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This version passed the hardening gate. Hardening applied 4 targeted changes and preserved all other text. Classified findings before hardening: 4. Classified findings closed: 1. Classified findings remaining: 3. Unclassified scanner findings remaining: 0. Review findings closed: 5. Review findings remaining: 13.
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Expand | File | Group | Subtype | Severity | Status |
|---|---|---|---|---|---|
SKILL.md | Memory & Context Poisoning | — | High | Closed | |
SKILL.md | Tool & MCP Manipulation | MCP over-permissioning | Medium | Open | |
scripts/check_interconnect.py | Malicious Code & Command Execution | Dynamic code execution | Medium | Open | |
BENCHMARK.md | Sensitive Data & Secret Exposure | Agent snooping | Medium | Open |
4 findings
Confirm that the transport disaggregated serving depends on actually works. A deployment can pass an endpoint smoke test while disagg is silently wrong: if NIXL/UCX cannot reach the peer worker over RDMA or NVLink, KV transfer falls back to a slow or broken path. Catch that with read-only checks before trusting a disagg deployment or its benchmark numbers.
This skill is read-only. It never mutates the cluster and never prints secrets.
kubectl exec access to a worker pod in the target Dynamo deployment.recipes/<model>/<framework>/<mode>).ibstat, nvidia-smi, lsmod available in the worker pod image (missing tools are reported as skipped, not failures).dynamo-recipe-runner deploys a disagg or multi-node recipe.For diagnosing pods that are already crashing or unschedulable, use
dynamo-troubleshoot first.
Reports which NIXL/UCX/NCCL transport variables are set and flags
disagg-critical ones (e.g. UCX_TLS, UCX_NET_DEVICES, NCCL_IB_HCA) that are
absent. Missing here is only a warning — they may be baked into the image — so
confirm with the node and NIXL checks. See
references/interconnect-env-vars.md for what each variable does.
Locally on a GPU node, or inside a running worker pod:
Probes (read-only) for: InfiniBand devices and Active links, GPUDirect RDMA
(nvidia_peermem), GDRCopy, and NVLink in the GPU topology. Missing tools are
reported as skipped, not failures.
Looks for NIXL test tooling in the pod and surfaces the exact next step to run a pairwise prefill↔decode transfer test. A full cross-pod transfer test requires two scheduled GPU pods on the fabric.
| Script | Purpose | Arguments |
|---|---|---|
| Inspect NIXL/UCX/NCCL env vars on a recipe | positional recipe path |
| Probe InfiniBand, GPUDirect RDMA, GDRCopy, NVLink on a node or pod |
|
| Surface NIXL transfer-test readiness for a pod |
|
Invoke via the agentskills.io run_script() protocol:
Verify a disagg recipe's transport env shape before deploy:
After deploy, validate a worker pod's fabric:
Equivalent through the agent protocol:
Each check returns ok / warn / fail / skipped with a one-line detail,
plus a rolled-up verdict on disagg transport readiness. Report:
skipped results for missing tools (ibstat, nvidia-smi, lsmod) are inconclusive, not a pass.| Symptom | Likely cause | Next step |
|---|---|---|
| Vars baked into image or injected by operator | Run the |
| Fabric down or HCA not provisioned to the node | Contact cluster admin; verify |
| GPUDirect RDMA module not loaded | Ask cluster admin to load |
| Worker image lacks NIXL test harness | Use a NIXL-enabled image |
See BENCHMARK.md for the NVCARPS-EVAL performance report (auto-generated by the NVSkills CI pipeline).
references/interconnect-env-vars.md — NIXL/UCX/NCCL env var catalog and IB
capability checklist.scripts/check_interconnect.py for all read-only checks.Get started with chainctlchainctl skill install public/nvidia/dynamo-interconnect-check:latest
Bring Chainguard's hardening pipeline to your first-party skills — custom rulesets, automated remediation, full audit trail.
skills.cgr.dev/public/github.com/nvidia/skills/dynamo-interconnect-check:latest
Apache-2.0
BENCHMARK.md
HARDENING.md
LICENSE
SKILL.md
evals/evals.json
references/interconnect-env-vars.md
scripts/check_interconnect.py
skill-card.md
skill.oms.sig