autobidsify

v0.9.6 suspicious
4.0
Medium Risk

Automated BIDS standardization tool powered by LLM-first architecture

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some concerning indicators such as network and shell execution risks, along with metadata issues. These factors suggest potential misuse but do not conclusively point to malicious intent.

  • network risk
  • shell execution risk
  • metadata inconsistencies
Per-check LLM notes
  • Network: Network calls to an external API may indicate legitimate functionality, but could also be used for unexpected data transmission.
  • Shell: Execution of external commands like 'dcm2niix' is likely intended for the package's functionality, but can pose risks if not properly controlled or sanitized.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags, including an author with limited information and a new or inactive account.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://autobidsify.readthedocs.io
  • Detailed PyPI description (7453 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

  • 199 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 75 commits in cotilab/autobidsify
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • ture try: resp = requests.post(f"{base_url}/api/chat", json=payload, timeout=300) r
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • mp directory result = subprocess.run( [ "dcm2niix", "
  • Run dcm2niix result = subprocess.run( [ dcm2niix_path,
  • ") try: result = subprocess.run( [validator_path, "--json", str(bids_root)],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: northeastern.edu>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 autobidsify 
Your task is to create a fully-functional mini-application called 'BIDSifier' using the Python package 'autobidsify'. This application will serve as an automated tool for researchers working with neuroimaging data, enabling them to quickly and accurately convert their datasets into the Brain Imaging Data Structure (BIDS) format, which is widely recognized for its standardized organization of neuroimaging data.

Step-by-Step Instructions:
1. Begin by setting up a new Python virtual environment for your project.
2. Install the 'autobidsify' package along with any other necessary dependencies such as pandas and numpy.
3. Design a user-friendly command-line interface (CLI) where users can input paths to their raw neuroimaging data directories.
4. Implement functionality within your CLI to scan the input directory and identify potential files that could be part of a BIDS dataset.
5. Use 'autobidsify' to analyze these files and determine the best way to structure them according to the BIDS standard.
6. Automatically generate a BIDS-compliant folder structure and move/copy the files into the appropriate locations.
7. Optionally, add a feature that allows users to specify certain customizations or exceptions in the BIDS conversion process.
8. Finally, ensure your application logs all actions taken during the BIDSification process for audit and debugging purposes.

Suggested Features:
- Support for common neuroimaging file formats such as NIfTI (.nii), DICOM, and JSON metadata files.
- Ability to handle both single-file conversions and batch processing of multiple files/directories.
- User-configurable options for specifying file naming conventions, subject IDs, session dates, etc.
- Integration with popular cloud storage solutions for remote data access.
- Detailed error handling and reporting mechanisms.

How to Utilize 'autobidsify':
- Leverage 'autobidsify' to perform intelligent analysis on the input data, identifying patterns and relationships between files.
- Use the package's LLM-first architecture to predict the most likely BIDS-compliant structure based on the content and context of the files.
- Automate the process of moving, renaming, and organizing files according to the BIDS specification.
- Incorporate 'autobidsify' feedback into your logging mechanism to provide users with insights into why certain decisions were made during the BIDSification process.

💬 Discussion Feed

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