🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate network risk due to external data transmission, while other risks such as shell execution and credential harvesting are minimal. However, the missing maintainer information adds uncertainty.

Moderate network risk due to potential data exfiltration
Missing maintainer details increases suspicion

Per-check LLM notes

Network: The observed network call suggests the package may be sending usage data to an external server, which could indicate legitimate telemetry but also raises concerns about potential data exfiltration.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The maintainer's author name is missing and they appear to be new or inactive, raising some concerns but not definitive proof of malintent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

10 test file(s) detected (e.g. test_config.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://aistatus.cc/docs
Detailed PyPI description (13133 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

227 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 48 commits in fangxm233/aistatus-python
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

one: try: urllib.request.urlopen( urllib.request.Request(
uest.urlopen( urllib.request.Request( f"{self._base_url}/api/usage/up
"aistatus.uploader.urllib.request.urlopen", lambda *_args, **_kwargs: (_ for _ in
ch.setattr("aistatus.uploader.urllib.request.urlopen", fake_urlopen) payload = {"records": [{"ema
e: self._client = httpx.Client(timeout=self._timeout) return self._client def
self._async_client = httpx.AsyncClient(timeout=self._timeout) if not self._shutdown_registe

✓ 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: aistatus.cc>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository fangxm233/aistatus-python appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 aistatus

Develop a Python-based command-line tool named 'AIWorkbench' that leverages the 'aistatus' package to manage interactions with multiple large language models (LLMs). This tool should provide users with a seamless experience by automatically routing requests to the most suitable LLM based on real-time status awareness and pre-flight checks. Additionally, it should offer fallback mechanisms to ensure reliability when primary models are unavailable or underperforming. Here are the key steps and features to include in your project:

1. **Setup**: Begin by installing the necessary packages including 'aistatus'. Ensure you have at least two LLMs configured as part of your setup.
2. **Status Awareness**: Implement functionality within AIWorkbench to query the status of each LLM before sending any request. Use 'aistatus' to check if the model is available, responsive, and capable of handling the type of request being made.
3. **Request Routing**: Based on the status information obtained from step 2, route user requests to the best-suited LLM. If multiple models are suitable, consider implementing a simple load balancing algorithm.
4. **Pre-flight Checks**: Before sending a request to an LLM, perform pre-flight checks using 'aistatus' to ensure the request meets the model's requirements. This could include checking for input length limits, supported languages, etc.
5. **Fallback Mechanism**: In case the primary LLM fails to respond or does not meet the required criteria, AIWorkbench should automatically reroute the request to a secondary LLM. Utilize 'aistatus' to handle this process smoothly.
6. **User Interface**: Design a clean, intuitive CLI interface where users can interact with AIWorkbench. Include options for submitting text queries, specifying preferred LLMs, and viewing status updates.
7. **Logging & Reporting**: Implement logging capabilities to track all interactions with LLMs through AIWorkbench. Users should be able to review these logs for troubleshooting or analysis purposes.
8. **Configuration Management**: Allow users to configure their preferred settings and LLM preferences easily through a configuration file or command-line options. 'aistatus' should facilitate easy management of these configurations.

By following these guidelines, you will create a robust, user-friendly tool that maximizes the potential of LLMs while minimizing downtime and errors.