Track solver progress and timing across Python and MATLAB optimization workflows.
npx skills add https://github.com/Best6668/AMIS --skill monitor-solver
Please help me install this Agent Skill. Skill: monitor-solver Source: https://github.com/Best6668/AMIS/tree/main/skills/monitor-solver Command: npx skills add https://github.com/Best6668/AMIS --skill monitor-solver
This Skill provides real-time visibility into long-running solver processes by checking statuses, aggregating logs, and estimating remaining time, helping teams manage expectations and resources.
Run the monitor-solver with the solver-name or log-file to begin tracking progress.
High-intent search queries and answers about installing and using this skill.
To track solver progress and estimate remaining time, parse existing logs and result files to extract key metrics. This provides real-time status checks and ETA calculations for iterative solver runs.
Yes, you can monitor hybrid Python and MATLAB optimization pipelines. The system detects running, finished, or failed states across both environments to provide ongoing visibility into your workflow.
The best way to aggregate logs for solver diagnosis is to collect and surface recent logs, errors, and performance metrics automatically. This enables quick diagnosis of issues during long-running optimization processes.
To parse JSON and CSV result files for solver metrics, the system automatically extracts progress indicators and key metrics from these formats. This generates concise status updates for stakeholders.
Yes, solver monitoring works without installing additional dependencies. You can begin tracking progress by running the monitor with a solver name or log file to extract metrics.
You need progress reporting for iterative solver runs to manage stakeholder expectations and allocate resources. It generates concise status updates and ETA estimates to guide decisions on proceeding to the next pipeline stage.
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