AI Analysis
Final verdict: SAFE
The package exhibits low risks across multiple categories including network, shell, obfuscation, and credential risks. While there are some concerns about metadata suggesting low maintenance efforts, these do not strongly indicate malicious activity.
- Low network risk due to expected aiohttp usage
- No evidence of shell execution or obfuscation
Per-check LLM notes
- Network: The detection of network calls is expected as the package likely uses aiohttp for asynchronous HTTP requests, which is common for client libraries.
- Shell: No shell execution patterns were detected, indicating no immediate risk associated with shell command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some signs of low maintenance and possibly low effort, but there are no clear indicators of malicious intent.
Package Quality Overall: Low (3.6/10)
✦ High
Test Suite
9.0
Test suite present — 16 test file(s) found
16 test file(s) detected (e.g. test_default_api.py)
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ 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
49 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 1.5
Found 1 network call pattern(s)
self.pool_manager = aiohttp.ClientSession( connector=aiohttp.TCPConnector(limit=self.m
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: openapitools.org
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 "OpenAPI Generator community" 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 aind-slims-service-async-client
Create a fully-functional mini-application named 'LabInventoryManager' using the Python package 'aind-slims-service-async-client'. This application will serve as a simple inventory management system for a laboratory, allowing researchers and lab technicians to keep track of their equipment, supplies, and consumables. The app should provide functionalities such as adding new items to the inventory, updating item details, marking items as borrowed or returned, and searching for specific items based on various criteria like name, type, or status. Step 1: Set up your development environment with Python installed and create a virtual environment for the project. Step 2: Install the required packages including 'aind-slims-service-async-client', and any other necessary dependencies such as FastAPI for the backend and React for the frontend if you choose to build a web interface. Step 3: Utilize 'aind-slims-service-async-client' to interact with the underlying SLIMS service asynchronously. This includes setting up the client configuration, connecting to the service, and performing CRUD operations (Create, Read, Update, Delete) on inventory items. Step 4: Implement the following features: - Add new items to the inventory with fields such as item ID, name, description, type, quantity, and status. - Update existing item details when needed. - Mark items as borrowed or returned, changing their status accordingly. - Search for items by different attributes such as name, type, and status. - Provide a user-friendly interface for these operations either through a command-line tool or a web-based application. Step 5: Ensure the application handles errors gracefully and provides informative feedback to users. Step 6: Document your code thoroughly and include instructions on how to set up and run the application. The goal is to demonstrate proficiency in using 'aind-slims-service-async-client' while building a practical, useful application for managing laboratory inventories.
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