🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to the use of pickling for obfuscation and the sparse metadata, raising concerns about potential hidden functionality or malicious intent.

High obfuscation risk due to the use of pickling.
Sparse and potentially suspicious author metadata.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary commands.
Obfuscation: The code uses pickling to encode and decode functions which could be used for obfuscation purposes, potentially hiding the actual functionality of the code.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The author's information is sparse and the account seems new or inactive, which raises some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

f = compose(str, sum) g = pickle.loads(pickle.dumps(f)) assert f((1, 2)) == g((1, 2)) def test
f = curry(map)(str) g = pickle.loads(pickle.dumps(f)) assert list(f((1, 2, 3))) == list(g((1,
juxt(str, int, bool) g = pickle.loads(pickle.dumps(f)) assert f(1) == g(1) assert f.funcs
sert f(False) is True g = pickle.loads(pickle.dumps(f)) assert f(True) == g(True) assert f(
p.__get__(1) is True p2 = pickle.loads(pickle.dumps(p)) assert p2.__get__(None) is None ass
def test_flip(): flip = pickle.loads(pickle.dumps(humpy_cytoolz.functoolz.flip)) assert flip

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: pm.me>

Very short email domain: pm.me>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository hunterhogan/Z0Z_tools appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 Z0Z-tools

Create a Python-based utility called 'Streamline' that leverages the 'Z0Z-tools' package to enhance data processing workflows. Streamline should offer users a seamless way to manipulate, analyze, and visualize datasets efficiently. The utility should support the following key functionalities:

1. **Data Transformation**: Allow users to perform complex data transformations using functions from 'Z0Z-tools'. This includes operations like mapping, filtering, and reducing data.
2. **Documentation Generation**: Automatically generate comprehensive documentation for any custom functions or pipelines created within Streamline, ensuring that the code remains understandable and maintainable over time.
3. **Performance Optimization**: Use 'Z0Z-tools' to optimize the performance of data processing tasks, making use of parallel processing capabilities where applicable.
4. **Visualization Tools**: Integrate basic visualization tools that allow users to plot their processed data in various formats (e.g., line charts, bar graphs).
5. **User Interface**: Develop a simple command-line interface (CLI) that guides users through each step of the data processing workflow.

The application should demonstrate proficiency in utilizing 'Z0Z-tools' for data manipulation and optimization, while also providing clear, well-documented outputs. Users should be able to input raw data, specify transformations, and receive both processed data and visual representations as output.