ai-decision-council

v1.4.2 safe
3.0
Low Risk

Multi-model council orchestration with CLI and integration bridge

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all assessed categories, with a notable use of HTTP requests that are generally benign. The lack of shell execution and minimal metadata risk further supports its safety.

  • Low network risk
  • No shell risk
  • Minimal metadata risk
Per-check LLM notes
  • Network: The network call patterns indicate the package uses HTTP requests for its functionality, which is common but should be reviewed for destination URLs and request payloads to ensure no unexpected behavior.
  • Shell: No shell execution patterns were detected, indicating low risk for direct system command execution.
  • Metadata: The author has only one package, which might indicate a new or less active account, but no other suspicious activities are detected.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://github.com/ch1kim0n1/ai-decision-council/tree/main/d
  • 3 documentation file(s) (e.g. __init__.py)
  • Detailed PyPI description (2148 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

  • Classifier: Typing :: Typed
  • 169 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 18 commits in ch1kim0n1/ai-decision-council
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • Envelope: async with httpx.AsyncClient(timeout=self.timeout) as client: response = awa
  • amEvent]: async with httpx.AsyncClient(timeout=self.timeout) as client: async with cli
  • : async with httpx.AsyncClient(timeout=timeout) as client: response =
✓ 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 ch1kim0n1/ai-decision-council appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "LLM Council 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 ai-decision-council 
Your task is to develop a mini-application named 'DecisionMaker' using the Python package 'ai-decision-council'. This application will serve as a decision-making tool that leverages multiple AI models to provide comprehensive advice on a given topic or question. Here are the steps and features you need to implement:

1. **Setup**: Install the necessary dependencies including 'ai-decision-council' and any other required libraries. Ensure you have access to at least three different AI models (e.g., one for sentiment analysis, another for summarization, and a third for generating creative solutions).
2. **CLI Interface**: Create a command-line interface where users can input their query or problem they want advice on.
3. **Model Integration**: Use 'ai-decision-council' to integrate these models into a council-like structure where each model provides its unique perspective on the user's query.
4. **Data Processing**: Implement data processing logic to clean and prepare the user's input for each model. This might include natural language processing tasks like tokenization or part-of-speech tagging.
5. **Aggregation of Results**: Once all models have processed the input, aggregate their outputs to form a cohesive response. Consider using voting mechanisms or weighted averages based on the reliability of each model.
6. **Output Presentation**: Display the final decision or advice in a structured format that highlights the contributions from each model. Include a summary statement that synthesizes the collective wisdom of the council.
7. **User Feedback Loop**: Optionally, implement a feature where users can rate the quality of advice received, which could be used to improve future responses.

Suggested Features:
- Support for adding new models to the council dynamically.
- Customizable weighting schemes for model outputs.
- Integration with external APIs for real-time data retrieval.
- Exporting results in various formats such as CSV, JSON, or PDF.

Use 'ai-decision-council' to streamline the process of integrating and managing multiple AI models within your application. This package offers tools for setting up a council of models, handling their inputs and outputs, and orchestrating their interactions to achieve complex decision-making tasks.