allotropy

v0.1.134 safe
3.0
Low Risk

Converts to ASM (Allotrope Simple Model) from various file formats.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell risks from benign version control operations, no obfuscation, and no credential harvesting attempts. The metadata suggests a less experienced maintainer but does not indicate malicious behavior.

  • No network calls detected
  • Shell risks are benign version control operations
  • Maintainer has a single package and lacks author name
Per-check LLM notes
  • Network: No network calls were detected.
  • Shell: The shell execution patterns detected are related to version control operations and do not indicate malicious activity.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The maintainer's author name is missing and they appear to have only one package, which may indicate a less experienced or potentially suspicious account.

📦 Package Quality Overall: Medium (6.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Benchling-Open-Source/allotropy#readme
  • Detailed PyPI description (4356 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
  • 99 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 100 commits in Benchling-Open-Source/allotropy
  • 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 score 10.0

Found 6 shell execution pattern(s)

  • dict[str, list[str]]: p = subprocess.run( ["git", "log", "--oneline"], capture_output=True, t
  • print("Making commit...") subprocess.run( [ "git", "commit",
  • rint("Pushing commit...") subprocess.run(["git", "push", "-u", "origin", f"release-v{version}"], chec
  • rint("Tagging branch...") subprocess.run( ["git", "tag", "-a", f"v{version}", "-m", f"'allotr
  • ion}'"], check=True ) subprocess.run(["git", "push", "origin", "tag", f"v{version}"], check=True)
  • int("Creating PR...") subprocess.run( [ "gh", "pr",
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: benchling.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Benchling-Open-Source/allotropy 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 allotropy 
Create a Python-based mini-application called 'ASM Converter' that leverages the 'allotropy' package to convert data from various scientific file formats into the Allotrope Simple Model (ASM). Your goal is to develop a user-friendly tool that scientists and researchers can use to easily convert their experimental data into a standardized format for better data sharing and analysis. Here’s a step-by-step guide on how to approach this project:

1. **Setup Environment**: Begin by setting up a virtual environment and installing necessary packages including 'allotropy'. Ensure your application has a clean and organized structure.
2. **Define Supported Formats**: Identify a range of scientific file formats (e.g., CSV, Excel, JSON, etc.) that your application will support for conversion into ASM. Document these supported formats clearly.
3. **Design User Interface**: Develop a simple yet effective command-line interface (CLI) or a graphical user interface (GUI) using libraries like Tkinter or PyQt. The UI should allow users to select input files, specify output directories, and initiate the conversion process.
4. **Implement Conversion Logic**: Utilize the 'allotropy' package to implement the core functionality of converting data from selected file formats into ASM. Ensure the converted data retains all essential information and metadata from the original files.
5. **Add Error Handling**: Incorporate robust error handling mechanisms to manage issues such as incorrect file formats, missing files, and conversion errors. Provide informative messages to help users understand and resolve any encountered problems.
6. **Testing and Validation**: Rigorously test your application to ensure it accurately converts data across different file formats. Validate the integrity of the converted ASM files against known standards and benchmarks.
7. **Documentation and Packaging**: Write comprehensive documentation detailing how to install and use the 'ASM Converter', along with examples and best practices. Package your application so it can be easily distributed and installed via pip.
8. **Enhancements and Features**: Consider adding advanced features such as batch processing, automatic metadata extraction, and support for additional file types. These enhancements will make your application more versatile and valuable to its users.

By following these steps, you'll create a powerful and user-friendly tool that simplifies the process of converting scientific data into a standardized format, facilitating better collaboration and data interoperability in research.