Missing

v5.2 safe
3.0
Low Risk

Special Missing objects used in Zope.

🤖 AI Analysis

Final verdict: SAFE

The package has low risks across all categories except for metadata, where incomplete maintainer information suggests a potential issue with developer experience or activity level.

  • No network calls detected.
  • No shell execution detected.
  • Incomplete maintainer information.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
  • Metadata: The maintainer information is incomplete and may indicate a less experienced or potentially inactive developer.

🔬 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: zope.dev>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository zopefoundation/Missing appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 Missing 
Create a Python-based web application named 'MissingManager' that leverages the 'Missing' package from Zope to manage and display data with special missing values. This application will serve as a demonstration of how the 'Missing' package can be integrated into real-world applications to handle incomplete datasets more effectively than traditional NULL or NaN representations.

Step 1: Set up your development environment. Ensure you have Python installed along with Flask (for web framework), SQLAlchemy (for ORM capabilities), and install the 'Missing' package from Zope.

Step 2: Design the database schema using SQLAlchemy to include tables that can contain 'Missing' objects. These tables should represent various types of data where missing values might occur, such as user profiles, product information, etc.

Step 3: Implement CRUD operations (Create, Read, Update, Delete) for these tables. Pay special attention to how 'Missing' objects are handled during creation, reading, updating, and deletion processes.

Step 4: Develop a front-end interface using HTML/CSS/JavaScript that allows users to interact with the data stored in the database. Users should be able to add new records, view existing records, update records, and delete records. When viewing or editing records, ensure that 'Missing' values are displayed and handled appropriately.

Suggested Features:
- Detailed documentation on how 'Missing' objects are used within the application.
- A comparison feature that shows how 'Missing' objects behave differently compared to traditional NULL or NaN values.
- An option to filter out records based on whether certain fields contain 'Missing' values.
- A feature that automatically fills in 'Missing' values with default data or user-defined values.

Remember, the goal of this project is not just to create a functional application but also to showcase the unique benefits and use cases of the 'Missing' package in managing incomplete datasets.