🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network calls, shell execution, obfuscation, and credential harvesting. However, it has missing author information and lacks a GitHub repository, raising concerns about its legitimacy and transparency.

Missing author information
Lack of GitHub repository

Per-check LLM notes

Network: No network calls detected, which is low risk.
Shell: Detected shell execution may be for version control purposes but requires further investigation to confirm legitimacy.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has some red flags including missing author information and lack of a GitHub repository, but no clear signs of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

try: completed = subprocess.run( ["git", *args], cwd=_REPO_ROOT,

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ndcn.ox.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 DiagnoseHarmonisation

Create a mini-application called 'HarmonyChecker' using Python and the 'DiagnoseHarmonisation' package. This tool aims to simplify the process of diagnosing issues related to data harmonization in various datasets. The application should be user-friendly and capable of providing quick insights into whether the data preprocessing steps have been correctly applied. Here’s a detailed breakdown of what your application should include:

1. **Data Input Module**: Allow users to upload multiple CSV files or connect to a database to pull data. Ensure that the application supports common file formats and can handle large datasets efficiently.
2. **Pre-Harmonization Diagnostic**: Utilize the 'DiagnoseHarmonisation' package to run diagnostics on the raw data before any harmonization processes are applied. The diagnostics should check for consistency, completeness, and other potential issues that could affect the harmonization process.
3. **Harmonization Process**: Implement a basic set of harmonization techniques based on user-defined rules or default settings provided by the 'DiagnoseHarmonisation' package. These techniques could include standardizing date formats, handling missing values, or aligning categorical variables across different datasets.
4. **Post-Harmonization Diagnostic**: After applying the harmonization techniques, use 'DiagnoseHarmonisation' again to verify if the issues identified in the pre-harmonization phase have been resolved. Highlight any remaining problems that need further attention.
5. **Report Generation**: Automatically generate a comprehensive report summarizing the findings from both the pre- and post-harmonization diagnostics. Include visualizations where appropriate to make the results more understandable.
6. **User Interface**: Develop a simple web-based interface using Flask or Django to allow non-technical users to interact with the application easily. The UI should guide users through each step of the process and display results in an accessible manner.
7. **Documentation and Help**: Provide thorough documentation explaining how to use the application effectively, including examples and troubleshooting tips.

By completing these steps, you will create a valuable tool that streamlines the data harmonization process, ensuring that datasets are ready for analysis without manual intervention.