AI Analysis
Final verdict: SUSPICIOUS
The package has no detected malicious activities such as network calls, shell executions, or obfuscation. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of community engagement, which raises suspicion about its legitimacy.
- Metadata risk score of 4 out of 10
- Maintainer has a new or inactive account
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell executions detected, indicating the package does not perform unexpected system-level operations.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has a new or inactive account and the repository lacks community engagement, raising some concerns.
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: zopyr.us
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 2.0
1 maintainer concern(s) found
Author "Yufei Pan" 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 TSVZ
Your task is to develop a command-line utility called 'TSVQuery' using Python that leverages the TSVZ package to manage and query tab-separated values (TSV) files as if they were databases. This utility should allow users to perform basic CRUD (Create, Read, Update, Delete) operations on TSV data stored in memory. Here’s a breakdown of the steps and features you need to implement: 1. **Setup**: Begin by installing the TSVZ package and setting up your Python environment. 2. **Loading Data**: Create a feature that allows users to load TSV files into memory using TSVZ. Ensure that the structure of the TSV file is parsed correctly and stored as a database-like object. 3. **Querying Data**: Implement a query system that allows users to search through the loaded TSV data based on specific columns and values. Users should be able to filter rows based on criteria provided at runtime. 4. **Adding New Records**: Develop functionality that enables users to add new records to the TSV dataset stored in memory. Ensure that this operation is done efficiently and that the new record is properly formatted before being added. 5. **Updating Existing Records**: Allow users to update existing records in the TSV data set. This should include specifying which row(s) to update and what changes to make. 6. **Deleting Records**: Provide the ability to delete records from the in-memory TSV dataset. This could involve specifying a condition for deletion or selecting specific rows. 7. **Saving Changes**: After performing any operations, the utility should have the capability to save the modified TSV data back to a file or overwrite the original file. 8. **User Interface**: Design a simple but intuitive command-line interface for interacting with the utility. Commands should be clearly defined and easy to understand. 9. **Error Handling**: Implement robust error handling to manage issues such as incorrect input formats, non-existent files, and invalid queries. 10. **Documentation**: Write comprehensive documentation detailing how to install the utility, use its commands, and troubleshoot common issues. Throughout development, focus on leveraging TSVZ’s capabilities to handle the TSV data efficiently and effectively. Emphasize performance and user-friendliness in your design.