atoti-core

v0.9.15 safe
3.0
Low Risk

Code required by both client and server Atoti packages

🤖 AI Analysis

Final verdict: SAFE

The package shows very low risks across all categories except for metadata, where the single package from the author suggests potential novelty or lower activity, but this alone is not enough to label it as suspicious.

  • No network calls or shell executions detected.
  • Low risk of obfuscation and credential harvesting.
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 immediate risk of unauthorized command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package, suggesting a new or less active account which may warrant further investigation.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in atoti/atoti
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

Email domain looks legitimate: activeviam.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository atoti/atoti appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ActiveViam" 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 atoti-core 
Create a data analytics mini-app using the 'atoti-core' package, which is designed to handle complex data analysis tasks efficiently. Your app should allow users to upload datasets, perform various aggregations, and visualize the results interactively. Here’s a step-by-step guide on what your app should include:

1. **Data Upload**: Implement a user-friendly interface where users can upload their CSV files. Ensure that the app supports multiple file formats if possible.
2. **Data Exploration**: Once the data is uploaded, provide basic exploratory data analysis (EDA) functionalities such as viewing summary statistics and plotting histograms.
3. **Aggregation Functions**: Allow users to apply various aggregation functions like sum, average, min, max, etc., across different dimensions of the dataset. Users should be able to select dimensions dynamically.
4. **Interactive Visualization**: Integrate visualizations that update in real-time based on the selected aggregation functions and dimensions. Include charts like bar graphs, line plots, and pie charts.
5. **Custom Queries**: Enable advanced users to write custom SQL-like queries to extract specific insights from the data.
6. **Export Results**: Provide an option for users to export their analysis results either as a new CSV file or a PDF report.

### Utilizing 'atoti-core'
- Use 'atoti-core' to manage and manipulate the data in memory efficiently. This includes loading data, performing aggregations, and handling the backend logic for the interactive visualizations.
- Leverage its capabilities to support real-time data processing and quick response times for user interactions.
- Ensure that the app is scalable and can handle large datasets without significant performance degradation.

This project aims to showcase the power of 'atoti-core' in building robust data analytics tools accessible to both technical and non-technical users.

💬 Discussion Feed

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