anemoi-models

v0.15.1 safe
3.0
Low Risk

A package to hold various functions to support training of ML models.

🤖 AI Analysis

Final verdict: SAFE

The package anemoi-models v0.15.1 poses minimal risk based on the analysis. It has no network calls, no obfuscation, and no credential harvesting attempts.

  • Shell=True usage in subprocess.run
  • Lack of network communication
Per-check LLM notes
  • Network: No network calls detected, which is typical and not indicative of malicious activity.
  • Shell: The use of shell=True with subprocess.run is risky if input is not sanitized, but the provided context does not indicate it's being used maliciously; however, it could be exploited.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Medium (6.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://anemoi-models.readthedocs.io/
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (2078 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 381 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 22 unique contributor(s) across 100 commits in ecmwf/anemoi-models
  • 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 score 4.0

Found 2 shell execution pattern(s)

  • n""" run_new_migrations = subprocess.run( [ "git diff --name-only --diff-filter=A
  • capture_output=True, shell=True, ) new_migrations = [root_folder / file for file in
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ecmwf.int>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository ecmwf/anemoi-models appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 anemoi-models 
Create a weather prediction mini-app using the 'anemoi-models' Python package. This app will predict weather conditions based on historical data. Your task is to develop a user-friendly interface where users can input their location and date range to receive a forecast. Here are the key steps and features you should include:

1. **Data Collection**: Utilize 'anemoi-models' to fetch historical weather data from a reliable API or dataset. Ensure the data includes temperature, humidity, wind speed, and precipitation.
2. **Model Training**: Train a machine learning model using the collected data. Use 'anemoi-models' functions to preprocess the data, handle missing values, and split it into training and testing sets. Experiment with different algorithms provided by 'anemoi-models' to find the best fit for your predictions.
3. **Prediction Interface**: Develop a simple command-line interface (CLI) or web-based interface where users can input their location and select a date range. Use 'anemoi-models' to process this information and generate a weather forecast.
4. **Visualization**: Implement basic visualization tools to display the forecasted weather conditions. This could be as simple as graphs showing temperature trends over time or more complex visualizations if resources permit.
5. **Evaluation and Feedback**: Include functionality to evaluate the accuracy of the predictions against actual weather data. Allow users to provide feedback on the accuracy of the forecasts through the interface.

In your implementation, make sure to document how each function from 'anemoi-models' is utilized, explaining its role in the overall workflow. Additionally, ensure the code is well-structured, modular, and easy to extend for future enhancements.