AI Analysis
Final verdict: SAFE
The package is deemed safe with a low risk score. It shows no signs of malicious activity, such as network calls, shell executions, or obfuscation techniques.
- No network calls detected.
- No shell execution detected.
- Low risk of obfuscation.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
- Shell: No shell execution detected, indicating the package does not execute system commands, which is safe.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating secure handling of secrets and credentials.
- Metadata: Low risk due to lack of suspicious elements, but caution needed as the maintainer has only one package and lacks PyPI classifiers.
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
Detailed PyPI description (1161 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
Email domain looks legitimate: cqm.nl>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "Pepijn Wissing" 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 algomancy-content
Create a Python-based content generation mini-app called 'AlgoBlogger' using the 'algomancy-content' package. This app will allow users to quickly generate sample blog posts for testing purposes. Here’s a step-by-step guide on how to develop this application: 1. **Setup Project Environment**: Initialize a new Python virtual environment and install necessary packages including 'algomancy-content'. 2. **Define Core Features**: The app should include options for generating different types of content such as blog posts, comments, and user profiles. 3. **User Interface**: Develop a simple command-line interface (CLI) where users can input parameters like post length, topic, and number of comments. 4. **Content Generation**: Use 'algomancy-content' to generate placeholder content based on user inputs. Ensure the content is coherent and relevant to the specified topic. 5. **Output Options**: Provide functionality to output the generated content either to the console or to a file. 6. **Additional Features**: - Implement a feature to save generated content into a local SQLite database for future reference. - Add an option to generate content based on pre-defined templates (e.g., technical blogs, travel blogs). 7. **Testing**: Write unit tests to ensure the content generation works as expected under various scenarios. 8. **Documentation**: Create comprehensive documentation detailing how to use the CLI and any additional functionalities included in the app. By following these steps, you'll create a versatile tool for developers and bloggers who need to quickly generate sample content for their projects.