AI Analysis
The package has low risk scores across all categories except metadata, where it shows signs of low maintenance. However, this alone does not indicate malicious intent.
- Low network risk
- No shell or obfuscation risks
- No credential risks
- Potential low maintenance
Per-check LLM notes
- Network: The presence of HTTP/HTTPS client initialization suggests the package might be designed to perform network requests, which is not inherently suspicious but should be reviewed for legitimacy.
- Shell: No shell execution patterns were detected, indicating no immediate risk related to command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintenance and effort, which could indicate potential risks but does not conclusively point to malicious intent.
Package Quality Overall: Low (3.8/10)
Partial test coverage signals detected
Test runner config found: pyproject.toml
Some documentation present
Detailed PyPI description (9429 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
81 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Found 2 network call pattern(s)
rip("/") self._http = httpx.Client(transport=transport, timeout=timeout) def _send(self, rrip("/") self._http = httpx.AsyncClient(transport=transport, timeout=timeout) async def _send(s
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: anyformat.ai>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a command-line utility called 'FormatMaster' using Python and the 'anyformat' package. This utility will allow users to convert various types of data into different formats such as CSV, JSON, XML, and more. The main goal is to streamline the process of converting structured data from one format to another, making it easier for developers and data analysts to work with diverse data sources. ### Features: 1. **User Input:** Users should be able to input their data either directly through the command line or by specifying a file path. 2. **Output Format Selection:** Provide options for users to choose which format they want their data converted to (CSV, JSON, XML, etc.). 3. **Data Preview:** Before conversion, display a preview of the data in its original format for verification purposes. 4. **Conversion Process:** Use the 'anyformat' package to handle the conversion process efficiently. Ensure that the utility supports all formats supported by the 'anyformat' package. 5. **Error Handling:** Implement robust error handling to manage issues like invalid input, unsupported formats, and file read/write errors. 6. **Logging:** Include logging functionality to record each conversion operation along with timestamps and status (success/failure). 7. **Help Documentation:** Provide comprehensive help documentation within the utility itself, explaining how to use each feature and common troubleshooting tips. ### Utilization of 'anyformat': - The 'anyformat' package will be primarily used to perform the actual conversion of data between formats. It should handle the complexities of parsing and formatting data according to the specified output format, ensuring accuracy and efficiency. - Leverage the 'anyformat' package's capabilities to support a wide range of input and output formats, enhancing the utility's versatility. - Use the package's advanced features, if available, to optimize performance and reduce processing time for large datasets.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue