aury-model-core

v0.1.0 suspicious
4.0
Medium Risk

Protocol conversion and canonical model core for the Aury stack.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate metadata risk score due to missing maintainer history and lack of an associated GitHub repository, which raises suspicion about its legitimacy.

  • Moderate metadata risk
  • No maintainer history
  • No associated GitHub repository
Per-check LLM notes
  • Network: Network calls are likely for legitimate purposes such as fetching data or interacting with APIs, but should be reviewed to ensure they align with the package's intended functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
  • Metadata: The package shows some red flags, such as a lack of maintainer history and no associated GitHub repository, but there's insufficient evidence to conclude it is malicious.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (582 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

  • 377 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 9.0

Found 6 network call pattern(s)

  • manifest.to_jsonl() with httpx.Client(transport=transport, timeout=_timeout_from_metadata(options)
  • st.to_jsonl() async with httpx.AsyncClient(transport=transport, timeout=_timeout_from_metadata(options)
  • aders, stream=False) with httpx.Client(transport=transport, timeout=_timeout(request_options.timeou
  • stream=False) async with httpx.AsyncClient(transport=transport, timeout=_timeout(request_options.timeou
  • rs, stream=True) with httpx.Client(transport=transport, timeout=_timeout(request_options.timeou
  • ream=True) async with httpx.AsyncClient(transport=transport, timeout=_timeout(request_options.timeou
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 aury-model-core 
Create a mini-application that serves as a protocol converter and data validator using the 'aury-model-core' package. This application will take input data from different protocols (e.g., JSON, XML, CSV), convert them into a canonical format supported by the Aury stack, validate the converted data against predefined schemas, and then optionally convert it back to another protocol (e.g., YAML, JSON). Here are the detailed steps and features to implement:

1. **Setup**: Install 'aury-model-core' and any additional necessary Python packages such as 'xmltodict', 'pandas', and 'PyYAML'.
2. **Input Parsing**: Develop functions to parse input data from various formats like JSON, XML, and CSV.
3. **Canonical Conversion**: Use 'aury-model-core' to convert parsed data into the canonical model format used within the Aury stack.
4. **Data Validation**: Implement validation logic to check if the canonical data conforms to specific schema rules. Use 'aury-model-core' functionalities to assist in this process.
5. **Output Formatting**: Optionally, after validation, convert the canonical data back into one of the original or a new format like YAML.
6. **User Interface**: Create a simple command-line interface where users can specify the input file, desired output format, and whether they want validation applied.
7. **Testing**: Write unit tests for each parsing, conversion, and validation function to ensure reliability.

This project aims to demonstrate the versatility of 'aury-model-core' in handling protocol conversions and data standardization, providing a practical tool for developers working with diverse data sources.

💬 Discussion Feed

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