AI Analysis
The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks. The metadata risk is slightly elevated due to incomplete maintainer information.
- Low risk scores across all categories.
- Metadata risk slightly elevated due to incomplete maintainer information.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
- Metadata: The maintainer's author information is incomplete and may indicate a less experienced or potentially inactive developer.
Package Quality Overall: Medium (5.8/10)
Test suite present — 8 test file(s) found
8 test file(s) detected (e.g. base_test.py)
Some documentation present
Detailed PyPI description (11628 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
78 type-annotated function signatures detected in source
Active multi-contributor project
3 unique contributor(s) across 100 commits in Auttcast/python-function-compositionSmall but multi-author team (3–4 contributors)
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
Email domain looks legitimate: gmail.com>
All external links appear legitimate
Repository Auttcast/python-function-composition appears legitimate
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 small, interactive command-line utility that allows users to manipulate and analyze a list of integers using the 'auttcomp' Python package. This utility should showcase the power of functional composition and iterable pipelines, allowing for complex operations to be built up from simple steps. Here are the key features your utility should include: 1. **Data Input**: Users should be able to input a list of integers directly through the command line. 2. **Operation Pipeline**: Implement a series of operations such as filtering (e.g., even numbers, prime numbers), mapping (e.g., square each number, add a constant to each number), and reduction (e.g., sum, product). 3. **Interactive Mode**: After inputting the data, the user should enter an interactive mode where they can chain together these operations using natural language commands. For example, the user might type `filter even`, then `map square`, and finally `reduce sum`. 4. **Output**: Display the final result after applying all specified operations. 5. **Help/Documentation**: Provide a help menu that explains all available operations and how to use them. Utilize the 'auttcomp' package to demonstrate how these operations can be chained together seamlessly, showing off its capabilities for functional composition and iterable pipelines. This will not only make your code cleaner but also more powerful and flexible.
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