation: For each step shown, provide a brief explanation of what is happening in plain language to help users understand the mechanics of the algorithm.
4. Speed Control: Allow users to control the speed at which the algorithm runs, from slow (for detailed observation) to fast (for a quick overview).
5. Reset and Shuffle: Provide buttons to reset the array to its original state or shuffle it randomly for another demonstration.
6. Additional Information: Include a section on each algorithm's time complexity, space complexity, and use cases.

How to Utilize 'algo-learn':
- Import 'algo-learn' in your Flask app to enable tracking and visualization of algorithm steps.
- Use 'algo-learn' functions to generate the step-by-step visualization data based on user inputs and selected algorithms.
- Integrate these visualizations into your Flask templates to display them alongside user inputs and controls.