nvflare-diagnose-job

name: nvflare-diagnose-job description: "Diagnose failed, stalled, or suspicious NVFLARE jobs in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions." min_flare_version: "2.8.0" blast_radius: read_only skill_version: "0.1.0"

NVFLARE Diagnose Job

Use When

Use when the user asks why an NVFLARE job failed, stalled, timed out, ended with EXECUTION_EXCEPTION, lost clients, produced suspicious logs, or needs failure evidence interpreted.

Do Not Use When

Do not use for creating jobs, converting training code, submitting healthy jobs, monitoring a normal run, downloading results, production deployment, or generic Python debugging without NVFLARE job context.

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 <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 simulate_job/metrics/ directory for metrics_summary.json and round_metrics.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, use nvflare job download <job_id> -o <dir> --format json and read data.artifacts.global_model, data.artifacts.metrics_summary, and data.artifacts.round_metrics when present.
5. Match evidence against the packaged failure-pattern catalog before interpreting raw logs.
6. Report observed status, evidence quality, matched pattern, likely cause, confidence, recovery category, and concrete next action.

Requirements

• Must keep diagnosis read-only.
• Must distinguish simulation from POC/production before choosing evidence commands.
• Must use simulation server metrics artifacts when present and production nvflare job download artifacts when available, instead of inventing metric or model paths.
• Must keep log evidence bounded and report truncation or missing site logs.
• Must avoid confident root-cause claims when required site evidence is missing.
• Must not read private key contents, mutate jobs/configs/runtime state, or run unbounded scans.

Output Shape

Report:

• runtime mode and evidence sources;
• job status or local failure status;
• matched failure pattern and confidence;
• recovery category such as FIXABLE_BY_CODE, FIXABLE_BY_CONFIG, ENVIRONMENT_FAILURE, RETRYABLE, or UNKNOWN;
• source-aware evidence summary with site/process labels when available;
• next action and any missing evidence.

Load references/evidence-collection.md for mode-specific evidence collection and references/failure-patterns.md before assigning a likely failure cause.