approck-healthcheck

v1.0.13 suspicious
4.0
Medium Risk

Liveness and readiness HTTP probes with StatsD worker gauges for Gevent and Gunicorn.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to its network activity and low community engagement, without clear indicators of malicious intent.

  • network activity through client_session or requests.Session()
  • low community engagement with only one package from the maintainer
Per-check LLM notes
  • Network: The use of client_session or requests.Session() suggests the package performs network calls, possibly for health checks as indicated by its name.
  • 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 maintainer has only one package and the repository lacks community engagement, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

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

Some documentation present

  • Detailed PyPI description (4074 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ 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 4 commits in adalekin/healthcheck
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • lication["client_session"] or requests.Session() resp = application["client_session"].get(applicat
✓ 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 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Alexey Dalekin" 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 approck-healthcheck 
Create a fully-functional mini-web application using Flask and integrate it with the 'approck-healthcheck' package to ensure robust health monitoring. Your application will serve as a simple blog platform where users can post articles and comments. Here’s a step-by-step guide on how to build this application:

1. **Setup Project Structure**: Begin by setting up your project directory structure which includes directories for templates, static files, and a configuration file.
2. **Install Required Packages**: Install Flask and approck-healthcheck along with any other necessary packages such as Flask-SQLAlchemy for database management.
3. **Database Setup**: Use SQLite as your database to store blog posts and comments. Define models for BlogPost and Comment in SQLAlchemy.
4. **Create Basic Endpoints**: Implement endpoints to add new blog posts, retrieve all posts, and add comments to specific posts.
5. **Integrate Health Checks**: Utilize approck-healthcheck to set up liveness and readiness probes. Configure these checks to monitor the availability of the database and the responsiveness of the Flask server.
6. **Implement StatsD Gauges**: Set up StatsD workers within your application to gauge the performance metrics like response times and request counts. This will help in understanding the load on the system.
7. **Testing and Validation**: Write tests to ensure that your health checks are functioning correctly and that your application behaves as expected under various conditions.
8. **Deployment Considerations**: Discuss how you would deploy this application using Gunicorn and Gevent, leveraging the capabilities of approck-healthcheck for better monitoring and scalability.

Your final deliverable should include a working Flask application, properly configured health checks, and a setup for monitoring system performance. This project aims to demonstrate not only basic web development skills but also advanced practices in ensuring application health and performance.

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