array-api-shape-check

v0.1.3 safe
4.0
Medium Risk

Check shapes of input arrays easily

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activities such as network calls, shell executions, or credential harvesting. However, the low activity and new/inactive status of the maintainer slightly increase the metadata risk.

  • No network calls or shell executions detected.
  • Maintainer's new/inactive status raises minor concerns.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on array shape checks.
  • Shell: No shell execution patterns detected, consistent with a benign utility package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's low activity and the maintainer's new/inactive status raise concerns, but there are no direct indicators of malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_main.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "documentation" -> https://array-api-shape-check.readthedocs.io
  • Detailed PyPI description (5034 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 5.0

Partial type annotation coverage

  • 8 type-annotated function signatures (partial)
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 9 commits in 34j/array-api-shape-check
  • Small but multi-author team (3–4 contributors)

🔬 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: simplelogin.com>

✓ 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 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 array-api-shape-check 
Create a mini-application called 'ShapeChecker' that helps users validate the shapes of multi-dimensional arrays in their Python projects. This application will utilize the 'array-api-shape-check' package to streamline the process of shape validation. Here are the steps and features you need to implement:

1. **Setup**: Initialize your project with the necessary Python libraries, including 'array-api-shape-check'. Ensure that the application can be run as a standalone script or integrated into other Python projects.
2. **Input Handling**: Implement a user-friendly interface where users can input their arrays either through direct code execution or via a command-line interface. Support various data types and dimensions for flexibility.
3. **Validation Logic**: Use 'array-api-shape-check' to define functions that check if the input arrays meet specific criteria such as having equal dimensions, being within certain size limits, or matching a predefined shape template.
4. **Output Display**: Provide clear, informative output that indicates whether the array(s) passed the validation tests or not. Include error messages that suggest possible corrections if the input does not match expected criteria.
5. **Advanced Features**:
   - Allow users to save common validation scenarios as presets for future use.
   - Implement a feature to compare multiple arrays against each other based on shape similarity.
   - Offer options to generate random arrays that fit specific shape requirements for testing purposes.
6. **Documentation**: Write comprehensive documentation that explains how to install and use 'ShapeChecker', including examples of typical use cases and troubleshooting tips.

Your goal is to create a versatile tool that simplifies working with arrays by focusing on one of the most critical aspects: ensuring the correct shape and dimensionality.

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