AI Analysis
Final verdict: SAFE
The package shows minimal risks across all assessed categories, with only minor concerns about metadata completeness.
- Missing author name in metadata
- Single package associated with the maintainer
Per-check LLM notes
- Network: The use of aiohttp for making HTTP requests is common and expected in many legitimate Python packages for networking purposes.
- Shell: No shell execution patterns were detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package has a missing author name and a single package associated with the maintainer, which may indicate a less experienced or potentially suspicious account.
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-training.readthedocs.io/1 documentation file(s) (e.g. conf.py)Detailed PyPI description (2355 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 project424 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
25 unique contributor(s) across 100 commits in ecmwf/anemoi-trainingActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
total=timeout) async with aiohttp.ClientSession(timeout=timeout_config) as session, session.get(url) as resp
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: 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-training appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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-training
Create a mini-application named 'WeatherPredictor' using the Python package 'anemoi-training'. This application aims to predict weather conditions based on historical data. It should include the following steps and features: 1. Data Collection: Fetch historical weather data from an API or a dataset. Ensure the data includes temperature, humidity, wind speed, and other relevant parameters. 2. Data Preprocessing: Use 'anemoi-training' to preprocess the collected data. This includes cleaning the data, handling missing values, and scaling the features if necessary. 3. Model Training: Utilize 'anemoi-training' to train a machine learning model on the preprocessed data. Consider experimenting with different types of models such as linear regression, decision trees, or neural networks to find the best fit for the data. 4. Model Evaluation: Evaluate the trained model using appropriate metrics like accuracy, precision, recall, F1-score, etc., provided by 'anemoi-training'. Visualize the performance using plots and graphs. 5. Prediction: Implement a feature where users can input current weather conditions, and the app predicts future weather conditions using the trained model. 6. User Interface: Develop a simple web interface using Flask or Django where users can interact with the application, upload datasets, and view predictions. 7. Documentation: Write comprehensive documentation explaining how to install and use 'WeatherPredictor', including setup instructions and examples. The goal is to create an engaging and educational application that showcases the capabilities of 'anemoi-training' while providing practical value in predicting weather conditions.