Automate system health checks and diagnostic reporting across Linux, macOS, and Windows.
npx skills add https://github.com/ntygod/ZhiWei --skill healthcheck-ntygod
Please help me install this Agent Skill. Skill: healthcheck Source: https://github.com/ntygod/ZhiWei/tree/main/src/main/resources/skills/healthcheck Command: npx skills add https://github.com/ntygod/ZhiWei --skill healthcheck-ntygod
Automates system health checks and diagnostic reporting, replacing laborious manual monitoring with repeatable, accurate insights.
Run the diagnose.py script to perform a full health check and generate a diagnostic report.
High-intent search queries and answers about installing and using this skill.
Automated system health checks use a single diagnostic script to gather CPU, memory, disk usage, port status, and service health across Linux, macOS, and Windows. This replaces manual monitoring by running shell commands to produce consistent, repeatable reports for quick remediation.
An automated system health diagnostic report includes CPU usage, memory utilization, disk space, port status, uptime, and service health. The script outputs both a human-readable report and machine-parseable JSON to support downstream analysis and rapid incident response.
Yes, you can use shell commands to gather CPU and memory diagnostics for pre-deployment verification. The health check script coordinates data collection across different operating systems, ensuring consistent metrics for validating system readiness before deployment.
The best way to monitor disk usage and port status for routine maintenance is running an automated diagnostic script. It collects system metrics using shell execution and outputs structured JSON, providing accurate insights without laborious manual checks.
Yes, this system monitoring approach works without external dependencies by using built-in shell_exec and file_read tools. It directly executes shell commands on the host operating system to collect metrics, requiring no additional monitoring agents.
Your system health check needs to output structured JSON for downstream analysis and integration. While human-readable reports aid quick remediation, machine-parseable JSON allows automated pipelines to ingest diagnostic data for continuous monitoring and alerting.
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