agi-app-weather-forecast

v2026.6.4 suspicious
5.0
Medium Risk

AGILAB weather-forecast notebook-migration demo with forecast analysis artifacts

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in direct execution and network behaviors, but its recent creation and lack of previous projects from the same maintainer raise concerns about potential supply-chain attacks.

  • New package with limited history
  • Single project maintainer with no prior record
Per-check LLM notes
  • Network: No network calls detected, which is normal for a weather forecast package that might not require external API calls.
  • Shell: No shell execution detected, indicating no risk 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 with limited history and no other packages from the maintainer, which raises some suspicion but does not conclusively indicate 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 (2334 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-weather-forecast 
Create a fully-functional mini-application using the Python package 'agi-app-weather-forecast'. This application will serve as a weather forecasting tool for users, allowing them to input a location and receive detailed weather forecasts including temperature, humidity, wind speed, and precipitation chances. Additionally, the app should provide graphical representations of the forecast data using matplotlib or seaborn libraries for better visualization. Here are the steps and features to include:

1. **Setup Environment**: Ensure you have Python installed along with the 'agi-app-weather-forecast' package. If not already installed, use pip to install it.
2. **User Interface**: Develop a simple command-line interface (CLI) where users can enter their city name or zip code to fetch weather information.
3. **Data Fetching**: Utilize the 'agi-app-weather-forecast' package to retrieve weather data for the specified location. The package should handle the fetching and preprocessing of the data.
4. **Forecast Analysis**: Implement basic statistical analysis on the retrieved weather data, such as calculating average temperatures over the forecast period and identifying trends in weather conditions.
5. **Graphical Representation**: Use matplotlib or seaborn to create graphs showing temperature trends, humidity levels, and other key metrics over time.
6. **Output Display**: Display the analyzed forecast data alongside the graphical representations back to the user through the CLI.
7. **Enhancements**: Consider adding features like saving the forecast data to a CSV file, allowing users to specify a date range for the forecast, and integrating with an external API for more real-time data if needed.

The goal is to create a user-friendly, efficient, and informative weather forecasting application that leverages the capabilities of the 'agi-app-weather-forecast' package.