ast-canopy

v0.10.0 safe
3.0
Low Risk

(No description)

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories except for metadata quality, where it has a moderate score due to low maintainer activity and poor metadata. However, there are no clear signs of malicious intent.

  • Low network and shell execution risks
  • No obfuscation or credential harvesting detected
  • Moderate metadata risk due to low maintainer activity and poor metadata quality
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 signs of malicious activity.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to secrets or credentials.
  • Metadata: The package shows low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Brief PyPI description (437 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ast-canopy 
Create a Python-based utility called 'CodeAnalyzer' that leverages the 'ast-canopy' package to analyze CUDA C++ source code files. This utility will parse CUDA C++ headers and extract information about top-level declarations, such as functions, classes, and variables, which it will then serialize into a structured format like JSON or YAML. Here are the key steps and features of the project:

1. **Setup**: Begin by installing the necessary packages including 'ast-canopy'. Ensure you have a working environment set up with Python and CUDA support.

2. **Parsing Functionality**: Implement a function within 'CodeAnalyzer' that accepts a path to a CUDA C++ header file as input. Utilize 'ast-canopy' to parse the file and extract all top-level declarations.

3. **Serialization**: Develop a feature that serializes the extracted declarations into a structured format. Choose between JSON and YAML based on which one is more readable and easier to work with for further processing or display.

4. **Output Display**: Design a method to output the serialized data in a user-friendly way. This could be a formatted string printed to the console or saved to a file.

5. **Error Handling**: Incorporate robust error handling to manage cases where the input file does not exist, is not a valid CUDA C++ header, or if there are issues during parsing or serialization.

6. **Command Line Interface (CLI)**: Optionally, create a CLI interface for 'CodeAnalyzer' that allows users to specify the input file and output format directly from the command line.

7. **Testing**: Write tests to ensure that 'CodeAnalyzer' correctly parses different types of CUDA C++ headers and handles various edge cases, such as missing files or incorrect formats.

By completing this project, you'll gain experience with 'ast-canopy', understand how to work with CUDA C++ headers using Python, and develop a practical tool for analyzing CUDA code.

💬 Discussion Feed

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