auraone-evalkit

v0.2.0 safe
4.0
Medium Risk

Local open-source evaluation tooling for rubric validation, linting, and deterministic scoring.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with no immediate signs of malicious intent. The maintainer's single package history suggests they might be new or less active, but this alone isn't indicative of a threat.

  • Maintainer has only one package
  • No suspicious activity or code detected
Per-check LLM notes
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Low (4.2/10)

◈ 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://auraone.ai/open/private-evals
  • Detailed PyPI description (7296 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

  • 176 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

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • e() -> bool: try: __import__("inspect_ai") except ImportError: return False return Tru
  • e() -> bool: try: __import__("lm_eval") except ImportError: return False return Tru
  • score += (act - exp) * __import__("math").log(act / exp) return score def drift_report(rows: It
✓ 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 2.0

1 maintainer concern(s) found

  • Author "AuraOne" 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 auraone-evalkit 
Create a Python-based educational platform mini-application that leverages the 'auraone-evalkit' package to enhance student assignments submission and evaluation processes. This app will streamline the workflow for educators by automating the assessment of student work based on predefined rubrics, ensuring consistency and fairness in grading. Additionally, it will provide immediate feedback to students, helping them understand their performance and areas for improvement.

Key Features:
1. **Rubric Validation**: Before any assignment is submitted, the system should validate that the rubric provided by the educator is correctly formatted and meets all necessary criteria. This ensures that the rubric is fair and comprehensive.
2. **Linting Functionality**: Implement a feature that checks submissions against a set of predefined coding standards. This helps in maintaining code quality and adheres to best practices.
3. **Deterministic Scoring**: Use 'auraone-evalkit' to score each submission based on the validated rubric, ensuring that every student receives a consistent and fair grade.
4. **Immediate Feedback**: Provide instant feedback to students upon submission, highlighting strengths and weaknesses according to the rubric.
5. **User Interface**: Develop a simple web interface using Flask or Django where educators can upload rubrics, and students can submit their assignments.
6. **Admin Dashboard**: Include an admin dashboard where educators can view all submissions, scores, and feedback for each student.
7. **Reporting Tools**: Generate reports summarizing class performance based on various metrics derived from the rubric.

How to Utilize 'auraone-evalkit':
- For Rubric Validation: Use 'auraone-evalkit' to parse and validate the rubric before it is applied to any assignment. This ensures that the rubric is structured correctly and all criteria are accounted for.
- For Deterministic Scoring: After validating the rubric, use 'auraone-evalkit' to apply the rubric to each submission, calculating scores in a deterministic manner to ensure consistency across all evaluations.
- For Linting: Integrate 'auraone-evalkit' linting capabilities to check the code submissions against coding standards, providing additional insights into the quality of the work.
- For Immediate Feedback: Use 'auraone-evalkit' to generate detailed feedback based on the rubric and coding standards, which can be immediately displayed to students upon submission.

This project aims to simplify the assessment process while enhancing the learning experience for both educators and students.

💬 Discussion Feed

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