AI Analysis
Final verdict: SAFE
The package exhibits minimal risks with no indications of malicious activities. The network and metadata risks are slightly elevated but not indicative of a supply-chain attack.
- Network risk due to potential external data fetches
- Maintainer has only one package, suggesting possible new or less active account
Per-check LLM notes
- Network: The package makes network calls which may be for legitimate purposes such as fetching updates or configuration data.
- Shell: No shell execution patterns detected, indicating no immediate risk associated with executing 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 may indicate a new or less active account.
Package Quality Overall: Medium (6.4/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.tomlClassifier: Framework :: Pytest
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/aignostics/foundry-python-core#readmeDetailed PyPI description (12546 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Classifier: Typing :: TypedType checker (mypy / pyright / pytype) referenced in project143 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
9 unique contributor(s) across 100 commits in aignostics/foundry-python-coreActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
try: async with httpx.AsyncClient() as client: resp = await client.get(f"https://{
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: aignostics.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository aignostics/foundry-python-core appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Oliver Meyer" 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 aignostics-foundry-core
Create a fully-functional mini-application that leverages the 'aignostics-foundry-core' package to manage and monitor a simple inventory system for a small manufacturing company. This application should allow users to add, update, delete, and view inventory items. Additionally, it should include features such as tracking stock levels, generating alerts when stock levels fall below a certain threshold, and providing a summary report of inventory status. The application will utilize the foundational infrastructure provided by 'aignostics-foundry-core' to ensure robust data management and efficient operations. Specifically, use the package to: - Implement a reliable database connection and management system for storing inventory data. - Utilize logging mechanisms to track actions performed on the inventory. - Set up alert systems based on predefined thresholds for stock levels. - Integrate monitoring tools to continuously check the health of the inventory system. Your task is to design and implement the backend logic of this application using Python and 'aignostics-foundry-core'. Provide clear documentation on how each feature integrates with the package's functionalities and ensure that the codebase is modular, scalable, and maintainable.