AI Analysis
Final verdict: SUSPICIOUS
The package exhibits low technical risks but raises concerns due to a lack of maintainer information and minimal repository activity.
- Metadata risk score of 6/10 due to suspicious metadata
- No provided description or maintainer details
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of being potentially suspicious due to lack of maintainer information and minimal repository activity.
Package Quality Overall: Low (2.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ 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
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 100 commits in anulum/director-aiTwo distinct contributors found
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
Email domain looks legitimate: anulum.li>
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 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 backfire-kernel
Create a real-time decision-making assistant application using the 'backfire-kernel' package. This application will serve as a director-class AI system, capable of providing instant advice and decision support based on user inputs. The application should have a user-friendly interface where users can input various scenarios or problems they face, and receive immediate recommendations from the AI. Key Features: 1. User Input Interface: A simple text-based input field where users can describe their situation or problem. 2. Decision Support System: Utilize the 'backfire-kernel' package to process user inputs within a 50ms safety gate, ensuring quick and reliable responses. 3. Recommendation Display: Present the AI's recommendation or advice in a clear, concise manner. 4. Feedback Loop: Allow users to provide feedback on the AI's recommendations, helping to improve future responses. 5. Historical Data Storage: Store past interactions and feedback for analysis and continuous improvement of the AI's performance. How to Use 'backfire-kernel': - Initialize the 'backfire-kernel' package at the start of your application to set up the AI processing capabilities. - Implement a function that takes user inputs and passes them through the 'backfire-kernel' for processing, ensuring all operations stay within the 50ms safety gate for real-time response. - Design a mechanism to capture user feedback and use it to refine the AI's decision-making process over time. - Consider integrating additional features such as sentiment analysis or context-awareness to enhance the AI's understanding and responsiveness.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue