Agent skill · nvidia
nvflare-diagnose-job
Use when the user asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.
What it needs
About 3k tokens when loaded.
What this skill does
NVFLARE Diagnose Job Use When Proceed only when the request includes a reported NVFLARE job failure signal as defined in the description. Follow the evidence workflow even when the likely cause appears obvious; do not diagnose from prior knowledge alone. Do Not Use When Stop this skill path and return to normal handling when no reported NVFLARE job failure signal is present. This includes creating jobs, converting training code, submitting or monitoring healthy runs, downloading normal results from a successfully completed job, production deployment, and generic Python debugging. Workflow 1. Determine runtime mode first: simulation: user provides job.py, SimEnv output, local logs, exported job folder, or a failed python job.py run; POC/production: user provides a job ID, startup kit, POC workspace, admin context, or asks about a running FLARE system. 2. If mode or evidence is ambiguous, ask for the missing mode, job ID, local log path, simulation output path, or startup-kit context before diagnosing. 3. For simulation mode, inspect local artifacts only. Use nvflare agent inspect source <path> --format json when a project or job path is available, then read bounded local logs and generated job/config artifacts. For completed simulations, check the server workspace's simulatejob/metrics/ directory for metricssummary.json and roundmetrics.jsonl before falling back to logs for metric evidence. 4. For POC/production mode, collect bounded job and system evidence through the FLARE CLI, using --tail, --since, or --max-bytes for logs. For terminal jobs with the reported failure signal, use nvflare job download <jobid> -o <dir> --format json and read data.artifacts.globalmodel, data.artifacts.metricssummary, and data.artifacts.roundmetrics when present. This is bounded failure-evidence collection for diagnosis; do not download artifacts for a healthy, successfully completed job. 5. Match evidence against the packaged failure-pattern catalog before interpreting raw logs. 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/nvflare-diagnose-job