AI Analysis
The package amesa-core v0.30.0 exhibits minimal risks across all categories analyzed. While there are slight concerns regarding metadata and package maintenance, these do not rise to the level of indicating malicious activity or a supply-chain attack.
- Low network, shell, obfuscation, and credential risks.
- Metadata suggests potential low maintenance, but no clear malicious intent.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some signs of low maintenance and lack of author information, but there are no clear indicators of malicious intent.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (1344 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: amesa.com>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based educational tool using the 'amesa-core' package that implements the Machine Teaching paradigm. This tool will allow educators to create interactive lessons and quizzes tailored to individual student needs based on their performance data. The application should include the following key features: 1. User Authentication: Allow teachers to register, log in, and manage their classes. 2. Lesson Creation: Teachers should be able to create lessons that include various types of content such as text, images, and videos. 3. Quiz Generation: Automatically generate quizzes based on the lesson content, adjusting difficulty levels according to each student's understanding. 4. Performance Tracking: Track each student's performance over time and provide insights into areas where they need improvement. 5. Adaptive Learning Paths: Suggest personalized learning paths based on the student's performance data. 6. Reporting: Provide detailed reports for teachers about class performance and individual student progress. To utilize the 'amesa-core' package, integrate its machine teaching functionalities to dynamically adjust the quiz generation process based on real-time feedback from students. Use 'amesa-core' to analyze student responses, identify knowledge gaps, and adapt future questions to better suit the student's learning pace and style. Additionally, leverage 'amesa-core' to suggest new learning materials and exercises that target specific areas of weakness identified through the analysis of quiz results.
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