Doca Container Deployment
NVIDIAOfficialCreated by NVIDIA
- Skill
- Workflow
- Cloud and DevOps
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured, cross-cutting runtime pattern for deploying DOCA services (such as Argus, DMS, Firefly, and UROM) as containers on BlueField DPU hardware. It focuses on the kubelet standalone agent workflow, where operators manage services by dropping YAML pod-spec files into a designated directory. The skill emphasizes a systematic, eight-layer error taxonomy for troubleshooting deployment failures and enforces a smoke-before-bulk safety policy to prevent uncontrolled restart loops. It acts as a loader for specific service configurations, ensuring consistent observability and lifecycle management across different DOCA services.
What it does
- Guides operators through the kubelet standalone deployment pattern for DOCA service containers.
- Provides a systematic eight-layer error taxonomy for diagnosing pod scheduling, image, and runtime issues.
- Enforces a smoke-before-bulk safety policy to ensure services are healthy before production use.
- Standardizes the inspection of pod status, ENTRYPOINT logs, and service-specific liveness signals.
- Routes per-service configuration and API-specific questions to their respective specialized skills.
- Coordinates with the BlueField OS environment to ensure proper deployment preconditions are met.
When to use it
- When deploying a packaged DOCA service container onto a BlueField DPU.
- When troubleshooting a pod that is stuck in Pending, ImagePullBackOff, or CrashLoopBackOff states.
- When verifying if a running container has successfully initialized its internal service.
- When managing service lifecycle events like restarts or configuration updates.
- When needing to determine if multiple DOCA services can coexist on a single BlueField target.
Advantages
- Provides a uniform deployment and debugging experience across all supported DOCA services.
- Reduces downtime by enforcing a structured diagnostic approach rather than trial-and-error.
- Prevents configuration drift by relying on documented patterns rather than manual ad-hoc changes.
- Ensures safety by prioritizing root-cause resolution over automated pod restarts.
- Clearly defines the boundary between general runtime operations and service-specific configurations.
Usage examples
- “How do I run a DOCA service container on my BlueField?”
- The agent identifies the target service, verifies the environment, and guides the user to drop the correct YAML pod-spec into the static-pod manifests directory.
- “My Firefly pod is in a restart loop after I edited the configuration. What should I do?”
- The agent halts the restart cycle, guides the user through the eight-layer error taxonomy to identify the specific configuration fault, and validates the fix before redeployment.
- “How can I check if the DMS service inside my container is actually ready?”
- The agent directs the user to the documented liveness signal and logs inspection procedure to confirm the service is fully operational.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/doca-container-deployment/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip doca-container-deployment.zip -d .agents/skills
Documentation by NVIDIA
DOCA container deployment
Examples
"How do I run a DOCA service container on BlueField?" — this is in scope after the setup recognize front door. Drop a documented pod spec; do not invent YAML or image tags.
"The pod spec is in the directory but the pod never starts." — walk the eight-layer taxonomy before letting kubelet restart-loop the pod.
Instructions
Follow Loading order below. Open the tasks companion for configure / run / debug. Open the capabilities companion for the kubelet-standalone contract. Do not open every companion file.
Where to start: This skill is for operating the cross-cutting DOCA container-deployment runtime — the shared pattern every DOCA service on the BlueField uses to come up (kubelet standalone agent on the BlueField Arm watching a static-pod manifests directory; the operator drops a YAML pod spec into that directory; kubelet schedules the pod and runs the container).
If the developer has NOT yet decided container vs. bare-metal
("I just got a BlueField, what now?", "my code is built, how do I
run it?", "how do I deploy this?"), route them BACK to
doca-setup/SKILL.md first.
That is the front-door routing decision. The wrong failure mode is
to silently push every developer onto the container path because the
agent loaded this skill first. ## recognize detects the system
shape, asks the minimum residual question, and lands the developer on
either this skill (when the workload is a packaged DOCA service to
drop on a BlueField) or the bare-metal-path sibling
doca-bare-metal-deployment/SKILL.md
(when the workload is a DOCA-linked application binary the developer
launches directly).
If the developer is already on the container path, open
the tasks companion and start at
## configure. If the question is what shape
of runtime is this and what does the deployment contract look like,
start at the capabilities companion. For per-service
overlays, follow the per-service skill under skills/services/ that
layers on top of this one — the supported overlays are Argus, DMS,
Firefly, and UROM service. Flow-Inspector and OS-Inspector are
outside this bundle's scope; route them through
doca-public-knowledge-map/SKILL.md
instead of applying this runtime overlay. Externally-productized
NVIDIA services (BlueMan, HBN, SNAP, Virtio-net, DOCA Telemetry
Service as productized, …) are also out of scope and route through
that map. If DOCA is
not installed on the BlueField target yet, route to
doca-setup/SKILL.md first.
Audience
This skill serves external operators and platform teams who deploy
DOCA service containers on BlueField — i.e., people who have a
BlueField with DOCA installed on the Arm side, a container runtime
plus the kubelet standalone agent already present per the BlueField
OS image, and the host-OS permissions the public DOCA Container
Deployment Guide names for the chosen service. The skill is the
shared deployment runtime; each per-service skill in the bundle
(see the list in ## Related skills) supplies
the service-specific config schema, paired-workload contract, and
"healthy" definition.
It is not for NVIDIA developers contributing to the BlueField
container runtime or to kubelet itself, and it is not a generic
Kubernetes tutorial. Kubelet runs on the BlueField in standalone
mode here — no full Kubernetes control plane, no kubectl against
a cluster API server — and the substantive answer to most
container-deployment questions on the BlueField is the public DOCA
Container Deployment Guide. This skill teaches the agent which
guide to quote, in what order to walk it, and how to map a symptom
to a layer; it does NOT re-invent kubelet flags, pod-spec field
names, or static-pod path strings. The shared deployment runtime
described here is the cross-cutting layer; the per-service skill
(doca-argus, doca-dms, doca-firefly,
doca-urom-svc) supplies the per-service
config schema, paired-workload contract, and "healthy" definition.
When to load this skill
Load this skill when the user is doing hands-on container deployment of any DOCA service on a BlueField target, or asking a cross-service deployment question that is not specific to one service's config schema. Concretely:
Dropping a YAML pod spec into the documented static-pod manifests directory on the BlueField Arm so kubelet standalone schedules the pod and runs the DOCA service container.
Inspecting pod status, container logs, and the documented liveness signal for any supported in-bundle DOCA service container — Argus, DMS, Firefly, or UROM service — so the agent answers "did the container come up, and is the service inside actually ready" the same way for every service.
Walking the smoke-before-bulk loop (pod reaches
Running; ENTRYPOINT logs are clean; service answers a trivial liveness probe) BEFORE the BlueField is put under workload.Diagnosing a deployment that is misbehaving — pod-spec YAML is in the directory but the pod never schedules; pod schedules but image-pull fails; image pulls but container ENTRYPOINT immediately exits; container runs but the service inside never answers; container is in a restart loop after a config edit; a volume mount the pod spec names is missing on the host; a network policy or host-firewall rule is blocking the service.
Cross-service questions: "can I have DMS and Firefly on the same BlueField", "how do I list every DOCA service pod that is currently running", "what is the documented stop / restart semantics if I edit a pod-spec file in place".
Do not load this skill for per-service config schema questions
(those belong to the matching per-service skill); for installing
DOCA itself or preparing the BlueField env (use
doca-setup/SKILL.md); for library-API
questions (use the matching libs/<library> skill); or for general
Kubernetes-cluster operations (this skill covers kubelet standalone
mode on the BlueField, not a full Kubernetes control plane).
What this skill provides
This is a thin loader. Substantive material lives in two companion files:
the capabilities companion — the cross-cutting DOCA container-deployment runtime contract on the BlueField (kubelet standalone agent on BlueField Arm watching a documented static-pod manifests directory; YAML pod-spec drop is the unit of operator input; the same pattern applies across every DOCA service), the BlueField preconditions (DOCA install, container runtime, BFB version, per-service firmware slot when the service emulates a device, image-pull reachability to NGC, host-OS permissions), the observability surface (kubelet status, container logs, service- side liveness signal — three layers, each with its own owner), the cross-cutting error taxonomy (pod-spec syntax → pod scheduling → image pull → runtime → volume mount → network policy → version → cross-cutting host) covering exactly eight layers, and the safety policy (smoke before bulk; failed pod is high-stakes — clear the root cause before letting kubelet restart-loop the pod; do NOT invent pod-spec field names / kubelet flags / image tags from memory).
the tasks companion — step-by-step workflows for the in-scope deployment verbs:
configure,build,modify,run,test,debug, plus aDeferred task verbsblock routing per-service config questions, host-firmware-slot work, paired-workload work, and full-Kubernetes-cluster work out to their owning skills.
The skill assumes a BlueField target where DOCA is already installed
on the Arm side, the BlueField OS image ships kubelet standalone +
the container runtime per the public DOCA Container Deployment
Guide, and the operator has the host-OS permissions that guide
names. It does not cover installing DOCA — that path goes through
doca-setup/SKILL.md — and it does not
re-document the per-service config schema, which is the canonical
concern of each DOCA service's public guide reached through
doca-public-knowledge-map/SKILL.md.
Loading order
Read this skill first to confirm the user's question is in scope (cross-cutting deployment runtime, NOT a per-service config-schema question).
For the kubelet-standalone-mode runtime shape, the static-pod manifests directory rule, the host-OS / BFB / firmware-slot / image-pull preconditions, the eight-layer error taxonomy, the observability surface, and the safety / smoke-before-bulk policy, see the capabilities companion.
For step-by-step workflows — configure, build, modify, run, test, debug — see the tasks companion.
Related skills
doca-public-knowledge-map/SKILL.md— public Container Deployment Guide, per-service guides, and NGC. Quote documented paths and tags from there; do not invent them.doca-setup/SKILL.md— env and install on the BlueField target, including the recognize front door that chooses this skill vs bare-metal.doca-bare-metal-deployment/SKILL.md— the sibling binary-launch path.doca-debug/SKILL.md— cross-cutting debug ladder this runtime's pod / image / ENTRYPOINT layers sit on.doca-version/SKILL.md— four-way match and container-tag overlay.doca-structured-tools-contract/SKILL.md— prefer structured status when present.Per-service overlays:
doca-argus/SKILL.md,doca-dms/SKILL.md,doca-firefly/SKILL.md,doca-urom-svc/SKILL.md.doca-programming-guide/SKILL.md— program-side discipline when a service surfaces a library error.
Files
- ATTRIBUTION.mdAdded by AutoGineer933 B
- BENCHMARK.md10.2 KiB
- CAPABILITIES.md29.9 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md10.8 KiB
- TASKS.md38.8 KiB
- evals/evals.json3.2 KiB
- references/details.md9.9 KiB
- skill-card.md4.8 KiB
- skill.oms.sig5.1 KiB
BENCHMARK.md
Skill Benchmark: doca-container-deployment
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
doca-container-deploymentValidation status:
passedEvaluation date: 2026-10-28
Evaluator version:
1.6.2Agents: Claude Code (
aws/anthropic/bedrock-claude-opus-5-5), Codex (openai/openai/gpt-6.1-sol)Tasks: 4 evaluation tasks (3 positive, 1 negative)
Dataset digest:
sha256:f620767bb542265dce8251dde87797f9c87f96fcfb08bea2d513dddfb5ebd0e1(skill-evaluator-dataset-snapshot/1)Attempts per task: 1
Environment:
k8s-sandboxTier 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-container-deployment | nv_inference | openai | mcp_tool_poisoning | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-container-deployment | nv_inference | openai | semantic_developer_intent | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-container-deployment | nv_inference | openai | semantic_quality_policy | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-container-deployment | nv_inference | openai | semantic_security_discovery | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-container-deployment | nv_inference | openai | meta_analyzer | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
Controls for
doca-container-deployment: 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 | 97.8% — baseline ran, but no comparable score was available; uplift unavailable | 91.2% — baseline ran, but no comparable score was available; uplift unavailable |
| Security | 100.0% → 100.0% (±0.0 points) | 100.0% → 100.0% (±0.0 points) |
| Correctness | 70.0% → 100.0% (+30.0 points) | 80.0% → 90.0% (+10.0 points) |
| Discoverability | 100.0% — baseline ran, but no comparable score was available; uplift unavailable | 90.0% — baseline ran, but no comparable score was available; uplift unavailable |
| Effectiveness | 64.6% → 100.0% (+35.4 points) | 87.5% → 100.0% (+12.5 points) |
| Efficiency | 88.8% — baseline ran, but no comparable score was available; uplift unavailable | 76.1% — 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 | 98.1% → 92.5% (-5.6 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 | 85.3% → 86.8% (+1.5 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 | 621,280 | 325,793 | +295,487 | +90.70% | skill 4/4; base 4/4 |
| claude-code | doca-container-deployment.explicit-positive.v1 | 183,608 | 36,863 | +146,745 | +398.08% | skill 1/1; base 1/1 |
| claude-code | doca-container-deployment.implicit-positive.v1 | 199,568 | 72,462 | +127,106 | +175.41% | skill 1/1; base 1/1 |
| claude-code | doca-container-deployment.implicit-positive.v2 | 201,221 | 107,960 | +93,261 | +86.38% | skill 1/1; base 1/1 |
| claude-code | doca-container-deployment.negative.v1 | 36,883 | 108,508 | -71,625 | -66.01% | skill 1/1; base 1/1 |
| codex | All cases | 359,277 | 181,522 | +177,755 | +97.92% | skill 4/4; base 4/4 |
| codex | doca-container-deployment.explicit-positive.v1 | 152,139 | 97,626 | +54,513 | +55.84% | skill 1/1; base 1/1 |
| codex | doca-container-deployment.implicit-positive.v1 | 126,700 | 39,109 | +87,591 | +223.97% | skill 1/1; base 1/1 |
| codex | doca-container-deployment.implicit-positive.v2 | 50,667 | 22,461 | +28,206 | +125.58% | skill 1/1; base 1/1 |
| codex | doca-container-deployment.negative.v1 | 29,771 | 22,326 | +7,445 | +33.35% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 980,557 | 507,315 | +473,242 | +93.28% | skill 8/8; base 8/8 |
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); 11 finding(s) |
| Tier 2 | Semantic deduplication | PASSED WITH OBSERVATIONS | 2 validator(s); 2 finding(s) |
| Tier 3 | Live agent evaluation | PASS | 2 agent(s); 4 task(s) |
Findings and Observations
<details> <summary>Show detailed findings and successful checks</summary>
CRITICAL CONTENT_DEDUP/llm_error: LLM analysis failed for a content cluster (
skills/doca-container-deployment)HIGH DUPLICATE/duplicate: Duplicate content found within TASKS.md: "## test" in TASKS.md (lines 305-309) vs "## test" in TASKS.md (lines 391-400) (
TASKS.md:305)MEDIUM QUALITY/quality_discoverability: Description uses first/second person (
skills/doca-container-deployment/SKILL.md)MEDIUM QUALITY/quality_efficiency: Deeply nested references in details.md (
skills/doca-container-deployment/SKILL.md)MEDIUM SCHEMA/metadata_key_style: Metadata key 'doca_version' is not kebab-case (
skills/doca-container-deployment/SKILL.md)8 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_efficiencywire 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: No DOCA install required to read this skill (it is an overlay loaded against any DOCA artifact skill); the validation steps within DO require a live DOCA install at /opt/mellanox/doca.
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
- Created by
- NVIDIA
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/doca-container-deployment · 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-container-deployment.zip