🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risk due to potential unauthorized updates through shell commands and low package activity, suggesting it might be maintained less frequently or by someone new.

Moderate shell risk due to git operations
Low metadata activity and a new maintainer

Per-check LLM notes

Network: No direct network calls detected, which is normal.
Shell: Git operations may be legitimate for version control, but could also indicate updates from external sources without user consent.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The low activity and new maintainer suggest potential risks, but no clear indicators of malicious intent.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

Test runner config found: pyproject.toml
6 test file(s) detected (e.g. test_bibtex_lint.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (14373 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 5.0

Partial type annotation coverage

8 type-annotated function signatures (partial)

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 21 commits in UnaryLab/ai-for-research
Single author but highly active (21 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

one: %s" % CLONE_DIR) subprocess.run(["git", "-C", str(CLONE_DIR), "pull", "--ff-only"], check=Fa
% (REPO_URL, CLONE_DIR)) subprocess.run(["git", "clone", REPO_URL, str(CLONE_DIR)], check=True)

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ 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 2.0

1 maintainer concern(s) found

Author "UnaryLab" 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 ai-for-research

Create a fully-functional mini-application that leverages the 'ai-for-research' package to enhance research productivity through automated data analysis and report generation. This application will be called 'ResearchAssistant'. It aims to streamline the process of analyzing complex datasets and summarizing findings into concise reports, making it easier for researchers to focus on their work rather than on the technical aspects of data handling.

Step-by-Step Guide:
1. **Setup**: Begin by installing the necessary packages including 'ai-for-research', pandas, matplotlib, and jinja2. Ensure that 'ai-for-research' is correctly integrated into your project's environment.
2. **Data Import**: Design a user-friendly interface where users can upload their dataset in CSV format. Utilize the 'ai-for-research' package to automatically clean and preprocess the data, ensuring that outliers and missing values are handled appropriately.
3. **Analysis**: Implement functionalities within 'ResearchAssistant' that allow for advanced statistical analyses such as regression models, clustering, and hypothesis testing using 'ai-for-research'. These analyses should be customizable based on user input regarding specific variables and hypotheses.
4. **Visualization**: Integrate 'ai-for-research' to generate insightful visualizations from the analyzed data. Users should have the ability to select preferred chart types and customize them according to their needs.
5. **Report Generation**: Use the 'jinja2' template engine to create dynamic HTML reports summarizing the analysis results. The 'ai-for-research' package can be utilized here to automate the writing of summary statistics and key insights, reducing the time needed to compile comprehensive reports.
6. **Export Options**: Provide options for users to export both the visualizations and the final report in various formats such as PDF, Word document, and HTML.
7. **User Feedback**: Incorporate a feedback loop where users can provide comments on the accuracy and relevance of the analysis and report. This feedback can then be used to improve future iterations of 'ResearchAssistant'.

Suggested Features:
- Customizable analysis settings allowing for different levels of complexity in statistical tests.
- Integration with cloud storage services like Google Drive or Dropbox for seamless data import/export.
- Real-time visualization updates during data exploration phases.
- Automated email notifications when the report generation process is complete.

Utilization of 'ai-for-research': Throughout the development of 'ResearchAssistant', the 'ai-for-research' package plays a critical role in automating the preprocessing, analysis, and reporting stages. By leveraging its capabilities, 'ResearchAssistant' can offer a powerful yet accessible tool for researchers at all levels.