anthro

v1.1.1 suspicious
7.0
High Risk

WHO 2006 Child Growth Standards — z-scores and classifications for 6 indicators

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant metadata risks, with no maintainer history and a single version release, which raises concerns about its legitimacy and long-term maintenance.

  • Low obfuscation and credential risk
  • High metadata risk due to incomplete maintainer information and single version release
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package shows several red flags including a lack of maintainer history, a single version release, and an incomplete author profile, indicating potential risk.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_anthro.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3632 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

  • 38 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 21 commits in flame-cai/anthro
  • Single author but highly active (21 commits)

🔬 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 anthro 
Create a Python-based web application that allows healthcare professionals and researchers to analyze child growth data against the World Health Organization's (WHO) 2006 Child Growth Standards. The application should use the 'anthro' package to calculate z-scores and provide classifications for six key indicators of child growth: weight-for-age, height-for-age, BMI-for-age, head circumference-for-age, weight-for-height, and BMI-for-age. Here are the steps and features your application should include:

1. **User Interface Design**: Develop a clean, user-friendly interface where users can input child-specific data such as age, gender, and measurements like weight, height, etc.
2. **Data Input Validation**: Ensure that all inputs are validated to prevent errors and ensure accuracy. For example, check if the age is within the specified range for the WHO standards.
3. **Integration with 'anthro' Package**: Use the 'anthro' package to compute z-scores and classifications based on the input data. Display these results clearly on the webpage.
4. **Graphical Representation**: Implement a feature to display the calculated z-scores graphically, allowing users to visualize how their child's growth compares to the WHO standards.
5. **Export Results**: Provide an option for users to export the analysis results in PDF format for record-keeping or further review.
6. **Educational Resources**: Include links to additional resources explaining the significance of each indicator and what the z-score classifications mean.
7. **Responsive Design**: Ensure the application is responsive and works well on both desktop and mobile devices.

This project aims to leverage the power of Python's 'anthro' package to offer a valuable tool for monitoring and understanding child growth patterns, thereby contributing to better health outcomes.

💬 Discussion Feed

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