AI Analysis
The package shows low risk across all assessed categories, with no indications of malicious activity. The metadata risk is slightly elevated due to the author's single package, but overall, the package appears safe.
- Low network and shell risks
- No signs of obfuscation or credential harvesting
Per-check LLM notes
- Network: Expected minimal network calls for AWS storage interactions.
- Shell: No shell execution is expected for this type of package.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting secure handling of secrets.
- Metadata: The author has only one package, which might indicate a new or less active account, but no other suspicious activities were flagged.
Package Quality Overall: Low (4.2/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Documentation URL: "Documentation" -> https://docs.activeviam.com/products/atoti/python-sdk/0.9.15
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
Classifier: Typing :: Typed
Limited contributor diversity
2 unique contributor(s) across 100 commits in atoti/atotiTwo distinct contributors found
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: activeviam.com>
All external links appear legitimate
Repository atoti/atoti appears legitimate
1 maintainer concern(s) found
Author "ActiveViam" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Develop a fully functional mini-application that integrates data from AWS S3 using the 'atoti-client-storage-aws' package. Your application should allow users to authenticate with their AWS credentials, browse their S3 buckets, select a specific file, and load it into a local pandas DataFrame for further analysis. Additionally, the application should include features such as: 1. User-friendly interface for inputting AWS access keys and secret keys securely. 2. Ability to display a list of available S3 buckets and their contents. 3. Option to filter files by file type (e.g., CSV, Parquet). 4. Support for error handling and informative messages during the process. 5. Capability to save the loaded DataFrame locally in various formats (CSV, Excel, etc.). 6. Optional: Implement basic data visualization using matplotlib or seaborn based on the loaded DataFrame. The 'atoti-client-storage-aws' package will be primarily used to handle the connection to AWS S3 and the downloading of selected files. Ensure your application is well-documented and includes comments explaining the use of 'atoti-client-storage-aws' functions.
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