AI Analysis
Final verdict: SAFE
The package presents minimal risks based on the analysis, with no detected network calls, shell executions, obfuscations, or credential harvesting attempts. However, the low activity and engagement from the maintainer and repository slightly increase the metadata risk.
- No network calls detected.
- Repository shows low community engagement.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution detected, indicating the package does not attempt to run external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer seems new or inactive, and the repository lacks community engagement.
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
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Thor Whalen" 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 acture
Create a Python-based mini-application that leverages the 'acture' package to interact with an Acture MCP server, enabling users to manage data through a dictionary-like interface over the Model Context Protocol. Your task is to develop a simple inventory management system for a small store. This application should allow the user to add new items, update existing item details, delete items from the inventory, and retrieve item information based on various criteria such as item ID, category, or price range. Additionally, implement a feature that allows the user to search for items using natural language queries, where the application interprets the query and fetches relevant results from the inventory. Key Features: - Add new items to the inventory with fields such as item ID, name, category, price, and stock quantity. - Update existing item details including changing the name, category, price, and stock quantity. - Delete items from the inventory based on item ID. - Retrieve item information based on item ID, category, and price range. - Implement a natural language query parser to search for items in the inventory. How to Utilize the 'acture' Package: 1. Install the 'acture' package using pip: `pip install acture`. 2. Initialize the connection to the Acture MCP server using the 'acture' client. 3. Use the 'acture' client to interact with the server as if it were a dictionary, allowing you to perform CRUD operations on the inventory data. 4. For each operation (add, update, delete, retrieve), ensure that the corresponding action is performed via the 'acture' client to maintain consistency and integrity of the data stored on the server.