autorubric

v1.5.0 safe
3.0
Low Risk

A Python library encapsulating best practices for rubric-based evaluation of LLM/VLM outputs using LLM-as-a-judge.

🤖 AI Analysis

Final verdict: SAFE

The package autorubric v1.5.0 appears to have minimal risks with no signs of malicious activity. The only slightly elevated concern is the potential for external data fetching, but this seems to align with expected behavior.

  • network risk due to possible external data fetching
  • low risk in all other categories
Per-check LLM notes
  • Network: The network call pattern suggests the package may be fetching data from an external source, which could be normal if it's part of its intended functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, but there are no other red flags.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

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

Some documentation present

  • Detailed PyPI description (5339 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

  • 259 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 68 commits in delip/autorubric
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • last_log = 0.0 with urllib.request.urlopen(url) as resp: total = int(resp.headers.get("
✓ 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

Repository delip/autorubric appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Delip Rao" 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 autorubric 
Develop a Python-based mini-application named 'AutoEval' that leverages the 'autorubric' package to automatically evaluate student essays based on predefined rubrics. The application should allow educators to input essay prompts, upload a set of student essays, and define evaluation criteria through a user-friendly interface. Here’s a detailed breakdown of the steps and features required:

1. **Setup Interface**: Create a simple command-line interface (CLI) or a basic web interface using Flask or Django, allowing users to interact with the system easily.
2. **Essay Upload**: Implement functionality for uploading multiple student essays into the system. Each essay should be stored temporarily or in a database for evaluation.
3. **Rubric Definition**: Provide tools within the application for defining rubrics. Rubrics should include categories like grammar, argumentation, and creativity, each with specific criteria and weightings.
4. **Evaluation Process**: Use the 'autorubric' package to automate the evaluation process. This involves training an LLM to act as a judge based on the rubric, then having it evaluate the uploaded essays according to the defined criteria.
5. **Results Presentation**: Display evaluation results in a structured format, highlighting strengths and weaknesses of each essay. Include a summary of overall performance based on the rubric.
6. **Feedback Generation**: Enhance the application to generate personalized feedback for each essay, tailored to the individual student's work based on the evaluation.
7. **Integration Testing**: Ensure thorough testing of all components, including integration tests between the user interface and the 'autorubric' evaluation engine.
8. **Documentation**: Write comprehensive documentation detailing how to install and use AutoEval, including examples of rubric definitions and sample evaluations.

This project aims to streamline the grading process for educators, providing automated yet nuanced evaluations that adhere to educational standards.

💬 Discussion Feed

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