agi-app-pandas-execution

v2026.6.4 suspicious
4.0
Medium Risk

AGILAB Pandas execution benchmark for deterministic worker and reducer validation

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network calls, shell execution, and obfuscation. However, it is newly introduced and maintained by a user with limited history, which raises suspicion about its authenticity and intent.

  • Metadata risk due to limited maintainer history
  • New package with unknown reliability
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 signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is new and maintained by a user with limited history, raising some suspicion but not conclusive evidence of malice.

📦 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 (2667 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-pandas-execution 
Create a data processing utility that leverages the 'agi-app-pandas-execution' package to perform benchmarks on Pandas operations within a distributed computing environment. This utility will serve as a tool for developers and data scientists to validate the performance and determinism of their Pandas workflows across different worker nodes and reducers. The application should include the following features:

1. **User Interface**: A simple command-line interface (CLI) for interacting with the utility.
2. **Configuration Setup**: Allow users to configure the number of worker nodes and the type of reduction operations to be performed.
3. **Benchmark Operations**: Implement common Pandas operations such as filtering, grouping, and aggregation. Users should be able to select which operations to benchmark.
4. **Performance Metrics**: Collect and display performance metrics for each operation, including execution time, memory usage, and any other relevant statistics.
5. **Deterministic Validation**: Ensure that the results of the operations are consistent across different runs and workers, highlighting any discrepancies if they occur.
6. **Report Generation**: Generate a report summarizing the benchmark results, including visualizations if possible.
7. **Integration with 'agi-app-pandas-execution'**: Utilize the 'agi-app-pandas-execution' package to manage the execution of Pandas operations across worker nodes and reducers. This includes setting up the environment, executing the benchmarks, and validating the results.

The goal is to create a robust and user-friendly tool that not only benchmarks but also ensures the reliability and consistency of Pandas workflows in a distributed setting.