arcanada-model-connector

v0.1.1 suspicious
4.0
Medium Risk

Typed Python client SDK for the Arcanada Model Connector — unified API for AI CLI agents and cloud model providers.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential insecure API key handling and lack of maintainer activity. However, it does not exhibit other high-risk behaviors such as shell execution or credential harvesting.

  • Potential insecure handling of 'pi_key'
  • Sparse maintainer activity and repository inactivity
Per-check LLM notes
  • Network: The use of HTTP/HTTPS calls is common for packages that interact with external services or APIs, but the presence of 'pi_key' suggests potential unsecured API key handling.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository has no activity and the maintainer's information is sparse, raising concerns about its legitimacy.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_client.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Arcanada-one/model-connector/blob/main/do
  • Detailed PyPI description (3131 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 28 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 92 commits in Arcanada-one/model-connector
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • pi_key") self._http = httpx.Client( base_url=base_url.rstrip("/"), time
  • pi_key") self._http = httpx.AsyncClient( base_url=base_url.rstrip("/"), time
✓ 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: arcanada.one>

✓ 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 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 arcanada-model-connector 
Create a Python-based desktop application named 'AI Task Manager' using the 'arcanada-model-connector' package. This application will serve as a user-friendly interface for managing various AI tasks such as text generation, image synthesis, and data analysis, all through a unified API provided by Arcanada Model Connector.

Step 1: Set up your development environment by installing necessary libraries including 'arcanada-model-connector'. Ensure you have a basic understanding of Python and familiarity with GUI frameworks like Tkinter or PyQt.

Step 2: Design the main window layout which includes sections for task input, task selection, and result display. Implement a clean and intuitive UI design.

Step 3: Integrate 'arcanada-model-connector' into your application to handle communication with different AI services. Use the SDK to connect to at least two distinct AI service providers, ensuring you can switch between them seamlessly based on user preference.

Step 4: Develop functionalities allowing users to select from a variety of AI tasks. For instance, enable users to upload text or images and receive processed outputs according to their choices. Implement error handling for failed requests and display appropriate messages to users.

Step 5: Add advanced features such as task history logging, allowing users to review past tasks and results. Consider implementing a feature where users can save their preferred settings and models for quick access.

Step 6: Test your application thoroughly across different scenarios to ensure reliability and usability. Pay special attention to performance optimization, especially when dealing with large datasets or complex tasks.

Remember to document your code well and provide clear instructions for running the application. Your goal is to create a versatile tool that simplifies the interaction with AI services, making it accessible even for those less familiar with technical details.

💬 Discussion Feed

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