AI Analysis
The package has moderate risks due to potential insecure handling of credentials and signs of low maintenance effort. Further investigation is required.
- credential risk due to retrieval of GITHUB_TOKEN
- low metadata quality indicating possible lack of proper maintenance
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access to function properly.
- Shell: No shell execution detected, which is expected and safe.
- Obfuscation: No obfuscation patterns were detected in the provided code snippet.
- Credentials: The code is attempting to retrieve a GITHUB_TOKEN from environment variables, which could indicate legitimate use for interacting with GitHub APIs but also poses a risk if not handled securely.
- Metadata: The package shows some signs of low maintenance and possibly low effort, but there are no clear indicators of malicious intent.
Package Quality Overall: Low (2.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (3257 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
63 type-annotated function signatures detected in source
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
Found 1 credential access pattern(s)
token:Optional[str] = os.getenv('GITHUB_TOKEN') if not token: raise GitHubError("GITH
No typosquatting candidates detected
No author email provided
All external links appear legitimate
Repository not found (deleted or private)
Repository not found (deleted or private)
2 maintainer concern(s) found
Author "Dan East" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based command-line tool named 'RepoArchiver' which will utilize the 'archive-and-release' package to streamline the process of archiving and releasing code repositories. This tool should be able to handle both public and private GitHub repositories, including those that consist of multiple submodules. Here are the key functionalities that your application should have: 1. **Initialization**: Users should be able to initialize the tool with their GitHub token for authentication purposes. 2. **Repository Selection**: Provide options for users to select one or more repositories (including submodules) to archive. Repositories can be specified by name or URL. 3. **Branch/Tag Specification**: Allow users to specify the branch or tag they want to archive. If no specific branch/tag is provided, the main branch (usually 'main' or 'master') should be used. 4. **Archive Creation**: Use the 'archive-and-release' package to create an archive of the selected repositories and submodules based on the specified branch/tag. 5. **GitHub Release Creation**: After archiving, automatically create a GitHub release using the generated archive. Include the option for users to provide a custom release title and description. 6. **Optional Features**: - Support for dry-run mode where all steps are simulated without actual changes being made. - Integration with GitLab/Microsoft Azure DevOps repositories as well as GitHub. - Ability to exclude certain files or directories from the archive based on user-defined patterns. 7. **Error Handling and Logging**: Ensure that any errors encountered during the process are logged and reported back to the user in a clear manner. 8. **Security Considerations**: Safeguard the user's GitHub token and other sensitive information by storing them securely and not logging them. Your task is to design and implement this tool, ensuring that it makes efficient use of the 'archive-and-release' package's capabilities. Pay special attention to user experience and the robustness of the tool.
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