avenir-spectrum-import-pjnz

v0.1.5 safe
4.0
Medium Risk

PJNZ import for Spectrum Engine

🤖 AI Analysis

Final verdict: SAFE

The package shows no direct malicious activities such as network calls, shell executions, or obfuscations. However, low maintainer activity and poor metadata quality suggest some caution is warranted.

  • Low maintainer activity
  • Poor metadata quality
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating no direct system command execution by the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential risks but does not conclusively point to malicious intent.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (245 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

  • 11 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

No author email provided

✓ 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 avenir-spectrum-import-pjnz 
Create a mini-application called 'Spectrum Importer' that leverages the 'avenir-spectrum-import-pjnz' Python package to manage and process data from the Spectrum Engine. This application will serve as a user-friendly interface for importing, filtering, and exporting data based on specific criteria.

Step 1: Set up the Project
- Initialize a new Python project and install the 'avenir-spectrum-import-pjnz' package along with other necessary dependencies such as Pandas for data manipulation and Flask for web framework.

Step 2: Design the Application Structure
- Create a main application file that sets up the Flask server.
- Define routes for handling requests related to data import, filtering, and export.

Step 3: Implement Data Import Functionality
- Use the 'avenir-spectrum-import-pjnz' package to connect to the Spectrum Engine and retrieve data.
- Provide options for users to specify which datasets they want to import.

Step 4: Add Filtering Capabilities
- Allow users to apply filters based on various attributes of the imported data.
- Utilize Pandas functionalities to manipulate and filter the data according to user-defined criteria.

Step 5: Develop Export Options
- Enable users to export filtered data into different formats like CSV, Excel, or JSON.
- Ensure the exported files are downloadable directly from the application.

Suggested Features:
- User authentication for secure access to data.
- Real-time data updates from the Spectrum Engine.
- Graphical representation of data using libraries such as Matplotlib or Plotly.
- Support for batch processing of multiple datasets at once.

The 'avenir-spectrum-import-pjnz' package plays a crucial role in establishing the connection between your application and the Spectrum Engine, facilitating seamless data retrieval and management processes.

💬 Discussion Feed

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