astartool

v0.2 safe
3.0
Low Risk

toolkit for python

🤖 AI Analysis

Final verdict: SAFE

The package appears to be primarily focused on providing basic data structures and error handling without any malicious intent evident from the provided details.

  • No network calls detected
  • Use of shell=True is limited to benign purposes
Per-check LLM notes
  • Network: No network calls detected, which is normal and poses no immediate risk.
  • Shell: The use of shell=True can be risky if not sanitized properly, but here it seems to be used to get the timestamp of the latest git commit, which is generally benign.
  • Obfuscation: The base64 decoding is likely used for legitimate purposes such as handling encoded data or images, but it could still be a concern if the decoded content is not properly handled.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1431 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 23 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • base64_str) byte_data = base64.b64decode(base64_data) image_data = BytesIO(byte_data) img =
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • th(__file__))) git_log = subprocess.Popen( 'git log --pretty=format:%ct --quiet -1 HEAD',
  • err=subprocess.PIPE, shell=True, cwd=repo_dir, universal_newlines=True, ) timesta
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: snowland.ltd

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "A.Star" 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 astartool 
Create a Python-based mini-app called 'CodeAnalyzer' that leverages the 'astartool' package to analyze Python source code files and provide insights into their structure and potential issues. The app should be designed to help developers quickly understand the complexity of their codebase and identify areas for improvement. Here’s a detailed breakdown of the project requirements:

1. **Project Setup**: Start by installing the 'astartool' package. Ensure your environment is set up correctly for Python development.
2. **Core Functionality**:
   - **Parse Code Files**: Use 'astartool' to parse Python source code files and extract key information such as function definitions, class structures, and variable usage.
   - **Analyze Complexity**: Implement a feature to calculate the cyclomatic complexity of functions and methods within the parsed code. This will help in identifying overly complex parts of the code that might require refactoring.
3. **Additional Features**:
   - **Dependency Analysis**: Identify dependencies between different modules or classes in the codebase.
   - **Code Quality Metrics**: Calculate additional metrics like lines of code, comment density, and adherence to naming conventions.
4. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the app. Users should be able to specify the path to a directory containing Python files and receive a report on the analysis results.
5. **Reporting**: Generate a comprehensive report that summarizes the findings from the analysis. This report should be easy to read and highlight critical issues first.
6. **Testing**: Write tests using unittest or pytest to ensure the functionality of your app works as expected under various scenarios.
7. **Documentation**: Provide clear documentation on how to install, configure, and use the 'CodeAnalyzer' app. Include examples and best practices for using the tool effectively.

The 'astartool' package is crucial for parsing and analyzing the Python AST (Abstract Syntax Tree), which forms the basis of all further analysis in this project. Your goal is to create a tool that not only helps developers maintain high-quality code but also encourages them to adopt better coding practices.

💬 Discussion Feed

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