aifred-tk

v0.10.3 suspicious
4.0
Medium Risk

AI agent toolkit with CLI and MCP presentation layers

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal direct risks but has a metadata risk due to the maintainer's limited presence and lack of a GitHub repository, which raises some suspicion.

  • Low direct risk indicators such as network, shell, obfuscation, and credential risks.
  • Metadata risk due to the maintainer having only one package and no associated GitHub repository.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
  • Shell: No shell executions detected, indicating no immediate risk of unauthorized system command execution.
  • 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 no associated GitHub repository, which may indicate a less experienced or potentially suspicious actor.

📦 Package Quality Overall: Medium (5.2/10)

✦ 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. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://aifred-toolkit.readthedocs.io/
  • Brief PyPI description (445 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

  • 161 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Jesús Alonso Abad" 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 aifred-tk 
Create a mini-application named 'AI Task Manager' using the 'aifred-tk' Python package. This application will serve as a tool for managing daily tasks with the assistance of AI-driven task prioritization and classification. The app should allow users to add, edit, delete, and view their tasks. Additionally, it should use AI to categorize tasks into different types (e.g., work, personal, urgent, non-urgent) and suggest the optimal order in which to tackle them based on urgency and importance.

Steps to develop the application:
1. Set up the development environment with Python and install the 'aifred-tk' package.
2. Design the user interface using the CLI provided by 'aifred-tk'. Ensure that the UI is user-friendly and intuitive.
3. Implement basic CRUD (Create, Read, Update, Delete) operations for managing tasks.
4. Integrate the AI capabilities of 'aifred-tk' to classify tasks into categories and prioritize them according to urgency and importance.
5. Add features like setting deadlines for tasks and tracking completion status.
6. Test the application thoroughly to ensure all functionalities work as expected.
7. Deploy the application so that others can use it.

Suggested features:
- Ability to set reminders for upcoming tasks.
- Option to export task lists in CSV format.
- Support for adding notes or comments to each task.
- User authentication to secure personal task data.
- Integration with calendar apps for scheduling tasks.

How 'aifred-tk' is utilized:
- Use the CLI layer of 'aifred-tk' to create a seamless command-line interface.
- Leverage the MCP layer for machine learning components to classify and prioritize tasks.
- Employ 'aifred-tk' for handling user interactions and processing task-related data efficiently.

💬 Discussion Feed

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