algorhino-anemone

v0.2.20 safe
4.0
Medium Risk

anemone searches trees

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious behavior with low risks across network, shell, and obfuscation checks. However, the metadata risk due to low maintainer engagement is notable but insufficient to suggest a supply-chain attack.

  • Low risk scores in network, shell, and obfuscation checks.
  • Metadata risk due to limited maintainer activity.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's lack of engagement and the maintainer's limited activity raise some concerns.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3554 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

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

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in victorgabillon/anemone
  • Single author but highly active (100 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

Email domain looks legitimate: gmail.com>

✓ 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 algorhino-anemone 
Create a Python-based mini-application named 'TreeExplorer' that leverages the 'algorhino-anemone' package to explore and visualize hierarchical data structures. This application should enable users to input tree-like datasets (e.g., organizational charts, file systems, or any other structured data) and then utilize 'algorhino-anemone' to search through these trees efficiently.

### Key Features:
1. **User Input Interface**: Allow users to either upload a CSV file containing tree data or manually enter the tree structure via a command-line interface.
2. **Data Validation**: Ensure the integrity of the input data, checking for proper tree structure (no cycles, all nodes properly connected).
3. **Search Functionality**: Implement a feature using 'algorhino-anemone' to perform searches within the tree based on user-defined criteria (e.g., node value, depth level).
4. **Visualization Tool**: Integrate a simple visualization component that displays the tree structure graphically, highlighting the search results.
5. **Export Options**: Provide options for exporting the searched subtree or the entire tree into various formats (JSON, CSV, PNG).

### Steps to Build:
1. **Setup Environment**: Install necessary Python packages including 'algorhino-anemone', pandas for data manipulation, and matplotlib or networkx for visualization.
2. **Input Handling**: Develop functions to parse CSV files and validate tree structures.
3. **Integration with 'algorhino-anemone'**: Use 'algorhino-anemone' to implement the search functionality, ensuring it supports multiple search criteria.
4. **Graphical Visualization**: Utilize matplotlib or networkx to create visual representations of the tree, with an emphasis on clarity and ease of understanding.
5. **Export Mechanisms**: Implement functionality to export the tree data and search results in desired formats.
6. **Testing and Documentation**: Conduct thorough testing to ensure the application works as expected and document the code and usage instructions clearly.

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