acdh-arche-assets

v3.30.1 suspicious
4.0
Medium Risk

A set of static assets used (mainly) for ARCHE data preprocessing

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low individual risk factors but exhibits suspicious metadata, including non-HTTPS links and low repository activity, raising concerns about its legitimacy and security.

  • Suspicious non-HTTPS links
  • Low repository activity
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package relies on external services.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Suspicious non-HTTPS links and low activity in the repository suggest potential risks, but lack of clear malicious intent or typosquatting.

🔬 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: oeaw.ac.at

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://sws.geonames.org/1232324343/linz.html
  • Non-HTTPS external link: http://www.nationalarchives.gov.uk/PRONOM/Default.aspx
⚠ 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 "Mateusz Żółtak" 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 acdh-arche-assets 
Create a mini-application that assists researchers in preparing data for the ARCHE repository using the 'acdh-arche-assets' Python package. This application will streamline the process of uploading and preprocessing datasets by automating certain tasks. Here’s a detailed plan on how to approach this project:

1. **Project Overview**: Develop a command-line tool that leverages 'acdh-arche-assets' to facilitate the preparation of data for submission to the ARCHE repository. The tool should include functionalities such as data cleaning, transformation, and validation.

2. **Core Features**:
   - **Data Import**: Allow users to import data from various formats (CSV, Excel, JSON).
   - **Data Cleaning**: Implement basic data cleaning operations like removing duplicates, handling missing values, and standardizing formats.
   - **Transformation**: Provide options to transform data according to ARCHE standards, including renaming columns, adding metadata, and formatting dates.
   - **Validation**: Ensure the cleaned and transformed data meets ARCHE's submission requirements through automated checks.
   - **Export**: Enable users to export the processed data back into supported formats, ready for upload.

3. **Using 'acdh-arche-assets'**: Integrate 'acdh-arche-assets' to provide pre-configured templates and stylesheets for ARCHE submissions. Use its utilities to apply these configurations during the transformation phase.

4. **Implementation Steps**:
   - Set up a Python environment and install necessary packages including 'acdh-arche-assets'.
   - Design a user-friendly CLI interface allowing interaction with the tool.
   - Implement each feature described above, ensuring seamless integration with 'acdh-arche-assets'.
   - Test the application thoroughly with different types of datasets to ensure reliability and accuracy.

5. **Additional Enhancements**:
   - Add support for visualizing data before and after processing.
   - Include documentation and examples for common use cases.
   - Consider integrating with other tools or services for additional functionality.

6. **Deployment**: Once developed and tested, consider deploying the application as a standalone executable or as a web-based service for broader accessibility.

This project aims to significantly ease the burden of data preparation for researchers working with ARCHE, enhancing efficiency and compliance with submission standards.