awa-pg

v0.5.7 suspicious
4.0
Medium Risk

Postgres-native background job queue — Python SDK with async/sync workers, transactional enqueue, progress tracking, and web UI

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low direct risks such as network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk due to a suspicious non-HTTPS link and a new maintainer account raises some concern, warranting further investigation.

  • Suspicious non-HTTPS link in metadata
  • New maintainer account
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Suspicious non-HTTPS link and new maintainer account suggest potential risk, but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

○ 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://github.com/hardbyte/awa#readme
  • Detailed PyPI description (14971 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
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in hardbyte/awa
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:3000
✓ Git Repository History

Repository hardbyte/awa appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Brian Thorne" 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 awa-pg 
Create a fully-functional mini-application called 'TaskMaster' using the 'awa-pg' Python package. TaskMaster will serve as a simple yet powerful task management tool, allowing users to submit tasks that are then processed in the background. The application should leverage the core features of 'awa-pg', such as async/sync workers, transactional enqueue, progress tracking, and a web UI for user interaction.

### Step-by-Step Instructions:
1. **Setup Project Structure**: Initialize a new Python virtual environment and install 'awa-pg'. Also, set up a PostgreSQL database for the application.
2. **Define Task Models**: Create models for different types of tasks that users might submit, such as data processing tasks, file conversion tasks, etc. Each task model should include fields for task status, progress, start/end timestamps, and any other relevant metadata.
3. **Implement Task Submission Interface**: Develop a simple web interface where users can submit their tasks. This interface should allow users to select the type of task they want to perform and provide necessary inputs.
4. **Enqueue Tasks**: Use 'awa-pg' to enqueue submitted tasks into the background job queue. Ensure that the enqueuing process is transactional so that if any error occurs during submission, the task is not lost.
5. **Worker Implementation**: Implement both synchronous and asynchronous worker functions that will process the enqueued tasks. These workers should handle various types of tasks defined earlier, updating the task's progress and status as they work on them.
6. **Progress Tracking**: Integrate 'awa-pg' progress tracking capabilities to monitor the progress of each task in real-time. Display this information back to the user via the web interface.
7. **Web UI Development**: Build a user-friendly web UI using Flask or Django that allows users to view their submitted tasks, track their progress, and receive notifications once the tasks are completed.
8. **Testing and Deployment**: Thoroughly test the application to ensure all components work as expected. Then, deploy the application to a cloud service provider like AWS or Heroku for public access.

### Suggested Features:
- **Task Prioritization**: Allow users to specify priority levels for their tasks.
- **Error Handling and Retries**: Implement robust error handling and automatic retries for failed tasks.
- **Notifications**: Send email/SMS notifications to users when their tasks are completed.
- **Task Cancellation**: Provide functionality to cancel running tasks if needed.
- **Detailed Logs**: Maintain detailed logs of task execution for debugging purposes.

By following these steps and implementing the suggested features, you'll create a comprehensive task management application that demonstrates the power and flexibility of the 'awa-pg' package.

💬 Discussion Feed

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