🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network, shell, obfuscation, and credential risks. However, the metadata risk score suggests potential issues with maintainer effort and transparency, raising suspicion about its legitimacy.

Low network, shell, obfuscation, and credential risks.
Moderate metadata risk due to signs of low maintainer effort and lack of transparency.

Per-check LLM notes

Network: The network call patterns suggest legitimate API interactions, possibly for academic search queries.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer effort and lack of transparency, which could indicate potential risks.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

try: r = requests.get( url, params=params,
try: r = requests.get(url, params=params, timeout=30) r.raise_for_
try: r = requests.get(url, timeout=30) r.raise_for_status()

✓ 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: example.com>

✓ 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

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with academic-search

Create a mini-application called 'AcademicPulse' that leverages the 'academic-search' Python package to facilitate comprehensive academic research across multiple platforms including Semantic Scholar, Crossref, OpenAlex, and PubMed. This tool will allow users to input specific keywords or phrases and then retrieve a list of relevant publications, along with statistical information about the retrieved data. Here’s how you can structure the project:

1. **Project Setup**: Begin by installing the necessary Python packages, including 'academic-search'. Ensure your development environment is set up correctly.

2. **User Interface Design**: Develop a simple yet effective command-line interface (CLI) where users can enter their query terms. Consider adding options for specifying which databases they want to search through.

3. **Integration with 'academic-search'**: Use the 'academic-search' package to connect to the various academic databases. Implement regex filters based on user inputs to refine the search results.

4. **Data Retrieval & Processing**: Write functions to fetch data from the selected databases using the API provided by 'academic-search'. Process this data to extract key details such as author names, publication dates, titles, abstracts, etc.

5. **Statistical Analysis**: Incorporate basic statistical tools to analyze the retrieved data. Provide insights such as the number of publications per year, top authors, most cited papers, etc.

6. **Output Presentation**: Display the results in a readable format, possibly sorting them based on relevance or date. Include links to full articles if available.

7. **Error Handling & User Feedback**: Implement robust error handling to manage cases where no results are found or if there are issues with the API calls. Provide meaningful feedback to the user.

8. **Testing & Validation**: Test the application thoroughly with different sets of queries and validate its performance against known datasets or manual searches.

9. **Documentation**: Prepare a README file detailing how to install and use the application. Include examples of how to run different types of queries.

This project aims to streamline academic research by providing quick access to a vast array of scholarly resources, making it easier for researchers and students to stay updated with the latest developments in their fields.