🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network, shell, obfuscation, and credential aspects, but its metadata raises concerns due to lack of maintainer history and absence of a GitHub repository.

Metadata risk due to new package without maintainer history
Lack of GitHub repository

Per-check LLM notes

Network: No network calls detected, which is low risk.
Shell: Detected use of shell execution to run git commands, likely for version control purposes but could be used for other operations; requires further investigation.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new with no maintainer history and lacks a GitHub repository, raising suspicion but not conclusive evidence of malice.

🔬 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 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 HarmonizationDiagnostics

Develop a Python-based mini-application named 'HarmonyChecker' that leverages the 'HarmonizationDiagnostics' package to assess data quality before and after harmonization processes. This tool will be particularly useful for researchers and data scientists working with diverse datasets that require standardization. The goal of 'HarmonyChecker' is to provide users with a comprehensive report on the effectiveness of their data harmonization efforts, highlighting any inconsistencies or improvements observed.

Step 1: Define the Application Scope
- Identify common issues in data harmonization such as missing values, inconsistent formats, and incompatible scales.
- Determine which metrics from the 'HarmonizationDiagnostics' package are most relevant for evaluating these issues.

Step 2: Design the User Interface
- Create a simple command-line interface (CLI) for inputting dataset paths and specifying harmonization methods.
- Implement a GUI using Tkinter for a more user-friendly experience.

Step 3: Develop Core Functionality
- Integrate 'HarmonizationDiagnostics' into your project to perform pre-harmonization diagnostics.
- Apply a basic harmonization technique (e.g., normalization, standardization) to the dataset.
- Use 'HarmonizationDiagnostics' again to conduct post-harmonization diagnostics.

Step 4: Enhance Functionality with Additional Features
- Include options for users to select specific columns for analysis.
- Allow customization of diagnostic thresholds to suit different levels of data quality assurance.
- Implement a feature to visualize changes in data quality before and after harmonization using matplotlib.

Step 5: Ensure Usability and Reliability
- Write comprehensive documentation detailing how to use 'HarmonyChecker', including examples and best practices.
- Incorporate error handling to manage invalid inputs and unexpected errors gracefully.
- Test 'HarmonyChecker' extensively with various datasets to ensure it works reliably across different scenarios.