🤖 AI Analysis

Final verdict: SAFE

The package appears to serve a legitimate purpose with minimal risks identified. While there are some concerns regarding metadata, the lack of malicious activities detected makes it safe to consider.

Low risk scores across all categories except metadata.
No evidence of malicious intent or harmful actions.

Per-check LLM notes

Network: The use of httpx for network calls is common and does not inherently suggest malicious activity; however, the lack of context about the purpose of these calls warrants further investigation.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some red flags, including an author with missing details and a new or inactive account, but there's no direct evidence of malicious intent.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 19 test file(s) found

Test runner config found: conftest.py
Test runner config found: pyproject.toml
19 test file(s) detected (e.g. __init__.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (31011 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 7.0

Partial type annotation coverage

Classifier: Typing :: Typed
142 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 87 commits in utsmok/aireloom
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

sing httpx.""" async with httpx.AsyncClient() as client: logger.debug(f"Requesting raw data from

✓ 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: utwente.nl>

✓ 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 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 aireloom

Develop a research collaboration recommendation system using the 'aireloom' Python package. This system will help researchers find potential collaborators based on their publications and research interests. Here’s a detailed plan for building this mini-app:

1. **Setup and Installation**
   - Install the 'aireloom' package along with other necessary Python libraries such as pandas, numpy, and scikit-learn.
   - Configure your environment to use the OpenAIRE Graph API.

2. **Data Collection**
   - Use 'aireloom' to query the OpenAIRE Graph API for publication data related to specific research fields.
   - Collect metadata such as author names, affiliations, publication titles, and abstracts.

3. **Data Processing**
   - Clean and preprocess the collected data to ensure it is suitable for analysis.
   - Extract key information from publication abstracts using Natural Language Processing techniques.

4. **Collaboration Recommendation Engine**
   - Implement a recommendation engine that suggests potential collaborators based on shared research interests and co-authorship patterns.
   - Utilize similarity measures and clustering algorithms to identify groups of researchers working on similar topics.

5. **User Interface**
   - Develop a simple web interface using Flask or Django where users can input their research field and receive recommendations.
   - Ensure the UI is user-friendly and provides relevant details about recommended collaborators.

6. **Testing and Deployment**
   - Test the application thoroughly to ensure accuracy and reliability of recommendations.
   - Deploy the app using services like Heroku or AWS for public access.

**Suggested Features:**
- Integration with ORCID for user authentication and identification.
- Visualization of collaborative networks using libraries such as NetworkX and Plotly.
- Option to filter recommendations based on geographical proximity or institutional affiliation.
- Real-time updates of the recommendation list based on new publications.

The 'aireloom' package is utilized extensively throughout the project for its ability to interact with the OpenAIRE Graph API. It serves as the backbone for fetching publication data, which is then processed and analyzed to generate meaningful insights and recommendations. By leveraging 'aireloom', you can efficiently tap into a vast repository of academic data to enhance the functionality and value of your recommendation system.