🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network usage, shell execution, obfuscation, and credential handling. However, the metadata quality and maintainer activity levels raise some concerns, making the overall assessment suspicious.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls suggest normal operation without external dependencies.
Shell: No shell execution suggests the package does not execute external commands.
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 activity and poor metadata quality, raising some suspicion but not strong indicators of malicious intent.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (4947 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

9 type-annotated function signatures (partial)

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

No author email provided

✓ 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 ai-benchmarking

Create a mini-application that leverages the 'ai-benchmarking' Python package to evaluate the performance of Large Language Models (LLMs) in suicide risk assessment. This tool will help researchers and mental health professionals understand how accurately and safely different LLMs can interpret responses on the Columbia-Suicide Severity Rating Scale (C-SSRS). Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing necessary packages including 'ai-benchmarking'. Ensure you have the latest version of 'ai-benchmarking' installed.

2. **Data Collection**: Gather a dataset of responses to the C-SSRS questions from various individuals. These responses should include a mix of low-risk, moderate-risk, and high-risk statements.

3. **Model Integration**: Integrate at least three different LLMs into your application. Each model should be tested against the collected dataset to assess its ability to correctly identify suicide risk levels.

4. **Benchmarking Process**: Use the 'ai-benchmarking' package to run benchmarks on each LLM. The benchmarks should measure both the accuracy of risk level identification and the safety of the model's output, ensuring no inappropriate recommendations are made.

5. **Results Visualization**: Develop a user-friendly interface where users can input their own C-SSRS responses and receive a risk level assessment from each integrated LLM. Additionally, display comparative visualizations showing the performance metrics of each model.

6. **Security and Ethical Considerations**: Implement measures to ensure the security of user data and adhere to ethical guidelines regarding suicide risk assessments. This includes anonymizing data, providing clear disclaimers about the limitations of AI in mental health assessment, and ensuring that all interactions are handled with sensitivity.

7. **Feedback Mechanism**: Include a feedback system where users can report any inaccuracies or concerns they have about the model's assessments. This feedback will be crucial for continuous improvement of the models and the benchmarking process.

8. **Documentation and Reporting**: Finally, document your findings and create comprehensive reports summarizing the performance of each LLM. Highlight areas where improvements can be made and discuss the broader implications of using AI in suicide risk assessment.

By following these steps, you'll develop a valuable tool that not only evaluates the effectiveness of LLMs in suicide risk assessment but also promotes ethical AI development in sensitive domains.