automate-teaching

v1.0.4 safe
3.0
Low Risk

Functions to assist in course management, particularly economics.

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories with no network calls, minimal shell execution that appears benign, and no signs of obfuscation or credential harvesting. The metadata risk is slightly elevated due to low activity but does not indicate malicious intent.

  • No network calls detected.
  • Shell executions appear benign.
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell executions appear to be related to converting Jupyter notebooks and other benign operations.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Low activity and metadata quality suggest potential low effort or inactivity, but no clear signs of malicious intent.

📦 Package Quality Overall: Low (2.6/10)

○ 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
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 19 commits in letsgoexploring/automate_teaching
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • notebook_name + '.ipynb' subprocess.run( ['jupyter', 'nbconvert', '--to', 'script', notebook
  • md.append(notebook_name) subprocess.run(cmd, check=True) import pandas as pd import numpy as np fro
  • ', tex_path.name] subprocess.run(cmd, **run_args) subprocess.run(cmd, **run_args)
  • (cmd, **run_args) subprocess.run(cmd, **run_args) # second pass for references p
  • ss.DEVNULL) try: subprocess.run(compiler_cmd, **run_args) subprocess.run(compiler_cm
  • iler_cmd, **run_args) subprocess.run(compiler_cmd, **run_args) subprocess.run(bibtex_cmd,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: uci.edu

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Brian C. Jenkins" 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 automate-teaching 
Create a mini-application called 'EcoTutor' which leverages the 'automate-teaching' Python package to assist educators in managing their economics courses more efficiently. EcoTutor should include several core functionalities that enhance course management and student engagement. Here are the steps and features you need to implement:

1. **Course Setup**: Allow users to set up new courses with basic information such as course name, instructor details, start/end dates, and syllabus outline. Use the 'automate-teaching' package to automatically generate a structured syllabus based on common economics topics.

2. **Student Enrollment**: Implement a feature where students can enroll in the course. Each student profile should include basic information like name, email, and a unique identifier. Utilize 'automate-teaching' to manage student enrollment data effectively.

3. **Resource Management**: Enable instructors to upload and manage course materials such as lecture notes, videos, and readings. The 'automate-teaching' package should facilitate categorizing these resources according to topics and making them accessible to students.

4. **Discussion Forums**: Integrate discussion forums where students can ask questions and engage in discussions related to course content. Use 'automate-teaching' to monitor and analyze forum activity, providing insights into areas of confusion or interest among students.

5. **Quizzes and Assessments**: Develop a system for creating quizzes and assessments based on the course material. The 'automate-teaching' package should help in generating questions and grading responses, providing immediate feedback to students.

6. **Progress Tracking**: Allow both instructors and students to track progress through the course. This includes completed assignments, quiz scores, and overall participation in class activities. Use 'automate-teaching' to provide personalized recommendations for improvement based on performance metrics.

7. **Notifications and Reminders**: Implement a notification system to remind students about upcoming deadlines, quiz results, and important announcements. Use 'automate-teaching' to schedule and send these notifications via email or SMS.

8. **Analytics Dashboard**: Provide an analytics dashboard for instructors to review course performance metrics such as average quiz scores, most popular topics discussed in forums, and general student engagement levels. The 'automate-teaching' package should power these analytics to give actionable insights.

In summary, EcoTutor aims to streamline course management tasks while enhancing student engagement and learning outcomes in economics education. By leveraging the 'automate-teaching' package, the application will offer robust tools for educators to create dynamic and effective learning environments.

💬 Discussion Feed

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