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Coreweave gpu node forensics

Skill jeremylongshore/claude-code-plugins-plus-skills/plugins/saas-packs/coreweave-pack/skills/coreweave-gpu-node-forensics

Triage a dead or degraded GPU on a CoreWeave node fast — decide reschedule vs GPU-reset vs node-reboot vs RMA from an Xid code or a pasted dmesg / nvidia-smi blob, so a bad card does not silently kill a multi-day training run. Use when a GPU throws an Xid error, a node "fell off the bus", a training run stalls or NCCL hangs on one rank, or you need to know whether to replace, reset, or just reschedule. Trigger with "xid error", "gpu fell off the bus", "coreweave gpu dead", "should I RMA this GPU", "gpu node triage".From its SKILL.md

Install
npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill coreweave-gpu-node-forensics

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SKILL.md

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CoreWeave GPU Node Forensics

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc. "NVIDIA" and "Xid" are trademarks of NVIDIA Corporation; Xid semantics are cited from NVIDIA's public documentation.

Triages a dead or degraded GPU on a CoreWeave node in seconds and returns one grounded move — reschedule, reset-gpu, reboot-node, rma, watch, or app-bug-not-hardware — from an NVIDIA Xid code or a pasted dmesg / nvidia-smi blob. The classification is deterministic: a bundled script does the mapping so the agent never guesses whether a card is dead, degraded, or fine.

Overview

A single bad GPU can kill a 64-GPU, multi-day training run — thousands of dollars and days of wall-clock gone — because one rank stalls the whole collective. The expensive mistakes are triage mistakes: RMAing a healthy card for an app bug, restarting a job onto a GPU whose memory error was uncontained, or manually uncordoning a node the lifecycle controller is trying to replace. This skill kills that ambiguity.

The decision logic is grounded in the NVIDIA Xid error catalog (https://docs.nvidia.com/deploy/xid-errors/) and CoreWeave's node-lifecycle / cordon behavior. The math-of-the-matter — which Xid means what, and how row-remapper state overrides it — lives in scripts/triage.py as a table the LLM does not get to re-litigate. Deep domain knowledge (the full code→action table, the row-remap decision, the cordon rules) loads from references/ on demand.

The headline is the Xid 94-vs-95 split. A contained memory error (94) cost you one job restart on a healthy node; an uncontained one (95) means the GPU could not isolate the fault and everything it touched is suspect. Getting that one bit wrong is the difference between a 30-second reschedule and a run that quietly trained on corrupt gradients. The script decides it; the skill never eyeballs it.

This skill is diagnostic, not destructive: it recommends the cordon / drain / reset / RMA next-step but its tools are scoped read-only (nvidia-smi -q, kubectl get, dmesg) — it never runs a reset, a reboot, or an uncordon itself.

Prerequisites

  • The failure evidence. Either an Xid number, or a pasted dmesg / nvidia-smi dump. The skill works from a paste alone — no live cluster access required — which is the common case (an operator pastes what the run's logs showed).
  • Optional live access for corroboration: kubectl context on the CoreWeave cluster (read-only is enough), and nvidia-smi on the node. If neither is available the skill still triages from the paste.
  • python3 to run the deterministic classifier (scripts/triage.py, stdlib only — no dependencies).

No secrets are handled. All commands are read-only queries.

Instructions

The pipeline is capture → classify → act. The classifier is authoritative for the verdict; references/ supplies the "why" when a case needs depth.

Step 1: Capture the evidence

If the user has not already pasted it, ask for (or read) the fault signal. The two richest sources:

dmesg -T | grep -i xid                       # the Xid line(s) with timestamps
nvidia-smi -q -d ROW_REMAPPER,ECC,PERFORMANCE # remap state, ECC counts, throttle reasons

On a CoreWeave node you can also check who owns any cordon before acting:

kubectl get node NODE -o json | jq '{unschedulable: .spec.unschedulable, taints: .spec.taints}'

Step 2: Run the deterministic triage

Feed the Xid code, or the whole blob, to the classifier. Do not classify by hand — the script owns the Xid→action mapping and the row-remap override.

# From an Xid code:
python3 "${CLAUDE_SKILL_DIR}/scripts/triage.py" --xid 95

# With row-remapper state (Xid 63/64 or a DBE):
python3 "${CLAUDE_SKILL_DIR}/scripts/triage.py" --xid 63 --pending yes
python3 "${CLAUDE_SKILL_DIR}/scripts/triage.py" --xid 48 --remap-failure yes

# From a pasted dmesg / nvidia-smi blob (file or stdin):
python3 "${CLAUDE_SKILL_DIR}/scripts/triage.py" --blob /path/to/dmesg.txt
dmesg -T | grep -i xid | python3 "${CLAUDE_SKILL_DIR}/scripts/triage.py"

The script emits a VERDICT block plus a JSON object ({classification, severity, action, why, next_command, cordon_rule?, unverified?}). Add --json for machine-readable output only.

When a blob carries several Xids, the most severe one governs the move and the rest are reported as co_occurring_xids — a co-occurring app-side Xid 43 next to a hardware Xid 79 does not soften the "reboot the node" verdict.

Step 3: Read the verdict and act on the action

Present the action and the next_command to the user in plain language. The six actions and what each means live in ${CLAUDE_SKILL_DIR}/references/xid-triage-table.md. Load it when the user wants the full table or asks about an Xid not in the summary.

  • reschedule — restart the failed rank; the node stays in service.
  • reset-gpu — drain the GPU and reset it; re-run any job that shared it.
  • reboot-node — the card is off the bus; only a bare-metal reboot returns it.
  • rma — terminal hardware fault; the card must be replaced.
  • watch — correctable / trending; monitor, do not act yet.
  • app-bug-not-hardware — the app faulted, not the GPU. Do NOT RMA.

Step 4: The 94-vs-95 headline (load-bearing — get this right)

If the Xid is 94 or 95, state the containment explicitly, because the two look almost identical in the logs and lead to opposite actions:

  • Xid 94 (CONTAINED)reschedule. The error was isolated to the app's context; the GPU and node are healthy. Restart the job. Do not reset or RMA.
  • Xid 95 (UNCONTAINED)reset-gpu. The GPU could not isolate it; every context it touched is suspect. Drain, reset, and re-run anything that shared the card — otherwise the run may continue on corrupt state.

Step 5: Row-remap (Xid 63 / 64 / 48) — routine vs terminal

For any ECC/DBE or row-remap Xid, the nvidia-smi -q -d ROW_REMAPPER fields override the base verdict. Pass them in (--pending, --remap-failure) and let the script decide. The rule:

  • Remapping Failure Occurred: Yesrma (terminal — sparing failed).
  • Pending: Yes (no failure) → reset-gpu (routine — applies on reset).

Full logic + how to read the histogram: ${CLAUDE_SKILL_DIR}/references/row-remap-decision.md.

Step 6: The cordon hard rule (never violate)

Whenever the action is a hardware move (reset-gpu, reboot-node, rma), the verdict carries a cordon_rule. Surface it verbatim:

Never manually uncordon a CoreWeave health cordon — the node-lifecycle controller owns cordon/uncordon and is driving remediation. Uncordoning re-admits the run onto known-bad hardware and races the controller.

Determine cordon provenance before touching schedulability. Full guidance: ${CLAUDE_SKILL_DIR}/references/cordon-rules.md.

Output

  • A verdict — the deterministic {classification, severity, action, why, next_command} for the governing signal, presented in plain language with the single next command an operator runs.
  • The 94-vs-95 call stated explicitly when either fires — contained → reschedule, uncontained → reset — never conflated.
  • The cordon rule attached to every hardware action, verbatim, so nobody uncordons a node the controller is replacing.
  • Co-occurring Xids listed when a blob carried several, with the most-severe one named as governing.
  • [unverified] hedges surfaced honestly — e.g. CoreWeave's exact auto-RMA thresholds and health-cordon taint strings are not publicly published, and the skill says so rather than inventing a number.

Error Handling

SituationCauseResponse
Unmapped XidXid not in the triage tableScript returns a conservative reset-gpu default, flags it [unverified], and points at the NVIDIA catalog. Do not RMA on an unmapped Xid.
Xid 94 vs 95 ambiguityBoth are "ECC error" in casual logsNever infer from prose — use the exact code. 94 = contained (reschedule), 95 = uncontained (reset).
Row-remap without an XidOnly nvidia-smi pastedScript reads Remapping Failure Occurred / Pending and decides from those alone.
App-side Xid mistaken for hardwareXid 13/31/43/45Classed app-bug-not-hardware; the fix is compute-sanitizer on the job, never an RMA.
Multiple Xids in one blobCascade (e.g. 43 then 79)Most-severe governs; others listed as co_occurring_xids.
Empty / no signalBlob has no Xid, remap, or thermal lineScript returns watch and asks for fresh dmesg + nvidia-smi -q evidence.
Thermal throttle, not a faultHW/SW Thermal Slowdown : ActiveClassed watch — healthy but hot; investigate cooling, not the card.
Tempted to uncordon a stalled nodeHealth cordon owned by the controllerRefuse. Surface the cordon rule; let remediation run.

Examples

Example 1: "Xid 94 on one rank — do I need to replace the GPU?"

VERDICT: Xid 94 — Contained ECC/memory error [severity: HIGH]
ACTION:  reschedule
WHY:     The error was CONTAINED to the faulting application's context — the GPU
         and node are healthy. Just reschedule/restart the job; no reset or RMA needed.
NEXT:    Restart or reschedule the failed rank; the node stays in service.

No replacement. The containment did its job — restart the rank and keep the node.

Example 2: "Xid 95 — same run, different node"

VERDICT: Xid 95 — Uncontained ECC/memory error [severity: CRITICAL]
ACTION:  reset-gpu
WHY:     The error was UNCONTAINED — the GPU could not isolate it, so every context
         it touched is suspect. Drain the GPU and reset it; treat all in-flight work as corrupt.
NEXT:    Cordon + drain, GPU-reset (nvidia-smi -r) or node reset; re-run any job that shared this GPU.
CORDON:  Do NOT manually uncordon a CoreWeave health cordon — the node-lifecycle
         controller owns cordon/uncordon. Let it drain and replace the node.

Opposite of Example 1 despite looking identical in the logs: drain, reset, and re-run the shared work — do not just restart.

Example 3: "GPU fell off the bus"

python3 scripts/triage.py --xid 79reboot-node. The card is unreachable on the PCIe bus and returns only after a bare-metal reboot; expect the controller to cordon and reboot — let it, and RMA only if it recurs after the reboot.

Example 4: "Xid 63 with a remap failure"

python3 scripts/triage.py --xid 63 --remap-failure yesrma. The remapper tried to swap in a spare row and physically could not — terminal. Cordon, drain, open the RMA. (The script flags that CoreWeave's exact auto-RMA threshold is [unverified].)

Example 5: "Xid 43 killed my job"

python3 scripts/triage.py --xid 43app-bug-not-hardware. Software-induced fault — the fix is in the application (run it under compute-sanitizer), not an RMA. Pulling the card would waste healthy hardware and never fix the job.

Resources

What ships with it: 7 files

61.1 KB alongside SKILL.md, 1 of them executable

scripts/

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