AI Analysis
Final verdict: SUSPICIOUS
The package has minimal risks associated with network, shell, obfuscation, and credential handling. However, the sparse maintainer information and lack of community engagement raise concerns about its origin and reliability.
- Sparse maintainer information
- Lack of community engagement
Per-check LLM notes
- Network: Network calls are expected if the package interacts with external APIs to fetch weather data from AEMET.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: The maintainer information is sparse, and the repository lacks community engagement, raising some suspicion.
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
ctions=5) async with httpx.AsyncClient(limits=_LIMITS) as client: resp = await client.) async with httpx.AsyncClient(limits=_LIMITS) as client: resp = await
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: outlook.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
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 aemetdata
Develop a weather data analysis tool using the Python package 'aemetdata'. This tool will allow users to fetch historical weather data from the Spanish National Weather Agency (AEMET) and perform basic statistical analyses on this data. The tool should be designed as a command-line interface (CLI) application, making it easy for users to interact with the weather data without needing any graphical interface. ### Key Features: 1. **Data Fetching**: Allow users to specify a location and date range to download weather data. Utilize the 'aemetdata' package to interact with the AEMET OpenData API and retrieve daily weather observations. 2. **Data Storage**: Implement functionality to save the fetched data into a local SQLite database for future use and analysis. 3. **Statistical Analysis**: Provide options for calculating mean temperature, precipitation, and other relevant statistics over the specified date range. 4. **Visualization**: Include a feature to visualize the weather data using simple plots. Use libraries such as Matplotlib or Seaborn to generate line charts of temperature trends and bar charts for precipitation. 5. **Report Generation**: Enable users to generate PDF reports summarizing the weather data analysis results. Utilize the FPDF library to create these reports. 6. **User Interface**: Design a CLI menu system that guides users through the process of fetching data, performing analyses, and generating reports. ### How 'aemetdata' is Utilized: - **Data Retrieval**: Use the 'aemetdata' package to authenticate and request weather data from AEMET based on user input. - **Error Handling**: Implement error handling specific to the 'aemetdata' package to manage issues such as invalid requests or server errors. - **Data Processing**: After fetching data, process it to match the required format for storage and analysis, ensuring compatibility with the chosen visualization and reporting tools. ### Additional Considerations: - Ensure the application is well-documented and includes a README file explaining how to install dependencies and run the application. - Incorporate logging to track application operations and potential issues during data fetching and processing.