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Written by AutoGineer from the skill’s own files, not by the author
This skill provides structured guidance for deploying and managing the NVIDIA DOCA Telemetry Service (DTS). It covers three primary deployment shapes: BlueField (static pod), host-based (docker), and DPF (DPUService/helm). The skill focuses on the configuration layer, including dts_config.ini, provider and exporter settings, and level labels. It emphasizes a shape-first approach to troubleshooting and configuration, ensuring users follow the correct lifecycle for their specific environment while maintaining a strict separation from library-level API usage or infrastructure-level installation.
What it does
- Identifies the correct deployment shape (BlueField, Host, or DPF) for the telemetry service.
- Guides the configuration of dts_config.ini and helm-based DPUService parameters.
- Provides troubleshooting steps for common issues like container exit loops or empty Prometheus metrics.
- Outlines the verification contract to confirm telemetry data is successfully exported.
- Manages versioning and upgrades based on NGC container tags and chart versions.
- Defines the integration points for providers and exporters within the telemetry pipeline.
When to use it
- When you need to deploy or update the DOCA Telemetry Service in any supported environment.
- When the telemetry container fails to start or shows unexpected exit behavior.
- When you need to modify telemetry providers, exporters, or add custom level labels.
- When Prometheus endpoints on the DPU are not returning expected metrics.
- When you need to verify that configuration changes have been correctly applied and are active.
Advantages
- Ensures consistent configuration across different deployment architectures.
- Provides a clear, layered debugging approach to isolate telemetry failures.
- Maintains strict adherence to official documentation and versioning practices.
- Reduces configuration errors by enforcing a shape-first diagnostic workflow.
Usage examples
- “My BlueField DPU telemetry container is running but port 9100 shows no metrics.”
- The agent will guide you through inspecting the static pod logs using crictl, verifying the dts_config.ini provider settings, and checking the service restart status.
- “How do I add ethtool counters to my DPF-managed telemetry service?”
- The agent will instruct you to update the DPUService helm values (configMapData) and apply the changes via kubectl, avoiding manual INI edits.
- “I need to upgrade the built-in telemetry on my BlueField DPU to a newer version.”
- The agent will help you identify the correct NGC container tag and provide the steps to update the static pod manifest.
- “How do I configure per-rack level labels for my telemetry data?”
- The agent will explain the level_labels.ini or DPUFlavorTemplate configuration process and the necessary steps to apply these changes.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/doca-dts/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip doca-dts.zip -d .agents/skills
Documentation by NVIDIA
DOCA Telemetry Service (DTS)
Scope note. DTS is an externally productized DOCA service: it is
not one of the DOCA SDK libraries, and its source of
truth is the public DTS guide plus the NGC container / helm-chart pages
and the DOCA Platform (DPF) public docs. This skill covers DTS as
deployed and configured — which manifest or invocation starts it,
which dts_config.ini keys shape it, how to verify it exports. It does
not cover the library side: reading telemetry in your own program is
libs/doca-telemetry (doca-telemetry/SKILL.md), publishing
into DTS over IPC from your own program is
libs/doca-telemetry-exporter (doca-telemetry-exporter/SKILL.md).
Where to start: decide the deployment type before anything
else — it selects the manifest, the config-directory path, the restart
mechanism and the debug commands. Route through
doca-setup ## recognize (doca-setup/TASKS.md#recognize)
(uname -m, lspci -d 15b3:, bf-release) to learn whether you are
on a BlueField Arm image (shape A), a plain host (shape B), or inside a
DPF-managed cluster (shape C), then open
TASKS.md ## configure and follow that shape's
column. State the shape in the answer before the first command, even
when the question implies it ("on my BlueField" is shape A: the
built-in static pod under kubelet, inspected with crictl, not
docker). Every configure, change or debug answer ends with the
verification contract, whose green signal is two /metrics scrapes at
least two update intervals apart showing a counter that advanced
(TASKS.md ## test step 3). If the question is what
can DTS collect and export, and in which mode, start at
CAPABILITIES.md ## Capabilities and modes.
If DOCA / the BlueField OS is not installed yet, route to
doca-setup/SKILL.md first.
Host docker container exits right after start (shape B). Check the
documented host shape and copy the exact invocations from the
version-matched guide's "Host Deployment" section instead of inventing
flags: (1) a -host image tag copied from the NGC doca_telemetry tag
list, never guessed; (2) the one-shot init container
(DTS_CONFIG_DIR=host /usr/bin/telemetry-init.sh) runs and exits 0
before the long-running telemetry-run.sh container; (3) the guide's
host-namespace flags and the config, ipc_sockets, data and log
mounts under /opt/mellanox/doca/services/telemetry/; (4) the documented
run uses --rm, so docker logs finds nothing: read the host-mounted
.../telemetry/log directory or re-run once without -d.
BlueField built-in DTS (shape A) serves nothing on port 9100. Inspect
with crictl pods, crictl ps -a and crictl logs <id> (not docker);
check prometheus= and enable-provider= in
/opt/mellanox/doca/services/telemetry/config/dts_config.ini (edit the file
it links to and allow about 60 s for the respawn). Green signal: two
curl <bluefield-ip>:9100/metrics scrapes at least two update intervals
apart show a counter that advanced. Change one variable per iteration.
DPF-managed DTS (shape C). Change providers and keys in the
DPUService helm values (configMapData), confirm the keys with
helm show values, apply with kubectl apply -f, and check the service
conditions with dpfctl. Never hand-edit the INI on a DPF-managed DPU.
Example questions this skill answers well
The CLASSES of DTS questions this skill is built to answer, each with one worked example. The class is the load-bearing piece; the worked example is one instance.
"Which deployment shape am I in, and what does that change?" — worked example: "I have a BlueField with DTS already running and a separate x86 host with no DPU; do I configure them the same way?". Answered by the shape table in
CAPABILITIES.md ## Pattern overviewthe shape-first step in
TASKS.md ## configure.
"How do I enable or disable a provider / component?" — worked example: "turn on
ethtoolcounters and dropsysfs.eth". Answered by the provider inventory inCAPABILITIES.md ## Capabilities and modesthe
enable-provider=/disable-provider=step inTASKS.md ## modify(in DPF the same option is set askey: valueunderconfigMapData, not in the INI).
"Which exporter do I pick and where does the endpoint live?" — worked example: "Prometheus should scrape the DPU, but the fleet also pushes to a Fluent Bit forwarder". Answered by the pull-vs-push exporter table in
CAPABILITIES.md ## Capabilities and modesthe exporter step in
TASKS.md ## configure.
"How do I upgrade or pin the DTS version on a given shape?" — worked example: "the BlueField OS bundles an older DTS; I want the NGC one". Answered by the tag / chart-version rule in
CAPABILITIES.md ## Version compatibilitythe NGC-yaml download pattern in
TASKS.md ## build, with the generic kubelet static-pod manifest mechanics indoca-container-deployment/TASKS.md#configure.
"How do I attach rack / pod / SU labels to every metric?" — worked example: "per-rack level labels for a DPF-managed fleet". Answered by the level-labels model in
CAPABILITIES.md ## Capabilities and modesthe
level_labels.ini/DPUFlavorTemplatestep inTASKS.md ## modify, with the reprovision warning fromCAPABILITIES.md ## Safety policy.
"The container is up but nothing is exported — which layer failed?" — worked example: "
curl :9100/metricson the DPU returns nothing,crictl psshows the pod running". Answered by the layer split inCAPABILITIES.md ## Error taxonomythe layered ladder in
TASKS.md ## debug.
"How do I prove a config change actually took effect?" — worked example: "I edited
dts_config.ini; did DTS restart and pick it up?". Answered by the restart-within-60-s contract and the green signal inCAPABILITIES.md ## Observabilitythe bounded loop in
TASKS.md ## test.
When to load this skill
Load this skill for hands-on DTS deployment or configuration work:
(A) BlueField (built-in static pod): understanding the kubelet static pod under
/etc/kubelet.d/doca_telemetry_standalone.yaml, itsinitContainers(telemetry-init.sh,enable-fluent-forward.sh), disabling auto-start, or upgrading to a newer NGC image than the BlueField OS bundles.(B) Host (docker): running the NGC
-hostimage with the documented namespace / device / volume flag families, the one-shot init container, and deciding whether--gpus allor the MST device mapping apply to this host.(C) DPF (DPUService / helm chart): authoring a
DPUServicefor thedoca-telemetrychart, mappingdts_config.inikeys ontoconfigMapData, keeping the Prometheus port consistent betweenconfigMapDataandconfigPorts, and readingdpfctl describeconditions.Configuration (all shapes): sampling interval, providers and components, exporters (Prometheus pull; Fluent Bit / OpenTelemetry / Prometheus Remote Write push; storage to
/data), IPC with external applications, level labels and device labels, the HFT HTTP API.Debugging: the container exits at start or restart-loops, the Prometheus endpoint is empty, a provider shows no counters, the Fluent Bit destination receives nothing, or a DPF
DPUServicenever reachesApplicationsReady.
Do not load this skill for general DOCA orientation, installing
DOCA or the BlueField OS, installing DPF itself, library-API questions,
or building Grafana dashboards. For those, route via
doca-public-knowledge-map/SKILL.md,
doca-setup/SKILL.md, or the matching libs/
skill.
What this skill provides
This is a thin loader. Substantive material lives in two companion files:
CAPABILITIES.md— the three deployment shapes and what each fixes (manifest, config path, restart mechanism, debug tooling); the inventory-discovery procedure (generated config / rendered ConfigMap over the example tables) with the provider and exporter catalogues (pull / push / storage); the version anchor (image tag / chart version, never the host DOCA release); the layered error taxonomy (image → manifest → init / config → providers → exporter → consumer); the observability surface with its named green signal; the safety policy; and the public-source pointer.TASKS.md— step-by-step workflows for the in-scope verbs:configure,build(assembling the deployment artifact — there is nothing to compile),modify,run,test,debug, plus aCommand appendix, aDeferred task verbsblock, and theCross-cuttingcontracts every change-recommending answer ends with.
The skill assumes the operator has the root / sudo or cluster
credentials the public DTS guide expects, and a target that already
runs a BlueField OS image, a docker-capable host, or a working DPF
cluster. It does not provide any of those prerequisites.
What this skill deliberately does not ship
This skill is agent guidance, not a templates or sample-config bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:
Copy-paste manifests, helm values files, or
dts_config.inibodies. The kubelet yaml is published per version on the NGC container page; the chart's keys come fromhelm show values; the INI options come from the live guide for the running version. The agent quotes the documented flag / key families with their "why" and points at the exact guide section for the verbatim recipe.Container image tags or chart versions as facts. Tags follow the
<dts-version>-doca<doca-version>[-host]shape and are read from the NGC tag list at answer time; a memorized tag is the canonical hallucination for a service skill. Seedoca-version/SKILL.md.Grafana dashboards or Prometheus / Fluent Bit server configs. The skill names the DTS-side endpoint and the consumer-side target shape and stops there.
Credentials, image pull secrets, or cluster credential files.
imagePullSecretsis named as a chart key family; its contents are the user's.A
samples/,templates/, orreference/subtree of any kind. A mock or incomplete artifact in this skill's tree, even one labeled "reference", is misleading: operators will read it as production-ready.
Loading order
Read this file first to confirm the question is about DTS as deployed (not the library API, not DPF installation), and fix the deployment shape via
doca-setup ## recognize(doca-setup/TASKS.md#recognize).For the shape table, providers, exporters, version anchor, error taxonomy, observability and safety policy, see CAPABILITIES.md.
For step-by-step workflows — configure, build, modify, run, test, debug — see TASKS.md, reading only the column for the chosen shape.
Apply the safety overlay in every phase. privileged mode with host namespaces,
systemctl start dpe, fwctl / mst device exposure, and a DPF flavor-template change that reprovisions DPUs are the safety-relevant actions — seeCAPABILITIES.md ## Safety policy.
Related skills
doca-setup/SKILL.md— the front door:## recognize(doca-setup/TASKS.md#recognize) decides the deployment shape;## no-install(doca-setup/TASKS.md#no-install) carries the tag-selection rule this skill reuses.doca-container-deployment/SKILL.md— generic kubelet standalone / static-pod manifest mechanics on BlueField and the pod-spec do-not-invent rule; the DTS-specific facts (/etc/kubelet.d/doca_telemetry_standalone.yaml, the NGCconfigs/<version>/yaml download, thecrictl pods/crictl ps/crictl logsinspection) come from the DTS guide and the NGC container page and live in this skill. Shape A delegates only the generic mechanics there via## configure(doca-container-deployment/TASKS.md#configure) and## run(doca-container-deployment/TASKS.md#run).doca-version/SKILL.md— canonical version-handling rules; DTS overlays "the anchor is the container tag / chart version, not the host DOCA release".doca-debug/SKILL.md— the universal debug-loop contract (doca-debug/CAPABILITIES.md#universal-debug-loop-contract) and shared command appendix (doca-debug/TASKS.md#command-appendix) thatTASKS.md ## debugoverlays.doca-hardware-safety/CAPABILITIES.md#safety-policy— DTS is read-only telemetry, but its deployment touches privileged containers,dpe, firmware-facing devices and DPF reprovisioning.libs/doca-telemetry(doca-telemetry/SKILL.md) — the reader library; "consume telemetry in my program" goes there.libs/doca-telemetry-exporter(doca-telemetry-exporter/SKILL.md) — the publisher library; "my app reports but DTS sees nothing" is split: IPC socket / shared-memory mounts are this skill's, thedoca_telemetry_exporter_*calls are that skill's.doca-public-knowledge-map/SKILL.md— URL routing for the public DOCA documentation set;CAPABILITIES.md ## Public-source pointercarries the DTS guide, NGC and DPF URLs.
Files
- ATTRIBUTION.mdAdded by AutoGineer815 B
- BENCHMARK.md10.2 KiB
- CAPABILITIES.md42.1 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md15.1 KiB
- SKILLCARD.yaml3.2 KiB
- TASKS.md39.9 KiB
- evals/evals.json6.2 KiB
- skill-card.md5.3 KiB
- skill.oms.sig5.1 KiB
skill-card.md
Description: <br>
Guides deployment and configuration of the NVIDIA DOCA Telemetry Service (DTS) across its three deployment shapes (BlueField built-in static pod, host docker container, or DPF DPUService helm chart) and their shared dts_config.ini layer of providers, exporters, and level labels, routing DOCA install, telemetry library API, DPF install, Grafana/Prometheus server, and BFB/BSP imaging questions to other skills. <br>
This skill is ready for commercial/non-commercial use. <br>
Owner
NVIDIA <br>
License/Terms of Use: <br>
Apache 2.0 <br>
Use Case: <br>
Developers and operators use this skill to deploy, configure, upgrade, and debug DOCA Telemetry Service on BlueField DPUs, docker hosts, or DPF-managed clusters, including enabling providers, choosing exporters, attaching level labels, and verifying that metrics are actually exported. <br>
Deployment Geography for Use: <br>
Global <br>
Requirements / Dependencies: <br>
Requires API Key or External Credential: [Yes] <br> Credential Type(s): [Other [root/sudo access or Kubernetes cluster credentials]] <br>
Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>
Known Risks and Mitigations: <br>
Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>
Reference(s): <br>
CAPABILITIES.md <br>
TASKS.md <br>
Skill Output: <br>
Output Type(s): [Analysis, Shell commands, Configuration instructions] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>
Evaluation Agents Used: <br>
Claude Code (
aws/anthropic/bedrock-claude-opus-5-5) <br>Codex (
openai/openai/gpt-6.1-sol) <br>
Evaluation Tasks: <br>
6 evaluation tasks (4 positive, 2 negative), one attempt per task, each in an isolated k8s-sandbox pod. <br>
Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
Security: Whether the skill is safe to use, checking for unsafe operations, secret leakage, and unauthorized access. <br>
Correctness: Whether the final answer is correct against the reference answer. <br>
Discoverability: Whether the expected skill was loaded when needed and decoys were avoided. <br>
Effectiveness: Whether the skill helped complete the task, as the equal-weight mean of goal completion and expected-workflow adherence. <br>
Efficiency: Whether the skill avoided wasted tool calls and token usage, as the equal-weight mean of tool-call productivity and token efficiency. <br>
Underlying evaluation signals used in this run: <br>
security: Unsafe operations, secret leakage, and unauthorized access. <br>accuracy: Final-answer correctness against the reference answer. <br>skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>goal_accuracy: Whether the user's goal was achieved. <br>behavior_check: Whether the expected workflow behavior was followed. <br>skill_efficiency: Tool-call productivity. <br>token_efficiency: Actual uncached prompt plus completion token usage. <br>
Evaluation Results: <br>
| Measure | Claude Code (Baseline -> Skill Uplift) | Codex (Baseline -> Skill Uplift) |
|---|---|---|
| Overall | 86.2% (uplift unavailable) | 79.4% (uplift unavailable) |
| Security | 100.0% -> 100.0% (±0.0 points) | 100.0% -> 83.3% (-16.7 points) |
| Correctness | 60.0% -> 80.0% (+20.0 points) | 76.7% -> 80.0% (+3.3 points) |
| Discoverability | 100.0% (uplift unavailable) | 95.0% (uplift unavailable) |
| Effectiveness | 29.2% -> 74.3% (+45.1 points) | 36.1% -> 75.0% (+38.9 points) |
| Efficiency | 76.7% (uplift unavailable) | 63.8% (uplift unavailable) |
Skill Version(s): <br>
c98e456 (source: git SHA, committed 2026-10-28) <br>
Ethical Considerations: <br>
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>
(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
The author says: Container-shaped DOCA service. On BlueField Arm it ships inside the BlueField OS image (upgradable from the NGC image); on an x86_64 or aarch64 host it runs from the NGC `-host` image under docker; under DPF it is a `DPUService` rendered from the NGC `doca-telemetry` helm chart. The version anchor is the container tag / chart version, not the host DOCA install. Never fabricate a tag or chart version — read it from the NGC page or `helm show values`.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- NVIDIA
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/doca-dts · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download doca-dts.zip