🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in network, shell, obfuscation, and credential aspects, but metadata analysis suggests low-effort maintenance and potential suspicious behavior which warrants further investigation.

Metadata risk score of 5 out of 10
Signs of low-effort and potentially suspicious maintainer behavior

Per-check LLM notes

Network: The observed network calls appear to be legitimate API interaction for authentication purposes.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting the package is not engaged in secret or credential theft activities.
Metadata: The package shows signs of low effort and potentially suspicious maintainer behavior, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://theaiarbitration.com/docs
Detailed PyPI description (4825 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

26 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 7.5

Found 5 network call pattern(s)

use client.""" resp = httpx.post( f"{base_url.rstrip('/')}/api/auth/login",
uest, **kwargs)) with httpx.Client(timeout=self._timeout) as http: resp = http.post
"stream": True} with httpx.Client(timeout=self._streaming_timeout) as http: with h
r r in requests] with httpx.Client(timeout=self._timeout) as http: resp = http.post
ormat, } with httpx.Client(timeout=60.0) as http: resp = http.post(

✓ 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 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 aiarbitration

Create a fully-functional mini-app called 'AIAdvisor' which leverages the 'aiarbitration' Python package to provide users with personalized advice on various topics such as health, finance, and technology. The app should allow users to input their specific questions or concerns, and then use the 'aiarbitration' package to route these inquiries to the most suitable AI models for accurate and relevant responses.

Steps to build the app:
1. Set up the environment by installing necessary packages including 'aiarbitration'.
2. Design a user-friendly interface where users can select their area of interest (health, finance, technology).
3. Implement a feature that allows users to input their specific questions or issues within their selected category.
4. Utilize the 'aiarbitration' package to analyze the input and route it to the appropriate AI model based on the topic and nature of the question.
5. Integrate APIs from different AI providers to ensure diverse and specialized answers.
6. Display the response from the chosen AI model back to the user in a clear and understandable format.
7. Optionally, implement a feedback system where users can rate the accuracy and helpfulness of the provided advice.
8. Ensure the app handles errors gracefully, providing clear messages when something goes wrong.

Suggested Features:
- User authentication and profile creation for personalized experiences.
- History of past queries and responses for easy reference.
- Integration with social media platforms to share advice or insights.
- Notifications for new updates or relevant information based on user preferences.

The 'aiarbitration' package will play a crucial role in the backend by managing the routing logic and ensuring that each user query is directed to the best-suited AI model available. This not only enhances the quality of responses but also optimizes resource usage by avoiding unnecessary computations.