agentscore-py

v2.4.3 suspicious
4.0
Medium Risk

Python client for the AgentScore APIs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of legitimacy with low risks for network, shell, obfuscation, and credential misuse. However, the metadata risk score suggests potential unreliability due to the maintainer's new or inactive account and lack of a proper author name.

  • Metadata risk indicates potential unreliability due to the maintainer's new or inactive account and lack of a proper author name.
  • Overall low risk scores for other categories suggest no immediate malicious activity.
Per-check LLM notes
  • Network: The presence of HTTP/HTTPS clients is common and may indicate legitimate API calls, but further investigation into the URLs and data being transmitted is necessary.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, indicating potential unreliability.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 7 test file(s) detected (e.g. test_mode.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (11202 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 40 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 37 commits in agentscore/python-sdk
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • self._sync_client = httpx.Client( base_url=self.base_url, hea
  • self._async_client = httpx.AsyncClient( base_url=self.base_url, hea
✓ 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

Repository agentscore/python-sdk 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 agentscore-py 
Your task is to develop a Python-based desktop application that serves as a personal productivity coach using the 'agentscore-py' package. This app will leverage the capabilities of AgentScore APIs to provide personalized feedback and advice based on user inputs and activities. Here’s a detailed plan for your project:

1. **Project Overview**: Create a desktop application that allows users to input daily tasks and receive feedback on their productivity levels. The app will use the AgentScore APIs to analyze these tasks and provide insights.

2. **Features**:
   - User Registration/Login: Allow users to create accounts and log in securely.
   - Task Input: Users can enter daily tasks they intend to complete.
   - Feedback Generation: Utilize 'agentscore-py' to process the entered tasks and generate personalized feedback on productivity levels.
   - Visualization: Display productivity scores in a visually appealing manner, such as charts or graphs.
   - Historical Data Review: Enable users to review past productivity scores and feedback.

3. **Utilizing 'agentscore-py' Package**:
   - Integrate 'agentscore-py' to send task data to the AgentScore API for analysis.
   - Retrieve and display the feedback provided by the API in a user-friendly format within the app.
   - Implement error handling for any issues that arise during API communication.

4. **Development Steps**:
   - Step 1: Set up a Python environment with necessary libraries, including 'agentscore-py'.
   - Step 2: Design the UI/UX for the application, focusing on ease of use and clarity.
   - Step 3: Develop the backend logic to handle user interactions and API communications.
   - Step 4: Test the application thoroughly to ensure all features work as expected.
   - Step 5: Deploy the application for users to start utilizing it as a productivity tool.

5. **Additional Considerations**:
   - Ensure the application complies with privacy laws and guidelines regarding user data.
   - Plan for scalability to accommodate more users and additional features in the future.