arneso-pypitemplate-instance

v0.4.3 suspicious
4.0
Medium Risk

Arneso Pypitemplate Instance

πŸ€– AI Analysis

Final verdict: SUSPICIOUS

The package has low risks for network and shell activities, but the metadata analysis shows signs of potential risk due to low activity and a new maintainer, raising concerns about its legitimacy.

  • Low network and shell risk
  • Metadata risk due to low activity and new maintainer
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.
  • Metadata: Low activity and new maintainer suggest potential risk, but no clear indicators of malicious intent.

πŸ“¦ Package Quality Overall: Medium (5.4/10)

β—ˆ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
β—ˆ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://arneso-ssb.github.io/arneso-pypitemplate-instance
  • Detailed PyPI description (3184 chars)
β—ˆ Medium Contributing Guide 7.0

Some contribution signals present

  • Separate author ("Arne SΓΈrli") and maintainer ("Statistics Norway, IT-partner Department (703)") listed
  • Development Status classifier >= Beta
β—‹ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
β—ˆ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in arneso-ssb/arneso-pypitemplate-instance
  • Two distinct contributors found

πŸ”¬ Heuristic Checks

βœ“ Outbound Network Calls

No suspicious network call patterns found

βœ“ Code Obfuscation

No obfuscation patterns detected

βœ“ Shell / Subprocess Execution

No shell execution patterns detected

βœ“ Credential Harvesting

No credential harvesting patterns detected

βœ“ Typosquatting

No typosquatting candidates detected

βœ“ Registered Email Domain

Email domain looks legitimate: ssb.no

βœ“ 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 2.0

1 maintainer concern(s) found

  • Author "Arne SΓΈrli" appears to have only 1 package on PyPI (new or inactive account)
βœ“ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

πŸ’‘ AI App Starter Prompt

Use this prompt to build a project with arneso-pypitemplate-instance
Create a simple yet engaging personal finance tracker application using the 'arneso-pypitemplate-instance' package. This application will allow users to log their daily expenses and incomes, categorize them, and generate monthly financial reports. The app should be user-friendly, allowing for easy input of transactions and viewing of summaries and graphs. Here’s a detailed plan on how to implement this project:

1. **Setup Environment**: Begin by setting up your Python environment and installing the 'arneso-pypitemplate-instance' package. Use pip for installation if it's not available via conda.
2. **Design User Interface**: Design a simple command-line interface (CLI) where users can interact with the app. Consider adding options like adding new transactions, viewing transaction history, and generating reports.
3. **Implement Core Functionality**:
   - **Transaction Management**: Allow users to add both income and expense transactions. Each transaction should have details such as date, amount, category (e.g., food, entertainment), and notes.
   - **Categorization**: Implement a system where transactions can be categorized into predefined groups (like groceries, bills, etc.) or custom categories chosen by the user.
4. **Reporting Features**: Develop functionality to generate monthly financial reports based on the logged transactions. These reports should include total income, total expenses, net balance, and graphical representations of spending patterns.
5. **Utilizing 'arneso-pypitemplate-instance'**: Utilize the 'arneso-pypitemplate-instance' package to streamline the setup process and manage the application's structure. Specifically, leverage its templating capabilities to dynamically generate reports and summaries, ensuring that the output is both informative and visually appealing.
6. **Testing and Refinement**: After implementing the basic functionalities, test the application thoroughly. Pay special attention to edge cases and user inputs that might cause errors. Refine the UI based on feedback from initial testers.
7. **Documentation**: Finally, write comprehensive documentation for the application, detailing how to install, use, and customize it. Include examples of how to integrate additional features or modify existing ones.

By following these steps, you'll create a practical and efficient personal finance management tool that leverages the power of 'arneso-pypitemplate-instance' for enhanced user experience.

πŸ’¬ Discussion Feed

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