AI Analysis
The package shows minimal direct risks but has a notable metadata risk due to low community engagement and limited maintainer history, suggesting potential issues.
- Low direct risk indicators
- Significant metadata risk due to maintainer and community activity
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external API interactions.
- 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 repository's lack of community engagement and the maintainer's limited history suggest potential risk.
Package Quality Overall: Medium (6.2/10)
Test suite present — 7 test file(s) found
Test runner config found: pyproject.toml7 test file(s) detected (e.g. test_data_loader.py)
Some documentation present
Documentation URL: "Documentation" -> https://github.com/Apilize/apilize-protocol/tree/main/sdk/pyDetailed PyPI description (5507 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
65 type-annotated function signatures detected in source
Limited contributor diversity
2 unique contributor(s) across 18 commits in Apilize/apilize-protocolTwo distinct contributors found
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
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Apilize" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a command-line tool named 'FinanceModeler' using the Python package 'apilize-protocol'. This tool should allow users to create, modify, and validate financial models according to the Apilize Protocol v1. Users will interact with the tool through a simple CLI interface, where they can input various financial data points such as interest rates, stock prices, and economic indicators. The tool should then use the 'apilize-protocol' package to process these inputs, generate corresponding financial models, and output them back to the user in a standardized format that adheres to the Apilize Protocol. Key Features: 1. User Input: Allow users to input financial data via the CLI, including parameters like interest rates, inflation rates, GDP growth forecasts, etc. 2. Model Generation: Use the 'apilize-protocol' package to automatically generate financial models based on the user-provided data. 3. Validation: Validate the generated models against predefined rules within the Apilize Protocol to ensure accuracy and compliance. 4. Output: Display the final financial model in a human-readable format and also provide an option to export it to a file in a structured format (e.g., JSON). 5. Help Documentation: Include comprehensive help documentation accessible via the CLI to guide users on how to use each feature of the tool. How to Utilize 'apilize-protocol': - Import necessary modules from the 'apilize_protocol' package to handle financial model creation and validation tasks. - Use the package's functions to parse user inputs into the correct data structures required by the Apilize Protocol. - Apply the protocol's validation methods to ensure the models adhere to specified standards before presenting them to the user. - Leverage the package's capabilities to enhance the functionality and reliability of the 'FinanceModeler' tool.
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