AI Analysis
Final verdict: SAFE
The package appears to be legitimate with low risks across all categories except metadata, which suggests it may have some issues with maintenance or quality. There are no clear indicators of malicious intent.
- Low network, shell, obfuscation, and credential risks.
- Metadata risk due to potential low maintenance and poor quality.
Per-check LLM notes
- Network: The presence of HTTP/HTTPS calls is common and does not necessarily indicate malicious activity, but could be a part of legitimate API interactions.
- Shell: No shell execution patterns were detected, indicating no immediate risk associated with shell command execution.
- Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating safe handling of sensitive information.
- Metadata: The package shows signs of low maintenance and potentially poor quality, but lacks clear indicators of malicious intent.
Package Quality Overall: Low (4.2/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://docs.auriko.aiDetailed PyPI description (6973 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
101 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
) self._httpx = httpx.Client( base_url=self.base_url, headers=sel) self._httpx = httpx.AsyncClient( base_url=self.base_url, headers=sel
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
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "Auriko" 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 auriko
Create a fully-functional mini-app called 'SmartQuery' that leverages the Auriko package to intelligently route user queries to the most suitable Large Language Model (LLM). The app should have a simple command-line interface where users can input their questions or commands, and the app will return answers from the best-fitting LLM based on the query content. Here are the steps and features to include in your project: 1. **Setup Environment**: Ensure you have Python installed along with the necessary packages including 'auriko'. Use virtual environments for better management. 2. **User Interface**: Develop a clean, user-friendly CLI interface where users can enter their queries. 3. **Query Parsing**: Implement basic parsing capabilities to understand the nature of the user's query (e.g., general information, specific domain knowledge). 4. **Intelligent Routing**: Utilize the 'auriko' package to route the parsed queries to the most appropriate LLM based on the query type and content. For instance, if the query is about programming, it might be routed to an LLM specialized in coding languages. 5. **Response Handling**: Once the query is processed by the selected LLM, handle the response appropriately. This could involve formatting the answer for readability, ensuring it fits within certain character limits, or even summarizing long responses. 6. **Feedback Loop**: Optionally, implement a mechanism for users to rate the quality of the answers received. This feedback can then be used to refine the routing algorithm over time. 7. **Security Measures**: Ensure that all interactions are secure, especially if sensitive data might be involved in the queries. 8. **Documentation**: Provide clear documentation on how to install, use, and contribute to the SmartQuery app. 9. **Testing**: Include comprehensive testing scenarios to ensure reliability and accuracy of the routing and response handling. By following these guidelines, you'll create a powerful tool that demonstrates the potential of intelligent LLM routing using the Auriko package.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue