🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks in terms of network, shell, obfuscation, and credential handling, but the metadata risk score is elevated due to a non-existent Git repository and a new maintainer account.

Suspicious metadata risk due to missing Git repository and new maintainer account
Low individual risk scores for network, shell, obfuscation, and credential handling

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
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: Suspicious due to non-existent Git repository and new maintainer account, but no direct evidence of malice.

🔬 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 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "The AgentForge Authors" 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 agentforge-reranker-cohere

Create a Python-based mini-application that leverages the 'agentforge-reranker-cohere' package to enhance search functionality within a document management system. This application should allow users to input a query related to documents they are searching for, and then use the 'agentforge-reranker-cohere' API to rank and return the most relevant documents based on the user's query.

The application should have the following features:
1. A simple web interface where users can enter their search queries.
2. Integration with a local file storage system to index and manage documents.
3. Utilization of the 'agentforge-reranker-cohere' package to process and rank search results.
4. Display of ranked documents in a user-friendly format, including document titles, brief descriptions, and links to view/download full documents.
5. Optional feature: Implement a feedback mechanism where users can rate the relevance of returned documents, which could potentially improve future search results.

Steps to develop the application:
1. Set up a virtual environment and install necessary packages, including Flask for the web interface, and 'agentforge-reranker-cohere' for search ranking.
2. Develop a basic Flask application structure with routes for handling GET requests from the user's search query.
3. Implement the backend logic to interact with the local file storage system, indexing all available documents.
4. Integrate the 'agentforge-reranker-cohere' package into your backend logic, using it to process and rank documents based on user queries.
5. Create a frontend interface that allows users to submit search queries and displays the ranked list of documents.
6. Test the application thoroughly, ensuring that searches are accurate and relevant documents are ranked at the top.
7. Optionally, add the feedback mechanism to allow users to provide ratings on the relevance of search results.