AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity or unusual behavior. It does not engage in network calls, shell executions, or any form of code obfuscation that could indicate suspicious or harmful intent.
- No network calls detected
- No shell execution patterns
- No obfuscation or credential harvesting
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
- Shell: No shell execution patterns detected, indicating the package does not execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
Package Quality Overall: Medium (5.6/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
1 test file(s) detected (e.g. test_package.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/SuarezPM/Apohara_Context_Forge#readmeDetailed PyPI description (4585 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
✦ High
Multiple Contributors
10.0
Active multi-contributor project
5 unique contributor(s) across 100 commits in SuarezPM/Apohara_Context_ForgeActive community — 5 or more distinct 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
Email domain looks legitimate: csnat.unt.edu.ar>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository SuarezPM/Apohara_Context_Forge appears legitimate
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor 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 apohara-vllm-plugin
Create a mini-application named 'CacheCraft' that leverages the 'apohara-vllm-plugin' package to manage and optimize the performance of a multi-agent system running on an AMD Instinct MI300X GPU. CacheCraft will focus on demonstrating the package's core features such as multi-agent KV-cache coordination, JCR Safety Gate (INV-15), and RotateKV INT4 hooks. Your task is to design and implement a simple yet effective system where multiple agents can collaborate efficiently while ensuring data integrity and performance optimization. Step 1: Set up the development environment. Ensure you have Python installed along with the necessary libraries including 'apohara-vllm-plugin'. Step 2: Design your multi-agent system architecture. Define how each agent interacts with others through shared resources, specifically focusing on cache management. Step 3: Implement the JCR Safety Gate mechanism within CacheCraft to prevent race conditions and ensure safe data access among agents. Step 4: Utilize RotateKV INT4 hooks provided by 'apohara-vllm-plugin' to demonstrate efficient memory usage and data manipulation techniques specific to the AMD Instinct MI300X hardware. Suggested Features: - A user-friendly interface for monitoring agent activities and cache states. - Real-time alerts for potential safety issues detected by the JCR Safety Gate. - Performance metrics display showing improvements due to RotateKV INT4 optimizations. - Detailed documentation explaining each feature and how they contribute to overall system efficiency.
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