ai-paper-review

v0.6.0 safe
1.0
Low Risk

AI paper review system: multiple LLM reviewer personas, parallel review, clustering and ranking, human-feedback calibration.

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activities such as network calls, shell executions, obfuscations, or credential risks. It appears safe to use within the described parameters.

  • No network calls detected.
  • No shell execution patterns found.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (31894 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 152 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 19 commits in unarylab/ai-paper-review
  • Single author with few commits — possibly a personal or throwaway project

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository unarylab/ai-paper-review appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Paper Review Contributors" 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 ai-paper-review 
Develop a fully-functional mini-application called 'PaperCritiquePro' using the Python package 'ai-paper-review'. This application aims to streamline the process of reviewing academic papers by leveraging multiple LLM (Large Language Model) reviewers, parallel processing capabilities, and advanced clustering and ranking algorithms. The goal is to provide a comprehensive critique on the paper, including insights from different perspectives and a final ranked summary of the reviews.

**Steps to Develop PaperCritiquePro:**
1. **Setup**: Begin by installing the necessary packages including 'ai-paper-review'. Ensure your environment is set up correctly to handle parallel processing.
2. **User Interface**: Design a simple yet effective user interface where users can upload their PDF documents or input a URL of the paper they wish to review.
3. **LLM Reviewers**: Utilize the 'ai-paper-review' package to create multiple LLM reviewer personas. Each persona should have a distinct approach to reviewing, such as focusing on methodology, results, or impact.
4. **Parallel Processing**: Implement parallel processing to allow multiple reviews to occur simultaneously. This will significantly reduce the time needed to receive a full critique.
5. **Clustering and Ranking**: After all reviews are completed, use the clustering feature of 'ai-paper-review' to group similar critiques together. Then, rank these clusters based on various criteria such as relevance and depth of analysis.
6. **Human-Feedback Calibration**: Integrate a feedback loop where users can provide feedback on the reviews. Use this data to calibrate the LLMs, improving future critiques.
7. **Final Summary**: Generate a concise summary of the review process, highlighting key points from each cluster and providing an overall assessment of the paper.

**Suggested Features**:
- A dashboard showing the progress of the review process.
- Option to download the full set of reviews.
- Integration with common citation management tools.
- Ability to save and revisit past submissions.
- Detailed logs for each review session, including timestamps and reviewer details.

By following these steps and incorporating the suggested features, you'll create a powerful tool for researchers and academics looking to efficiently and comprehensively evaluate academic papers.