amesa-core

v0.30.0 safe
3.0
Low Risk

the Amesa core functionality required to apply the Machine Teaching paradigm

🤖 AI Analysis

Final verdict: SAFE

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)

○ 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 (1344 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: amesa.com>

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

  • Author name is missing or very short
  • Author "" 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 amesa-core
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.

💬 Discussion Feed

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