asta-papers

v0.0.1 safe
3.0
Low Risk

Legal-only paper full-text retrieval and conversion. DOI/PMID/PMCID/arXiv/Corpus ID + BYO PDF/JATS → markdown with license classification.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be designed for legal purposes with minimal risk indicators. However, its novelty and unavailability of source code repository warrant further observation.

  • Low risk scores across all categories.
  • Repository not found, raising minor concern.
Per-check LLM notes
  • Network: The network calls appear to be legitimate API requests to retrieve papers, which aligns with the package's likely purpose.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
  • Metadata: The package appears to be new and the repository is not found, raising some concerns but no concrete evidence of malice.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 13 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/allenai/asta-sdk/tree/main/src/python/ast
  • Detailed PyPI description (4745 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 115 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 score 3.0

Found 2 network call pattern(s)

  • ache self._session = requests.Session() self._session.headers.update({"User-Agent": self.c
  • mc in candidates: r = requests.get( f"https://europepmc.org/articles/{pmc}?pdf=rend
✓ 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 score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Allen Institute for AI" 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 asta-papers 
Create a legal document management tool named 'LegalPulse' using the Python package 'asta-papers'. This tool will allow users to search for legal papers by DOI, PMID, PMCID, arXiv, or Corpus ID, retrieve their full texts, convert them into markdown format, and classify them based on their licenses. Here’s a step-by-step guide on how to build this tool:

1. **Setup Environment**: Ensure Python 3.x is installed along with pip. Install the required packages including 'asta-papers', 'requests', 'beautifulsoup4', and any other necessary libraries.

2. **User Interface**: Design a simple web interface using Flask or Django where users can input identifiers (DOI, PMID, etc.) of legal documents they want to retrieve.

3. **Retrieve Document**: Implement a function that uses 'asta-papers' to fetch the full text of the document from various sources like PubMed, arXiv, etc., based on the identifier provided by the user.

4. **Convert to Markdown**: Integrate 'asta-papers' functionality to convert the retrieved full text into markdown format. This should preserve the structure and readability of the document.

5. **License Classification**: Utilize 'asta-papers' to classify the document based on its license. This could include Creative Commons licenses, public domain works, or proprietary content.

6. **Display Results**: On the web interface, display the converted markdown document and its license classification in an easily readable format.

7. **Additional Features**:
   - Allow users to upload their own PDF or JATS files for conversion to markdown.
   - Provide an option to download the markdown version of the document.
   - Include a feature to save documents to a personal library within the app.
   - Offer a search history and bookmarking system for users to keep track of previously viewed documents.

8. **Testing and Deployment**: Thoroughly test the application to ensure all features work as expected. Deploy the application using services like Heroku or AWS.

By following these steps, you will have created a powerful tool for legal professionals and researchers to manage and access legal documents efficiently.

💬 Discussion Feed

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