Doca Dts

NVIDIAOfficialPublished by NVIDIA

Skill
Tool integration
Cloud and DevOps
Apache-2.0
Medium risk

From the author

Deploy and configure the NVIDIA DOCA Telemetry Service (DTS) as it is actually run: (A) BlueField (built-in static pod), (B) Host (docker) on x86_64 / aarch64, or (C) DPF (DPUService / helm chart) — plus the shared configuration layer (`dts_config.ini`, providers, exporters, level labels) that all three share. The user's DEPLOYMENT TYPE decides everything; pick it first. Trigger even without "DTS" for "prometheus on the DPU shows nothing on 9100", "telemetry container exits right after start", "add ethtool counters to the telemetry DPUService", "how do I upgrade the built-in telemetry on BlueField", or "level labels per rack/pod". Refuse and route elsewhere for installing DOCA itself, the `doca_telemetry` / `doca_telemetry_exporter` library API, installing the DPF cluster, Grafana / Prometheus server setup or dashboard / PromQL authoring, and BFB / BSP imaging — those belong to other skills or to external products.

— the description by NVIDIA

AutoGineer summary

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/download
Unzip 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 overview
- the shape-first step in TASKS.md ## configure.
"How do I enable or disable a provider / component?" — worked example: "turn on ethtool counters and drop sysfs.eth". Answered by the provider inventory in CAPABILITIES.md ## Capabilities and modes
- the enable-provider= / disable-provider= step in TASKS.md ## modify (in DPF the same option is set as key: value under configMapData, 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 modes
- the 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 compatibility
- the NGC-yaml download pattern in TASKS.md ## build, with the generic kubelet static-pod manifest mechanics in doca-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 modes
- the level_labels.ini / DPUFlavorTemplate step in TASKS.md ## modify, with the reprovision warning from CAPABILITIES.md ## Safety policy.
"The container is up but nothing is exported — which layer failed?" — worked example: "curl :9100/metrics on the DPU returns nothing, crictl ps shows the pod running". Answered by the layer split in CAPABILITIES.md ## Error taxonomy
- the 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 in CAPABILITIES.md ## Observability
- the 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, its initContainers (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 -host image with the documented namespace / device / volume flag families, the one-shot init container, and deciding whether --gpus all or the MST device mapping apply to this host.
(C) DPF (DPUService / helm chart): authoring a DPUService for the doca-telemetry chart, mapping dts_config.ini keys onto configMapData, keeping the Prometheus port consistent between configMapData and configPorts, and reading dpfctl describe conditions.
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 DPUService never reaches ApplicationsReady.

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 a Command appendix, a Deferred task verbs block, and the Cross-cutting contracts 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.ini bodies. The kubelet yaml is published per version on the NGC container page; the chart's keys come from helm 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. See doca-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. imagePullSecrets is named as a chart key family; its contents are the user's.
A samples/, templates/, or reference/ 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 — see CAPABILITIES.md ## Safety policy.

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 NGC configs/<version>/ yaml download, the crictl pods / crictl ps / crictl logs inspection) 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) that TASKS.md ## debug overlays.
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, the doca_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 pointer carries 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

BENCHMARK.md

Skill Benchmark: doca-dts

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

Skill: doca-dts
Validation status: passed
Evaluation date: 2026-10-28
Evaluator version: 1.6.2
Agents: Claude Code (aws/anthropic/bedrock-claude-opus-5-5), Codex (openai/openai/gpt-6.1-sol)
Tasks: 6 evaluation tasks (4 positive, 2 negative)
Dataset digest: sha256:1f0a5dec87b17e5f794f9aa7e1c80561854711f5e5b6fdd87c357e0f4a3b0a8c (skill-evaluator-dataset-snapshot/1)
Attempts per task: 1
Environment: k8s-sandbox
Tier 2 evidence: required for publication
Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

Tier 1 LLM Provenance

SkillSpector provider, model, analyzer revision, and determinism evidence retained for this scan.

Skill	Provider	Adapter	Analyzer	Model	Revision	Determinism
doca-dts	nv_inference	openai	mcp_tool_poisoning	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.1`	nondeterministic / provider_defaults
doca-dts	nv_inference	openai	semantic_developer_intent	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.1`	nondeterministic / provider_defaults
doca-dts	nv_inference	openai	semantic_quality_policy	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.1`	nondeterministic / provider_defaults
doca-dts	nv_inference	openai	semantic_security_discovery	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.1`	nondeterministic / provider_defaults
doca-dts	nv_inference	openai	meta_analyzer	`aws/anthropic/bedrock-claude-opus-5-5`	`2.13.1`	nondeterministic / provider_defaults

Controls for doca-dts: temperature=not set (source=provider_default, forwarded=no); seed=not set (source=unset, forwarded=no); reasoning_effort=not set (source=provider_default, forwarded=no)

What This Report Answers

The three-tier evaluation checks whether the skill:

is safe to use;
produces correct answers;
is discovered and activated when needed;
helps the agent complete the user's goal and expected workflow; and
avoids wasted skill and tool usage.

Results at a Glance

Measure	Claude Code (Baseline → Skill Uplift)	Codex (Baseline → Skill Uplift)
Overall	86.2% — baseline ran, but no comparable score was available; uplift unavailable	79.4% — baseline ran, but no comparable score was available; 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% — baseline ran, but no comparable score was available; uplift unavailable	95.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness	29.2% → 74.3% (+45.1 points)	36.1% → 75.0% (+38.9 points)
Efficiency	76.7% — baseline ran, but no comparable score was available; uplift unavailable	63.8% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.

Comparison details

Shared-source comparisons use the same signals and weights on both conditions. They do not replace the full dimension scores above or change the publication verdict.

Agent	Dimension	Basis	Baseline → With skill (uplift)	Explanation
claude-code	Discoverability	Dimension comparison	N/A	Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
claude-code	Efficiency	Token-only comparison	97.8% → 92.7% (-5.1 points)	Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.
codex	Discoverability	Dimension comparison	N/A	Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
codex	Efficiency	Token-only comparison	82.5% → 77.6% (-4.9 points)	Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

Agent	Dataset case	With skill	Without skill	Delta	Change	Coverage
claude-code	All cases	827,578	545,492	+282,086	+51.71%	skill 6/6; base 6/6
claude-code	doca-dts.explicit-positive.v1	237,474	36,629	+200,845	+548.32%	skill 1/1; base 1/1
claude-code	doca-dts.implicit-positive.v1	169,515	72,213	+97,302	+134.74%	skill 1/1; base 1/1
claude-code	doca-dts.implicit-positive.v2	171,583	108,569	+63,014	+58.04%	skill 1/1; base 1/1
claude-code	doca-dts.implicit-positive.v3	172,073	108,502	+63,571	+58.59%	skill 1/1; base 1/1
claude-code	doca-dts.negative.v1	38,211	108,819	-70,608	-64.89%	skill 1/1; base 1/1
claude-code	doca-dts.negative.v2	38,722	110,760	-72,038	-65.04%	skill 1/1; base 1/1
codex	All cases	748,842	302,612	+446,230	+147.46%	skill 6/6; base 6/6
codex	doca-dts.explicit-positive.v1	123,609	39,997	+83,612	+209.05%	skill 1/1; base 1/1
codex	doca-dts.implicit-positive.v1	107,679	26,777	+80,902	+302.13%	skill 1/1; base 1/1
codex	doca-dts.implicit-positive.v2	131,549	30,874	+100,675	+326.08%	skill 1/1; base 1/1
codex	doca-dts.implicit-positive.v3	163,618	39,234	+124,384	+317.03%	skill 1/1; base 1/1
codex	doca-dts.negative.v1	38,901	92,829	-53,928	-58.09%	skill 1/1; base 1/1
codex	doca-dts.negative.v2	183,486	72,901	+110,585	+151.69%	skill 1/1; base 1/1
ALL AGENTS	Dataset aggregate	1,576,420	848,104	+728,316	+85.88%	skill 12/12; base 12/12

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. Change is 100 * (with - without) / without; negative means fewer tokens. N/A comparisons include their reason in the coverage column; coverage is never estimated.

Tier Status

Tier	Purpose	Status	Evidence
Tier 1	Static validation	PASSED WITH OBSERVATIONS	11 validator(s); 22 finding(s)
Tier 2	Semantic deduplication	PASSED	2 validator(s); 0 finding(s)
Tier 3	Live agent evaluation	PASS	2 agent(s); 6 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.author' (skills/services/doca-dts/SKILL.md)
MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.tags' (skills/services/doca-dts/SKILL.md)
MEDIUM SCHEMA/metadata_key_style: Metadata key 'doca_version' is not kebab-case (skills/services/doca-dts/SKILL.md)
MEDIUM SCHEMA/folder_hierarchy: Unexpected nesting depth for general skill (skills/services/doca-dts)
MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/services/doca-dts/SKILL.md)
17 additional finding(s) are available in the full evaluation artifacts.

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

Dimension	Question	Scored signals
Security	Is it safe to use?	`security` (100%)
Correctness	Is the answer correct?	`accuracy` (100%)
Discoverability	Was the right skill loaded when needed?	`skill_execution` (100%)
Effectiveness	Did the skill help complete the task?	`goal_accuracy` (50%) + `behavior_check` (50%)
Efficiency	Did it avoid wasted tool calls and token usage?	`skill_efficiency` (50%) + `token_efficiency` (50%)

Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).
Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

security (Security): unsafe operations, secret leakage, and unauthorized access.
skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.
skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).
accuracy (Accuracy): final-answer correctness against the reference answer.
goal_accuracy (Goal Accuracy): whether the user's goal was achieved.
behavior_check (Behavior Check): whether the expected workflow behavior was followed.
token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

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

2026-11-08

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