apcore

v0.22.0 safe
4.0
Medium Risk

Schema-driven module standard for AI-perceivable interfaces

🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with no clear indications of malicious intent. However, the low credibility of the maintainer's metadata should be monitored.

  • Low shell risk
  • Common network interaction through aiohttp
  • Maintainer has low credibility due to new or inactive account
Per-check LLM notes
  • Network: The use of aiohttp for making network requests is common and indicates the package performs some form of network interaction, likely legitimate.
  • Shell: No shell execution patterns detected, suggesting no immediate risk related to shell command execution.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, indicating potential low credibility.

📦 Package Quality Overall: Medium (5.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/aiperceivable/apcore
  • Detailed PyPI description (19609 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 :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 661 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in aiperceivable/apcore-python
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • _headers} async with aiohttp.ClientSession(timeout=timeout) as session: async with session.
  • / 1000.0) async with aiohttp.ClientSession(timeout=timeout) as session: async with session.
✓ 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

Repository aiperceivable/apcore-python appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 apcore 
Your task is to develop a Python-based mini-application named 'AI-Perceivable Interface Tester' (APITester). This tool will serve as a demonstration of how the 'apcore' package can be utilized to create schema-driven interfaces that are perceivable by AI systems. The APITester will allow users to define custom schemas for various data structures, test these schemas against real-world data, and receive feedback on the compatibility and correctness of the provided data according to the defined schemas.

### Key Features:
1. **Schema Definition**: Users should be able to define their own schemas using a simple, intuitive interface provided by the 'apcore' package. Schemas can include constraints like data types, ranges, and relationships between different fields.
2. **Data Input**: Users can input real-world data that they want to validate against the defined schemas. This could be done through a file upload feature or manual entry.
3. **Validation & Feedback**: Upon submission, the APITester will use the 'apcore' package to validate the data against the defined schema. It should provide detailed feedback on whether each piece of data conforms to the schema, highlighting any discrepancies or errors.
4. **Visualization**: To enhance user understanding, the tool should visualize the validation results, showing which parts of the data match the schema and which do not.
5. **Documentation Generation**: For each schema, the APITester should automatically generate documentation detailing the structure, constraints, and any validation rules applied.
6. **User Interface**: Develop a clean, user-friendly web interface using a framework like Flask or Django. Ensure the UI is responsive and accessible.

### Utilization of 'apcore':
- Use 'apcore' to define and manage schemas. Explore its capabilities in creating, modifying, and validating schemas.
- Leverage 'apcore's schema-driven approach to ensure that all data validation processes are consistent and AI-perceivable.
- Integrate 'apcore' into the backend logic to handle all schema-related operations, ensuring seamless interaction between the frontend and the validation engine.

### Deliverables:
- A fully functional web application that allows users to define schemas, input data, and receive validation feedback.
- Source code that is well-documented, with clear comments explaining how 'apcore' is integrated into the project.
- A short report detailing the challenges faced during development and how 'apcore' helped in overcoming them.

This project aims to showcase the versatility and power of the 'apcore' package in creating robust, AI-perceivable interfaces.

💬 Discussion Feed

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