AI Analysis
The package is deemed safe based on the analysis notes which show no signs of obfuscation or credential harvesting. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone does not suggest a supply-chain attack.
- No obfuscation or credential harvesting detected
- Maintainer has only one package
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Low (2.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (6217 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
9 type-annotated function signatures (partial)
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
1 maintainer concern(s) found
Author "Dev Aggarwal" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based weather forecasting mini-app that integrates real-time data from multiple APIs, including OpenAI and Google Cloud Platform (GCP). The app will fetch weather predictions and display them in a user-friendly format. Additionally, it will include features such as retry mechanisms for failed API calls using the 'aifail' library, error handling, and logging of all API interactions for debugging purposes. ### Step-by-Step Guide: 1. **Setup Project Environment**: Initialize a new Python project and install necessary packages, including 'aifail', 'requests', and 'logging'. 2. **API Integration**: Integrate with at least two different weather prediction APIs (one from OpenAI and one from GCP). 3. **Data Fetching Functionality**: Implement functions to fetch weather data from these APIs. Use the '@retry_if' decorator from 'aifail' to handle potential failures during API calls. 4. **Error Handling & Logging**: Incorporate robust error handling and logging mechanisms to ensure all API errors are captured and logged properly. 5. **User Interface**: Develop a simple command-line interface (CLI) for users to interact with the app, allowing them to input locations and view weather predictions. 6. **Display Predictions**: Format and display fetched weather predictions in a readable manner within the CLI. ### Suggested Features: - Retry mechanism for failed API calls using 'aifail' - User-friendly CLI for easy interaction - Logging of all API calls for troubleshooting - Ability to fetch weather predictions for multiple locations - Display current temperature alongside predicted temperatures ### Utilizing 'aifail': - Wrap API fetching functions with '@retry_if' to automatically retry failed calls based on specified conditions, enhancing reliability. - Ensure that all API call functions are decorated with '@retry_if' to take advantage of 'aifail's retry capabilities.