AI Analysis
Final verdict: SUSPICIOUS
The package exhibits low risks for obfuscation and credential harvesting, however, it has shown low maintainer activity and poor metadata quality, raising concerns about its reliability and security.
- Low obfuscation risk
- Low credential risk
- Poor metadata quality and low maintainer activity
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows low maintainer activity and poor metadata quality, which raises some suspicion but does not definitively indicate malice.
Package Quality Overall: Low (3.6/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Brief PyPI description (490 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
30 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
5.0
Limited contributor diversity
1 unique contributor(s) across 29 commits in Akashmodi371/armo-aiSingle author but highly active (29 commits)
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
.AsyncClient: return httpx.AsyncClient( base_url=self.base_url, headers={mport httpx with httpx.Client(timeout=5.0) as client: client.post(
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 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with armo-sdk
Create a real-time system health monitoring tool using the 'armo-sdk' package in Python. This tool will allow users to monitor and manage resources across multiple agents in their infrastructure. The application should have the following functionalities: 1. **Agent Management**: Users should be able to add, remove, and list agents they are monitoring. 2. **Resource Monitoring**: Implement real-time monitoring of CPU usage, memory usage, disk space, and network traffic for each agent. 3. **Alerting System**: Set up customizable alerts based on thresholds for critical resource metrics (e.g., if CPU usage exceeds 80%, send an alert). 4. **Historical Data Storage**: Store historical data for at least one week, allowing users to review past performance and trends. 5. **User Interface**: Develop a simple web interface where users can view current status, set thresholds, and manage agents. 6. **Logging and Reporting**: Include logging for all actions performed via the UI and generate weekly reports summarizing the system health. **Utilization of 'armo-sdk':** Use the 'armo-sdk' package to interact with the agents, retrieve real-time data, and manage alerts. Specifically, utilize its API to fetch resource metrics, manage agent configurations, and handle notifications. Ensure you document how each function from the 'armo-sdk' is being used within your application to maintain clarity and ease of maintenance.
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