axis-synome

v0.1.0.dev25206189205 suspicious
4.0
Medium Risk

Axis specification modules (entities, formulas, validators)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to potential code obfuscation and low maintainer activity, but does not exhibit clear malicious behavior.

  • High obfuscation risk due to use of eval
  • Low maintainer activity and poor metadata quality
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk from command execution.
  • Obfuscation: Use of eval with a simple arithmetic operation suggests an attempt to obfuscate code rather than perform a necessary computation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising concerns about its legitimacy.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/archon-research/next-gen-atlas
  • Detailed PyPI description (1439 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

  • 85 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • def f() -> float:\n return eval('1 + 1')\n") assert any('builtin "eval" is not allow
✓ 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: archontech.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 axis-synome 
Your task is to develop a fully functional mini-application that utilizes the 'axis-synome' Python package to manage and validate data according to specific axis specifications. This application will serve as a tool for data scientists and analysts who need to ensure their datasets comply with certain structural requirements before performing complex analyses.

### Application Overview:
- **Name**: AxisValidator
- **Purpose**: To validate datasets against predefined axis specifications using entities, formulas, and validators from the 'axis-synome' package.

### Core Features:
1. **Data Input**: Allow users to upload CSV files containing dataset information.
2. **Axis Specification Definition**: Users should be able to define axis specifications using the entities, formulas, and validators provided by the 'axis-synome' package.
3. **Validation Engine**: Implement a validation engine that uses the defined axis specifications to check if the uploaded dataset meets all specified criteria.
4. **Result Presentation**: Display validation results clearly, indicating which parts of the dataset pass or fail the validation tests.
5. **Error Handling**: Provide detailed error messages when a dataset fails validation, suggesting possible corrections.

### Detailed Steps:
1. **Setup Environment**:
   - Install Python and necessary libraries including 'axis-synome'.
   - Set up a virtual environment for the project.

2. **User Interface**:
   - Design a simple web-based UI using Flask or Django for easy file uploads and specification definitions.

3. **Entity, Formula, and Validator Setup**:
   - Use 'axis-synome' to create entities representing different aspects of your dataset (e.g., columns, rows).
   - Define formulas to calculate expected values based on these entities.
   - Create validators that apply these formulas to verify dataset integrity.

4. **Data Processing and Validation**:
   - Parse uploaded CSV files into usable data structures.
   - Apply defined validators to the parsed data.
   - Collect and organize validation outcomes.

5. **Result Display**:
   - Present validation results in a user-friendly manner, highlighting issues and providing actionable feedback.

6. **Testing and Deployment**:
   - Thoroughly test the application with various datasets and specifications.
   - Deploy the application to a server or cloud platform for public access.

### Utilization of 'axis-synome':
- Entities will represent the structure of the dataset (e.g., column names, types).
- Formulas will define relationships between data points (e.g., sum of values in a row).
- Validators will enforce these rules, ensuring the dataset adheres to the specified structure and logic.

By following these steps and utilizing 'axis-synome', you'll create a powerful yet accessible tool for managing and validating structured data.

💬 Discussion Feed

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