AI Analysis
Final verdict: SAFE
The package action-rules v1.1.0 presents minimal risks based on the analysis. It lacks network calls, shell executions, and any signs of obfuscation or credential harvesting, indicating a low threat profile.
- No network calls detected
- No shell execution patterns detected
- Maintainer has only one package
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external API interactions.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which might indicate a new or less active account, but there are no other suspicious flags.
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: vse.cz
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository lukassykora/action-rules appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Lukas Sykora" 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 action-rules
Create a Python-based mini-app called 'ActionPredictor' that utilizes the 'action-rules' package to analyze customer behavior data from an e-commerce platform. This app aims to predict future actions of customers based on their past purchasing history and browsing behavior. The goal is to enhance customer experience and increase sales through personalized recommendations. Step 1: Data Collection - Collect historical transactional data including items purchased, dates, and any additional contextual information such as time of day or season. - Gather browsing history data if available, which includes items viewed, duration spent on each page, etc. Step 2: Preprocessing - Clean the data to remove any inconsistencies or irrelevant entries. - Convert all timestamps into a standard format suitable for analysis. - Merge browsing and transactional data where applicable to enrich the dataset. Step 3: Applying Action-Rules - Use the 'action-rules' package to mine action rules from the preprocessed data. Focus on identifying sequences of actions that lead to purchases. - Analyze the mined rules to understand patterns and relationships between different actions (e.g., viewing product A leads to buying product B). Step 4: Model Development - Develop a predictive model that takes into account the mined action rules and predicts the next action of a user given their current activity. - Implement a scoring system to rank potential next actions based on the strength of the mined rules. Step 5: User Interface - Design a simple web interface where users can input their current browsing session details or recent purchase history. - Display predictions for their next actions along with a confidence score derived from the strength of the associated action rule. Suggested Features: - Visualization of action sequences to help users understand the reasoning behind predictions. - Option to filter predictions based on specific criteria such as item category or time of day. - Integration with popular e-commerce platforms for seamless data import. Utilization of 'action-rules': - The 'action-rules' package will be central to the analysis phase where it mines actionable insights from raw transaction and browsing data. These insights form the basis of the predictive model and user recommendations.