🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential harvesting attempts. The only notable concern is the maintainer's limited package history, but this alone does not suggest malicious intent.

No network calls detected
Maintainer has only one package

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious flags were raised.

📦 Package Quality Overall: Low (4.0/10)

○ 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 (6617 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ 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 27 commits in PKU-ONELab/where-do-llms-go-wrong
Single author but highly active (27 commits)

🔬 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

Repository PKU-ONELab/where-do-llms-go-wrong appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Jiatao Li" 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-reviewer-diagnostics

Develop a Python-based mini-application called 'ReviewAnalyzer' that leverages the 'ai-reviewer-diagnostics' package to evaluate the effectiveness of automated peer-review systems. This application will serve as a diagnostic tool for researchers and developers working on AI-driven review systems. Here's a step-by-step guide on what your application should accomplish:

1. **Setup and Installation**: Ensure that the 'ai-reviewer-diagnostics' package is installed via pip or included in a requirements.txt file.
2. **Data Input**: Allow users to input or upload datasets containing peer-reviewed content and corresponding reviews. These datasets should include both original texts and the reviews they received.
3. **Aspect Selection**: Implement a feature where users can select specific aspects of the reviews to focus on (e.g., clarity, relevance, depth). This selection will guide the type of perturbations applied to the data.
4. **Perturbation Application**: Utilize the 'ai-reviewer-diagnostics' package to apply aspect-guided perturbations to the dataset. For instance, if focusing on 'clarity', the application should modify the texts in ways that might affect how clear they are perceived.
5. **Evaluation Metrics**: Integrate evaluation metrics provided by 'ai-reviewer-diagnostics' to assess how well the automated review system performs under different perturbation conditions. Display these results in a user-friendly format.
6. **Visualization**: Create visual representations of the evaluation results, such as graphs or charts, to help users quickly understand the impact of different perturbations on the review system's performance.
7. **Report Generation**: Automatically generate comprehensive reports summarizing the findings from the evaluations. These reports should include detailed analyses, visualizations, and recommendations for improving the review system based on the diagnostics.
8. **User Interface**: Develop a simple yet effective command-line interface (CLI) for interacting with 'ReviewAnalyzer'. Consider adding additional features like saving/loading configurations or exporting reports in various formats (PDF, CSV).

By following these steps and utilizing the capabilities of 'ai-reviewer-diagnostics', your application will provide valuable insights into the robustness and reliability of automated peer-review systems.