atomr-agents

v0.18.0 safe
2.0
Low Risk

Composable agentic framework on top of atomr actors and atomr-infer.

🤖 AI Analysis

Final verdict: SAFE

The package presents minimal risks based on the analysis. It shows no signs of network activity, shell execution, obfuscation, or credential harvesting. The metadata suggests a potentially new or less active developer, but this alone does not raise significant concerns.

  • No network calls detected
  • No shell execution patterns
  • No obfuscation patterns
  • No credential harvesting patterns
Per-check LLM notes
  • Network: No network calls detected, which is typical for many packages and does not necessarily indicate risk.
  • Shell: No shell execution patterns detected, indicating the package does not attempt to execute commands on the system directly.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package, suggesting it might be new or less active, but no other suspicious flags are present.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/rustakka/atomr-agents/tree/main/docs
  • Detailed PyPI description (19424 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

  • 341 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 92 commits in rustakka/atomr-agents
  • Small but multi-author team (3–4 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

All external links appear legitimate

✓ Git Repository History

Repository rustakka/atomr-agents appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "atomr-agents contributors" 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 atomr-agents 
Create a fully-functional mini-application called 'TaskMaster' using the Python package 'atomr-agents'. TaskMaster is designed to manage daily tasks and reminders for users, leveraging the composability and agentic framework provided by 'atomr-agents'. This application will allow users to add tasks, set reminders, and receive notifications based on their preferences. Here's a detailed breakdown of the steps and features:

1. **Setup Environment**: Ensure your development environment is set up with Python and the 'atomr-agents' package installed.
2. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the application. Users should be able to input commands like 'add', 'remove', 'list', and 'set reminder'.
3. **Task Management**: Implement functionality to add tasks, which can include details such as task name, description, due date, and priority level.
4. **Reminder System**: Utilize 'atomr-agents' to create agents that monitor the user's tasks and send reminders based on the set due dates. These reminders should be configurable via the CLI.
5. **Notifications**: Integrate a notification system within the application to alert users about upcoming deadlines or completed tasks. Notifications could be sent via email, SMS, or through the CLI itself.
6. **Customization**: Allow users to customize their experience by setting up different categories for tasks (e.g., work, personal, study) and adjusting the frequency of reminders.
7. **Persistence**: Use 'atomr-agents' to ensure that task data persists across sessions. This means that even if the application is closed and reopened, all tasks and settings should remain intact.
8. **Testing**: Thoroughly test the application to ensure that it functions correctly under various scenarios, including edge cases like adding tasks without specifying a due date.

By following these steps and utilizing the capabilities of 'atomr-agents', you'll create a robust and user-friendly tool for managing daily tasks and reminders.

💬 Discussion Feed

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