AI Analysis
Final verdict: SUSPICIOUS
The package shows low individual risks in terms of network, shell, obfuscation, and credential handling but raises concerns due to its metadata profile, including being newly published, lacking maintainer details, and not linking to a Git repository.
- Metadata risk is elevated due to the package's newness and lack of maintainer information.
- No linked Git repository suggests potential lack of transparency.
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communication.
- Shell: No shell execution detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of unauthorized access.
- Metadata: The package appears suspicious due to its newness, lack of maintainer information, and no linked Git repository.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
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: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor 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 agentic-json-middleware
Create a command-line tool that leverages the 'agentic-json-middleware' package to validate and correct JSON data obtained from Large Language Models (LLMs). This tool should serve as a robust interface between developers and LLMs, ensuring that the JSON output is both valid and usable. Here’s a detailed guide on how to proceed: 1. **Project Setup**: Start by setting up a new Python virtual environment. Install the necessary packages including 'agentic-json-middleware'. 2. **Design the User Interface**: Develop a simple yet effective command-line interface that allows users to input JSON strings or file paths. 3. **Integration with agentic-json-middleware**: Utilize the 'agentic-json-middleware' package to process the JSON data. Ensure it can automatically validate the JSON against predefined schemas and correct any errors found. 4. **Error Handling**: Implement comprehensive error handling to manage cases where the JSON cannot be corrected or validated. 5. **Output Formatting**: Provide options for users to format the corrected JSON output in various ways, such as pretty-printing or compact formatting. 6. **Testing**: Write tests to ensure your tool works correctly with a variety of JSON inputs and LLM outputs. 7. **Documentation**: Create clear documentation explaining how to use the tool and integrate it into existing workflows. **Suggested Features**: - Support for multiple JSON schemas. - Ability to automatically download and use latest schema updates. - Detailed logs for each validation/correction process. - Option to save corrected JSON directly to a file. - Integration with popular LLM APIs for direct interaction.