aiecs

v1.11.6 safe
3.0
Low Risk

AI Execute Services - A middleware framework for AI-powered task execution and tool orchestration

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators, with no network calls, shell executions, obfuscations, or credential harvesting attempts. The only notable concern is the metadata risk due to potential lack of maintenance or newness of the package.

  • No network calls detected.
  • No shell execution patterns.
  • No obfuscation patterns.
  • Metadata risk due to possible new or less maintained status.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function.
  • Shell: No shell execution patterns detected, indicating no direct command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The package shows signs of being newly created or infrequently maintained, with an author who may be inactive or new to PyPI.

📦 Package Quality Overall: Medium (5.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://aiecs.readthedocs.io
  • Detailed PyPI description (14254 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 456 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in Howmany-Zeta/AI-Execute-Services
  • Single author but highly active (100 commits)

🔬 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

Email domain looks legitimate: gmail.com>

✓ 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 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 aiecs 
Your task is to create a fully functional mini-application called 'TaskMaster' using the Python package 'aiecs'. TaskMaster will serve as a versatile tool for orchestrating complex workflows involving multiple AI services, making it easy for users to automate tasks that require different types of AI processing steps.

#### Core Features:
1. **User Interface**: Develop a simple web-based UI where users can input their workflow requirements, such as specifying which AI services they want to use, the order in which these services should be executed, and any parameters needed for each service.
2. **Service Orchestration**: Utilize 'aiecs' to manage the execution of these services in a defined sequence. This includes setting up dependencies between services, handling errors, and ensuring that the workflow completes successfully.
3. **Dynamic Workflow Creation**: Allow users to dynamically add, remove, or reorder services within their workflow without needing to restart the application. This flexibility should be handled seamlessly by 'aiecs'.
4. **Execution Monitoring**: Implement real-time monitoring of the workflow execution status through the UI. Users should be able to see the progress of each step and receive alerts if something goes wrong.
5. **Result Presentation**: Once all services have completed their tasks, present the final results back to the user in a clear and understandable format. This could include visualizations, reports, or downloadable files depending on the nature of the workflow.

#### How 'aiecs' is Utilized:
- **Initialization & Configuration**: Use 'aiecs' to initialize your application and configure it according to the user's specifications for the workflow.
- **Task Execution**: Leverage 'aiecs' to execute individual tasks or services as part of the overall workflow. This involves setting up task definitions, defining inputs/outputs, and configuring how these tasks interact with one another.
- **Error Handling & Retry Mechanisms**: Implement robust error handling using 'aiecs', allowing for retries of failed tasks or graceful fallbacks to alternative services when necessary.
- **Logging & Monitoring**: Utilize 'aiecs' logging capabilities to keep track of the workflow execution, including start/end times, success/failure statuses, and any relevant performance metrics.

By following these guidelines and utilizing the powerful features of 'aiecs', you'll be able to create a highly functional and user-friendly application that simplifies the process of managing complex AI-driven workflows.