AI Analysis
Final verdict: SUSPICIOUS
The package has minimal direct risks such as network calls or shell execution, but its low-maintenance metadata suggests potential issues that warrant further investigation.
- Low metadata quality
- No package description provided
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary 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 maintenance and possibly low effort, raising some suspicion but not definitive evidence of malice.
Package Quality Overall: Low (3.6/10)
✦ High
Test Suite
9.0
Test suite present — 12 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml12 test file(s) detected (e.g. conftest.py)
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ 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
152 type-annotated function signatures detected in source
○ 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 8.0
4 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" 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 agt-eval
Create a comprehensive evaluation tool for assessing the performance of various Large Language Models (LLMs) using the 'agt-eval' Python package. This tool will allow users to input different prompts and evaluate how well each model responds based on predefined criteria such as accuracy, coherence, and relevance. The application should also provide a user-friendly interface for visualizing the results through graphs and charts. Additionally, incorporate a feature to compare multiple models simultaneously, showcasing their strengths and weaknesses side by side. Use 'agt-eval' to handle the evaluation process, ensuring that it integrates seamlessly with other Python libraries for data visualization like Matplotlib or Seaborn.