Diagnose MachinaMed agent errors in GKE environments from agent-trace.log.
npx skills add https://github.com/NumberOne-AI/machina-meta --skill machina-agent
Please help me install this Agent Skill. Skill: machina-agent Source: https://github.com/NumberOne-AI/machina-meta/tree/main/.claude/skills/machina-agent Command: npx skills add https://github.com/NumberOne-AI/machina-meta --skill machina-agent
This Skill helps operators diagnose and trace errors in the MachinaMed medical agent ecosystem running on GKE, enabling faster identification and remediation of failures.
High-intent search queries and answers about installing and using this skill.
To trace a MALFORMED_FUNCTION_CALL error in a GKE deployment, locate the API pod in the tusdi-dev namespace, read the agent-trace.log, and filter for the specific error to identify the root cause.
You analyze agent-trace.log files to diagnose MachinaMed agent failures by inspecting the logs to identify errors, timings, and root causes, which enables faster identification and remediation of failures.
The best way to generate an error report for MachinaMed agent incidents is to compile a comprehensive report (AGENT_TRACE_ERRORS_YYYYMMDD.md) summarizing findings and root causes from the agent-trace.log.
Yes, you can trace MachinaMed agent invocations end-to-end by following an incident from the user message through agent routing to the final response, including MALFORMED_FUNCTION_CALL incidents.
Yes, debugging MachinaMed agent errors in GKE requires locating the specific API pod in the tusdi-dev namespace and noting its name to read and filter the agent-trace.log effectively.
You need to filter agent-trace.log when reproducing failures in GKE to isolate MALFORMED_FUNCTION_CALL errors and trace timings, which is a necessary step for producing a comprehensive incident analysis report.
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