🤖 AI Analysis

Final verdict: SAFE

The package poses minimal risk with no network or credential risks detected. While there are subprocess calls and some concerns about maintainer activity, these do not strongly indicate malicious intent.

Low network and obfuscation risks
Subprocess calls present but benign
Metadata suggests low maintainer activity

Per-check LLM notes

Network: No network calls detected, which is low risk.
Shell: Subprocess calls to node.js for linting purposes appear benign but could be misused.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting no immediate risk to stored secrets.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but there are no clear indicators of malicious intent.

📦 Package Quality Overall: Low (3.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

2 test file(s) detected (e.g. test_codegen.py)

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

40 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 score 6.0

Found 3 shell execution pattern(s)

rettier(): return subprocess.run( [ str(self.node_path),
self.rules)] eslint = subprocess.run( [ str(self.node_path),
ext", ext)] eslint = subprocess.run( [ str(self.node_path),

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: alliancesoftware.com.au>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with alliance-platform-codegen

Create a mini-application named 'CodeGenCraft' using the Python package 'alliance-platform-codegen'. This tool aims to simplify the process of generating boilerplate code for different platforms and frameworks, such as web services, mobile apps, or cloud functions. Here’s a detailed breakdown of what your application should achieve:

1. **User Interface**: Design a simple and intuitive user interface where users can select the type of platform they want to generate code for (e.g., Flask API, Django Web App, React Native Mobile App).
2. **Configuration Setup**: Allow users to configure their desired settings and parameters for the selected platform. For example, if generating a Flask API, users might specify the base URL, database connection details, etc.
3. **Code Generation**: Utilize the 'alliance-platform-codegen' package to automatically generate the necessary boilerplate code based on the user’s selections and configurations. Ensure that the generated code is well-structured and includes comments for better understanding.
4. **Output Display**: Present the generated code to the user in a readable format within the application itself. Users should have the option to download the code as a ZIP file containing all necessary files and directories.
5. **Documentation**: Automatically generate documentation for the generated code, including API endpoints, database schemas, and any other relevant information. This documentation should be displayed alongside the code and also downloadable as a PDF or Markdown file.
6. **Error Handling and Feedback**: Implement robust error handling to guide users through potential issues during the configuration or generation process. Provide clear feedback messages to help users troubleshoot common problems.
7. **Integration Testing**: Include a feature that allows users to perform basic integration tests on the generated code snippets (if applicable). For instance, if a Flask API was generated, provide a button to start a local server and test the API endpoints.
8. **Version Control Integration**: Enable users to directly commit the generated code to a Git repository (GitHub, GitLab, etc.) from within the application, streamlining the development workflow.

Your task is to design and implement these features, ensuring that the 'alliance-platform-codegen' package is integrated seamlessly into each step of the process. Focus on creating a user-friendly experience while maximizing the utility and flexibility of the generated code.