AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network, shell, obfuscation, and credential handling, but the metadata risk score is notably higher, suggesting potential concerns about the author's experience or intent.
- Metadata risk score of 4 out of 10
- Sparse author information
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 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 author information is sparse and could indicate a less experienced or potentially suspicious maintainer.
Package Quality Overall: Low (4.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (4258 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
20 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 39 commits in ryanboyd/archetypesSmall but multi-author team (3–4 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
score 3.0
Suspicious email domain flags: Very short email domain: cs.stonybrook.edu>
Very short email domain: cs.stonybrook.edu>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository ryanboyd/archetypes appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 archetyper
Create a Python-based personality analysis tool named 'ArchetypeAnalyzer' using the 'archetyper' package. This tool will allow users to input a set of characteristics or traits about themselves or others, and the tool will analyze these inputs to identify the most fitting archetypes from a predefined set of personality types. Here's a detailed breakdown of the steps and features for this project:
1. **Setup**: Install the 'archetyper' package and other necessary Python libraries.
2. **Data Input**: Develop a user-friendly interface (command-line or web-based) where users can input a series of traits or characteristics (e.g., 'introverted', 'creative', 'analytical'). These inputs should be structured as key-value pairs, such as {'trait': 'value'}.
3. **Archetype Definition**: Define a set of archetypes based on common personality frameworks (e.g., Myers-Briggs, Enneagram). Each archetype should be represented as a dictionary in the 'archetyper' format, containing relevant traits and their associated weights.
4. **Analysis**: Utilize the 'archetyper' package to match the user's input traits against the defined archetypes. The tool should calculate a score indicating how well each archetype fits the provided traits.
5. **Output**: Display the results in a clear, understandable format, highlighting the top matching archetypes and providing a brief description of each one.
6. **Enhancements**: Consider adding features like saving results to a file, allowing users to customize their own archetypes, or integrating with external data sources for more comprehensive analysis.
This project aims to demonstrate the versatility and power of the 'archetyper' package in handling complex data structures and performing meaningful analyses.
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