AI Analysis
Final verdict: SAFE
The package shows minimal risks across all assessed categories with no signs of malicious activities. It has low metadata risk due to its apparent newness or lesser community engagement, but there's no indication of any harmful intentions.
- No network calls or shell executions detected.
- Low risk of obfuscation and credential mishandling.
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution patterns detected, indicating the package does not execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
- Metadata: The low number of stars, forks, and the single package by the maintainer suggest the project may be new or less active, but there's no clear evidence of malicious intent.
Package Quality Overall: Low (4.4/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
1 test file(s) detected (e.g. test_aioprom.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1384 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
8 type-annotated function signatures (partial)
○ Low
Multiple Contributors
2.0
Single-author or unverifiable project
1 unique contributor(s) across 7 commits in cheesiestmaster/aiopromSingle author with few commits — possibly a personal or throwaway project
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 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "aioprom contributors" 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 aioprom
Your task is to create a simple yet functional monitoring tool using Python's 'aioprom' package. This tool will serve as a basic example of how to expose system health metrics to Prometheus, a popular open-source systems monitoring and alerting toolkit. Your application will run as an asyncio-based HTTP server, providing real-time performance data that can be scraped by Prometheus for further analysis and visualization. ### Application Requirements: 1. **Basic Server Setup**: Initialize your application as an asyncio HTTP server using 'aioprom'. Ensure it runs on a configurable port and IP address. 2. **Metrics Collection**: Implement basic metric collection for CPU usage and memory utilization. Use Python's built-in libraries such as `psutil` to gather these metrics. 3. **Prometheus Exporter**: Utilize 'aioprom' to expose these metrics via HTTP endpoints. These endpoints should be accessible by Prometheus for scraping. 4. **Configuration Management**: Allow users to configure the server's listening IP and port through command-line arguments or a configuration file. 5. **Logging**: Include logging capabilities to record any errors or critical information about the server's operation. 6. **Health Check Endpoint**: Create an endpoint that returns a simple 'OK' response if the server is running without issues, which can be used for liveness and readiness probes. 7. **Documentation**: Provide clear documentation on how to set up and run the application, including how to integrate it with Prometheus. ### Additional Features (Optional): - Implement a web interface that displays collected metrics in real-time. - Add support for additional system metrics such as disk usage or network I/O. - Integrate with Grafana for visualizing collected metrics. ### How 'aioprom' is Utilized: - 'aioprom' simplifies the process of exposing metrics to Prometheus by providing decorators and classes that can be easily integrated into your asyncio application. You'll use its `Metric` classes to define the metrics you wish to collect, and its `start_http_server` function to expose them via HTTP. - For each metric (e.g., CPU usage), create a corresponding Metric object, update it periodically with the collected values, and ensure it's exposed via the HTTP server. This project aims to give you hands-on experience with asyncio, Prometheus integration, and system monitoring in Python.