astrosylva

v0.1.0a2 suspicious
4.0
Medium Risk

Convert halo merger trees (Consistent-Trees, LHaloTree, SubLink, AHF) into the Galacticus HDF5 input format.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity such as network calls, shell executions, or credential harvesting. However, the metadata risk score is elevated due to sparse and potentially inactive author information, raising suspicion about the package's origin and intentions.

  • Sparse and possibly inactive author information
  • No direct evidence of malicious activities
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
  • Metadata: The author's information is sparse and the account seems new or inactive, which raises some suspicion but not enough to conclusively determine malice.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 14 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://astrosylva.readthedocs.io
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4072 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

  • 221 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 7 unique contributor(s) across 80 commits in galacticusorg/astrosylva
  • Active community — 5 or more distinct contributors

🔬 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

Email domain looks legitimate: carnegiescience.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository galacticusorg/astrosylva appears legitimate

⚠ 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 astrosylva 
Create a Python-based mini-application that facilitates the conversion of halo merger trees from various formats into the Galacticus HDF5 input format using the 'astrosylva' package. This tool will enable researchers and astronomers to easily manipulate and analyze large datasets related to cosmological simulations. Here’s a step-by-step guide on how to develop this application:

1. **Project Setup**: Initialize a new Python environment and install the required packages, including 'astrosylva'.
2. **Input Handling**: Design a user-friendly interface that allows users to upload their halo merger tree files in different supported formats (e.g., Consistent-Trees, LHaloTree, SubLink, AHF).
3. **Data Conversion**: Utilize the 'astrosylva' package to convert the uploaded data into the Galacticus HDF5 format. Ensure that the conversion process is efficient and preserves all necessary information.
4. **Output Management**: Provide options for saving the converted HDF5 file locally or uploading it to a cloud storage service like AWS S3.
5. **Error Handling & Logging**: Implement robust error handling and logging mechanisms to capture any issues during file processing and provide meaningful feedback to users.
6. **Documentation & Help**: Include comprehensive documentation and a help section within the application to guide users through the process and troubleshoot common issues.

Suggested Features:
- Support for multiple input file formats.
- Real-time progress tracking during the conversion process.
- Option to visualize the structure of the input and output data.
- Integration with popular cloud storage services.
- Detailed logs and error messages for troubleshooting.

By following these steps and incorporating the suggested features, your application will serve as a valuable tool for researchers working with complex cosmological datasets.

💬 Discussion Feed

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