AI Analysis
Final verdict: SUSPICIOUS
The package shows no immediate signs of malicious activity, but the metadata suggests it may not be well-maintained or widely used, raising concerns about its reliability.
- Low star count
- Single author
- Lack of PyPI classifiers
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The low star count, single package author, and lack of PyPI classifiers suggest the package may be of lower quality or less maintained.
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: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "cb" 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 actionswf
Create a simple workflow management system using the Python package 'actionswf'. This system will allow users to define workflows, assign tasks within these workflows, and track the progress of each task. The application should provide a command-line interface for users to interact with it. ### Core Features: - **Workflow Definition:** Users should be able to define workflows by specifying the sequence of actions/tasks. Each task can have dependencies on other tasks. - **Task Assignment:** Once a workflow is defined, users should be able to assign specific tasks to different users or roles. - **Progress Tracking:** The system should display the current status of each task and the overall workflow completion percentage. - **Notifications:** Implement basic notifications when a task is assigned or completed. Notifications can be simulated through logging or simple print statements for now. ### Utilizing 'actionswf': - Use 'actionswf' to bind and manage the actions within your workflows. This could involve creating action instances, linking them together to form workflows, and managing their execution states. ### Step-by-Step Implementation: 1. **Setup:** Install 'actionswf' and any additional necessary Python packages. Set up a virtual environment if needed. 2. **Define Workflows:** Create a class or set of functions that allows users to define workflows. These definitions should include details about each task, such as name, description, and dependencies. 3. **Task Management:** Implement functionality to assign tasks to users based on the workflow definition. Ensure that dependencies between tasks are respected. 4. **Tracking Progress:** Develop a mechanism to update and display the status of each task. This could be as simple as updating a dictionary or database entry for each task. 5. **Notifications:** Add logging or print statements to simulate notifications when tasks are assigned or completed. 6. **User Interface:** Build a command-line interface that allows users to define workflows, assign tasks, and check the status of workflows. ### Additional Suggestions: - Consider adding error handling for situations where dependencies are not met or tasks fail. - Allow users to edit workflows after they have been defined. - Implement a feature to save and load workflows from files or databases.