astroLACHESIS

v0.0.10 suspicious
3.0
Low Risk

Isochrone fitting with Bayesian Model Averaging

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks associated with network calls, shell executions, obfuscations, and credential harvesting. However, the low maintainer activity and poor metadata quality raise concerns about its legitimacy and maintenance.

  • Low maintainer activity
  • Poor metadata quality
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low maintainer activity and poor metadata quality, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 98 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with astroLACHESIS 
Develop a web-based mini-application using Flask that allows astronomers to upload their star cluster data and perform isochrone fitting with Bayesian Model Averaging. The application should leverage the 'astroLACHESIS' Python package to provide users with an intuitive interface for analyzing stellar populations within star clusters. Users should be able to input parameters such as age, metallicity, and distance modulus, and the app should generate isochrones that best fit the observed data. Additionally, the application should display the results visually, showing the best-fitting isochrones alongside the observed data points. Include features like saving the results to a local file or database, and allow users to compare multiple fits. Ensure the application is well-documented and includes examples of how to use it with sample datasets provided by the 'astroLACHESIS' package.

💬 Discussion Feed

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