AI Analysis
The package shows minimal risks across all checked categories with no network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata risk slightly increases due to the maintainer having only one package, but overall it does not suggest a supply-chain attack.
- No network calls
- Single package from maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account.
Package Quality Overall: Low (3.4/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
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
Create a real-time data analytics dashboard using the 'atoti-server' Python package. This dashboard will allow users to upload CSV files containing sales data, perform real-time analysis on these datasets, and visualize key metrics such as total sales, average sales per region, and top-selling products. Step 1: Set up a Flask web server to handle file uploads and API requests. Integrate 'atoti-server' to manage the Atoti sessions which will process the uploaded data. Step 2: Implement a user interface where users can select a CSV file from their local machine and upload it. Upon successful upload, the data should be automatically loaded into an Atoti session for analysis. Step 3: Develop real-time data processing capabilities within the Atoti sessions. This includes calculating total sales, average sales per region, and identifying the top-selling products based on quantity sold. Step 4: Create visualizations of the processed data using libraries like Plotly or Matplotlib. Display these visualizations on the dashboard to provide users with insights into their sales data. Suggested Features: - Real-time updates as data is being processed. - Ability to filter and drill down into specific regions or product categories. - Export options for the visualized data. - User authentication to secure data access. How 'atoti-server' is Utilized: - Use 'atoti-server' to start and manage multiple Atoti sessions that process different datasets concurrently. - Leverage the 'atoti-server' APIs to integrate data loading, processing, and analysis functionalities seamlessly into your Flask application. - Ensure that each Atoti session is properly initialized, configured, and shut down as needed to maintain optimal performance.
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