AI Analysis
The package presents minimal risks with no network calls, shell executions, obfuscations, or credential harvesting attempts. However, its low maintenance and poor metadata quality suggest caution.
- Low maintenance and poor metadata quality
- No detected malicious activities
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communication.
- Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows low maintenance and poor metadata quality, which may indicate low effort or potential risk.
Package Quality Overall: Low (1.2/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
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
No author email provided
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 data analysis tool called 'SnowflakeInsight' that leverages the 'analytics-agent-connector-snowflake' package to connect with Snowflake databases and perform various analytical tasks. This tool will serve as a bridge between developers and analysts who need quick access to data insights without diving into complex SQL queries. Your goal is to build a user-friendly interface where users can select their Snowflake database, choose specific tables, and run predefined or custom queries. Additionally, implement features such as query history, result visualization (charts and graphs), and export capabilities (CSV, Excel). Utilize the 'analytics-agent-connector-snowflake' package to handle the connection and execution of SQL commands against Snowflake databases. Ensure your application supports multi-tenant environments, allowing different teams within an organization to manage their own databases and permissions seamlessly.
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