agi-app-polars-execution

v2026.6.4 safe
1.0
Low Risk

AGILAB Polars execution benchmark for deterministic worker and reducer validation

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of network activity, shell execution, obfuscation, or credential harvesting, indicating a very low risk level.

  • No network calls detected
  • No shell executions detected
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell executions detected, which is expected unless the package requires executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://thalesgroup.github.io/agilab
  • Detailed PyPI description (2381 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 69 commits in ThalesGroup/agilab
  • Active community — 5 or more distinct contributors

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ThalesGroup/agilab appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Package is very new: uploaded 3 day(s) ago
  • Author "Jean-Pierre Morard" 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 agi-app-polars-execution 
Create a Python-based data processing application named 'PolarsBench' that leverages the 'agi-app-polars-execution' package to perform benchmarking and validation tasks on large datasets. This application will serve as a tool for developers and data scientists to ensure the accuracy and efficiency of their data processing pipelines.

### Features:
- **Data Import**: Users can import CSV files into the application.
- **Benchmark Execution**: The application will use the 'agi-app-polars-execution' package to run benchmarks on the imported dataset, focusing on operations such as filtering, aggregation, and transformation.
- **Deterministic Validation**: After running benchmarks, the application will validate the results using deterministic workers and reducers provided by the 'agi-app-polars-execution' package to ensure the correctness of the processed data.
- **Performance Analysis**: Provide visualizations and statistical summaries of the benchmark results to help users understand the performance characteristics of different operations.
- **Customizable Workflows**: Allow users to define custom workflows for data processing, including specifying which operations to perform and the order in which they should be executed.
- **Report Generation**: Automatically generate reports summarizing the benchmarking and validation processes, including any anomalies detected during the validation phase.

### Utilization of 'agi-app-polars-execution':
- Use the package to execute benchmarks on various Polars operations, ensuring that each operation is tested under different conditions (e.g., varying dataset sizes).
- Implement the package's deterministic validation feature to compare the expected outcomes against actual results, highlighting any discrepancies.
- Integrate the package's capabilities into a user-friendly interface where non-expert users can easily configure and run benchmarks without needing deep knowledge of Polars or the underlying benchmarking mechanisms.