AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate risks due to network and shell command execution activities, though there is no evidence of obfuscation or credential harvesting. The lack of a GitHub repository and a single package from the maintainer adds some uncertainty.
- Moderate network risk
- High shell risk
- Minimal package metadata
Per-check LLM notes
- Network: Network calls to an external server might be used for legitimate purposes like sending documentation, but could also indicate unauthorized data transmission.
- Shell: Executing shell commands, especially related to git operations, can be common in development tools but may pose risks if not properly controlled, potentially leading to unintended system changes.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package and no linked GitHub repository, which raises some suspicion but not enough to conclusively identify it as malicious.
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 (131375 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
81 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
score 3.0
Found 2 network call pattern(s)
= f.read() result = requests.post( f"{default_server_url}/docs/{os.getenv('REPO_IDtry: response = requests.get(url, headers=headers) response.raise_for_status()
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 6.0
Found 3 shell execution pattern(s)
h): try: result = subprocess.run( ['git', 'diff', target_hash, 'HEAD', ':(excludelude)*.md'] result = subprocess.run(cmd, capture_output=True, text=True, encoding='utf-8')ion_hash() -> str: return subprocess.check_output(['git', 'rev-parse', 'HEAD']).decode('ascii').strip() def c
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com
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 "dima-on" 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 autodocgenerator
Your task is to develop a simple yet powerful documentation generator tool for Python projects using the 'autodocgenerator' package. This tool will help developers quickly generate comprehensive documentation for their Python modules and packages, enhancing code readability and maintainability. Here’s a step-by-step guide on how to approach this project:
1. **Setup Your Environment**: Ensure you have Python installed along with 'autodocgenerator'. You can install 'autodocgenerator' via pip if it's not already available.
2. **Project Structure**: Create a directory named 'docgen_tool' where all your project files will reside. Inside this directory, set up a basic structure including a main script ('generate_docs.py'), configuration files, and a README.md file for instructions.
3. **Core Functionality**: Implement the core functionality of your tool in 'generate_docs.py'. This should include functions to parse input paths for Python files or directories, utilize 'autodocgenerator' to generate documentation from these inputs, and output the generated documentation into a specified directory or format.
4. **User Interface**: Design a simple command-line interface (CLI) for interacting with your tool. Users should be able to specify input paths, output directories, and other relevant parameters through CLI arguments.
5. **Customization Options**: Add customization options to tailor the generated documentation. For example, users might want to choose between different output formats (HTML, Markdown, etc.), include/exclude certain sections, or customize styling.
6. **Testing and Validation**: Write tests to ensure your tool works as expected across various scenarios. Validate the generated documentation against known good outputs to catch any issues early.
7. **Documentation and Instructions**: Provide thorough documentation within the README.md file explaining how to install your tool, its usage, and any customization options available.
8. **Final Touches**: Polish your tool by adding error handling, improving user feedback during execution, and ensuring it adheres to best coding practices.
By following these steps, you'll create a valuable tool that streamlines the process of generating documentation for Python projects, making it easier for developers to maintain high-quality, well-documented codebases.
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