🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell executions, or obfuscation techniques. The primary concern is the metadata risk due to the package being new and having limited maintainer history.

No network calls detected.
No shell execution patterns observed.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Metadata: The package is new with limited maintainer history, but there are no immediate red flags.

🔬 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: nist.gov

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Rachael Sexton" 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 affinis

Create a Python-based social network analysis tool called 'Affinity Mapper'. This tool will leverage the 'affinis' package to analyze and visualize connections within a given dataset of social media users. Your goal is to build a fully-functional mini-application that allows users to input their social media data (such as followers/following lists), and then use 'affinis' to identify key influencers, clusters of closely connected users, and overall network structure.

Steps to complete the project:
1. Research and understand the functionalities provided by the 'affinis' package, even though its official documentation might not be detailed.
2. Design a simple user interface using a Python library such as Streamlit or Flask, where users can upload their social media data in CSV format.
3. Implement a function that reads the uploaded CSV file and converts it into a suitable format for 'affinis' to process.
4. Use 'affinis' to perform network analysis on the converted data, identifying key metrics like centrality measures (e.g., degree centrality, betweenness centrality) and clustering coefficients.
5. Develop visualization capabilities within your application to graphically represent the network structure, highlighting key nodes and clusters identified by the analysis.
6. Extend the functionality by allowing users to filter the network based on certain criteria (e.g., date range, activity level).
7. Ensure the application can handle errors gracefully, providing meaningful feedback to users when something goes wrong.
8. Test the application thoroughly with different datasets to ensure reliability and accuracy.
9. Document your code and provide clear instructions on how to run the application.

Suggested Features:
- Real-time visualizations of the network as the data is processed.
- A feature to export the results of the analysis (network diagrams, key metrics) in various formats (PNG, PDF, CSV).
- Integration with popular social media platforms for direct data import (if possible).
- Support for multiple types of social networks (Twitter, Instagram, LinkedIn, etc.).

How 'affinis' is utilized:
- The 'affinis' package will be used primarily for network analysis tasks, such as calculating centrality measures and detecting community structures within the social network data.
- It may also be leveraged for more advanced analyses, like predicting new connections or assessing the robustness of the network under different conditions.