Doca Urom Svc
NVIDIAOfficialCreated by NVIDIA
- Skill
- Tool integration
- Cloud and DevOps
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The DOCA UROM Service is a DPU-side containerized service that executes remote memory operations (puts, gets, atomics, and collectives) offloaded by host applications using the doca-urom library. It operates as a paired executor, requiring strict version alignment between the host library and the DPU service. The service does not implement standalone access control; security is managed via DOCA Comch pairing and underlying RDMA permissions. This skill provides the operational framework for deploying, configuring, and debugging the service container on BlueField Arm, ensuring that HPC workloads can effectively offload communication tasks to the DPU.
What it does
- Deploys and manages the DOCA UROM Service container on BlueField Arm.
- Configures UCX components and collective exposure based on hardware capabilities.
- Establishes and validates DOCA Comch pairing between the host and the DPU service.
- Sizes enqueue queues to optimize offload performance.
- Monitors service health and RDMA transport status to diagnose offload failures.
- Enforces version compatibility between the host-side library and the DPU-side service.
When to use it
- When deploying HPC clusters that utilize doca-urom for offloading collective communication.
- When troubleshooting performance issues where offloaded collectives are slower than host-CPU baselines.
- When diagnosing communication failures or NOT_PERMITTED errors between a host and the DPU.
- When configuring the DPU-side service to expose specific collective families to the host.
- When verifying the integrity of the paired-contract between the host library and the service container.
Advantages
- Offloads compute-intensive communication tasks from host CPUs to the DPU.
- Provides a structured approach to managing paired-contract services on BlueField.
- Enables fine-grained control over UCX component exposure and queue depth.
- Ensures secure operation through Comch pairing and least-privilege RDMA access.
- Facilitates systematic debugging of complex offload-path failures.
Usage examples
- “How do I configure the DOCA UROM Service to support all-reduce collectives?”
- The agent will guide you to the configuration-axes section of the capabilities companion to identify the required UCX component flags and the corresponding config-authoring steps in the tasks companion.
- “My host application receives DOCA_ERROR_NOT_PERMITTED when calling doca_ctx_start. Is the service blocking me?”
- The agent will direct you to the layered ladder in the tasks companion to verify Comch pairing and RDMA permissions, clarifying that the error is a transport-level signal rather than a service-side authz block.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/doca-urom-svc/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip doca-urom-svc.zip -d .agents/skills
Documentation by NVIDIA
DOCA UROM Service
Scope first. This skill operates the DPU-side service container.
Writing the host-side application code that submits offloaded
operations (from an MPI or UCX job, for example) is not this skill:
route it to doca-urom/SKILL.md, the host
library, and come back here only for the service it pairs with.
Examples
"Slow UCX collectives on a BlueField pair." — this is in scope for the DPU-side service container; host app code belongs to the
doca-uromlibrary skill."Unexpected NOT_PERMITTED after start." — verify Comch pairing and least-privilege memory regions before widening exposure.
Instructions
Follow Loading order below. Open the tasks companion for configure / pair / debug. Open the capabilities companion for queues and UCX components.
Where to start: This skill is for operating the DOCA UROM
Service container on the BlueField Arm side. It is not for
linking against a library, and it is not the host-side
enqueue surface. If the user wants to deploy or run the
service container, start at the tasks companion ## configure. If the question is what
shape of service is DOCA UROM Service, what does it execute, and
how does it pair with the host-side library, start at
the capabilities companion. If DOCA is not installed
on the BlueField yet, route to
doca-setup/SKILL.md first. If the user's
real question is about writing host-side code that enqueues
remote memory operations through the paired API, the right
skill is doca-urom/SKILL.md — the
host-side library; this service is the DPU-side executor that
library offloads to.
How to use this skill. Read this file, then search the companion file
for the ## <name> heading the question needs and read that whole section.
Answer completely from it, quoting only its symbols and commands. Do not
browse docs; use the entry points in doca-public-knowledge-map. For
out-of-scope requests, name the skill that owns them and stop.
Example questions this skill answers well
The CLASSES of DOCA UROM Service 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.
"Is DOCA UROM Service the right thing to deploy on my BlueField, or do I just need the host library?" — worked example: "my MPI cluster's host nodes link against
doca-urom; what runs on the BlueField side and why must it also be there?". Answered by the publisher / executor paired-contract model in the capabilities companionthe deploy-this-when path-selection rule in the capabilities companion
the env-prep checklist in the tasks companion.
"Which library / service version pair am I supposed to run together?" — worked example: "the host fleet upgraded to a newer
doca-urom; do I have to upgrade the service containers on every BlueField, or is the pairing flexible?". Answered by the version-contract overlay in the capabilities companionthe paired-version step in the tasks companion +
doca-version/SKILL.mdas the canonical body.
"What does the service configure — UCX components, collectives, queue depths, how the host pairs over Comch?" — worked example: "my upstream stack wants to offload all-reduce collectives; how do I tell the service to expose that collective family and how does the host pair to it over DOCA Comch?". Answered by the configuration-axes table in the capabilities companion
the config-authoring step in the tasks companion.
"Host's
doca-uromcalls fail withNOT_PERMITTEDeven thoughdoca_devaccess is fine — is this the service?" — worked example: "first enqueue from host returnsDOCA_ERROR_NOT_PERMITTEDafter a cleandoca_ctx_start()". Answered by the Comch-pairing / RDMA-permissions layer in the capabilities companionthe layered ladder in the tasks companion, which surfaces "is the DOCA Comch endpoint pair correctly established and is the underlying RDMA permission stack happy" BEFORE blaming a service-side authz layer (no such layer exists in the shipped binary —
NOT_PERMITTEDhere is a Comch / RDMA signal, not a UROM-service authz signal).
"Operations enqueue but never complete — service or substrate?" — worked example: "host enqueue succeeds, the progress engine never sees the completion, what layer is hung". Answered by the service-vs-substrate split in the capabilities companion
the layered ladder in the tasks companion, which separates service queue full / handler stuck from underlying RDMA transport down before recommending a fix on either side.
"Performance with offload is worse than the host-CPU baseline — is the service the bottleneck?" — worked example: "we deployed the service, the workload runs, but collectives are slower than when the host CPU posted them itself". Answered by the offload-isn't-free rule in the capabilities companion
the smoke-before-scale step in the tasks companion, which surfaces the workload's pattern may not actually benefit from DPU offload as a legitimate diagnosis, not a service bug.
Audience
This skill serves external operators and platform teams who
deploy and operate the DOCA UROM Service container on
BlueField to receive and execute the remote memory operations
HPC / UCX / MPI workloads on the host enqueue through
doca-urom. Concretely: people running the service container
on BlueField Arm, choosing which UCX components and collectives
it exposes, sizing the enqueue queue depth, wiring the DOCA
Comch endpoint pairing between host doca-urom and the
service container (the shipped binary has NO standalone
service-side "host-endpoint authorization list" — access is
governed by Comch pairing + the underlying RDMA permissions),
and validating the host-library + DPU-service paired contract
end-to-end before scaling a real HPC workload on top.
It is not for NVIDIA developers contributing to the DOCA
UROM Service itself, nor a programming guide for building on
DOCA libraries (see doca-programming-guide/SKILL.md). DOCA
UROM Service is a service, not a library: the operator
deploys a container and configures it; there is no
lib<uromservice>.so to link. The paired host-side library
doca-urom/SKILL.md is a separate
skill with its own audience (HPC application developers, not
service operators); refuse to collapse the two.
Path selection up front. Deploy this service when the HPC
cluster's host nodes use the doca-urom library and want host
CPU freed for compute by offloading collective communication
to the BlueField, when the team is building a custom HPC stack
on top of doca-urom, or when an upstream MPI / UCX stack has
been wired to use UROM as a transport. Do not deploy this
service when the hosts are not using doca-urom, when the HPC
stack is neither MPI nor UCX (this service won't help — it
executes UROM-shaped offloads, not arbitrary networking), or
when the BlueField hardware is too constrained for the
intended offload (a cap-query at deploy time surfaces this
upfront). Deploying speculatively where host workloads will
not offload through doca-urom adds operational complexity
with no benefit.
When to load this skill
Load this skill when the user is doing hands-on DOCA UROM Service deployment work on a BlueField where DOCA is already installed. Concretely:
Deciding whether DOCA UROM Service is the right answer for the user's HPC environment (vs. keeping the host CPU on the communication path with raw
doca-rdmaor with no DPU offload at all).Deploying the service container on BlueField Arm — pulling the image per the public DOCA UROM Service Guide, setting the daemon's CLI flags / env (
SERVICE_ARGS,UROM_PLUGIN_PATH) and mounting theplugins/directory, starting / stopping the container under the BlueField container runtime per the public Container Deployment Guide.Choosing the service's configuration axes — which UCX components / collectives the service exposes (cap-bound to what the BlueField generation supports), enqueue queue depths for the offload path, and how the host's
doca-uromlibrary pairs to the service over DOCA Comch (access is governed by that Comch pairing + the underlying RDMA permissions — there is no service-side authorization list). Pair only explicitly intended hosts and keep RDMA exports and permissions to the minimum the workload requires; pre-start verification through the documented Comch and RDMA read-only surfaces is mandatory.Confirming the host-library + DPU-service version pair is one the DOCA Compatibility Policy supports — a mismatch is the canonical subtle-failure mode for the paired contract.
Reading the service container's logs, the service's observability surface, and the underlying RDMA substrate counters to confirm the service is actually executing the operations the host enqueued.
Debugging a deployment where the container is healthy but the host's
doca-uromenqueues fail or never complete, or where the offload's performance is worse than the host-CPU baseline.
Do not load this skill for general DOCA orientation,
install of DOCA itself, host-side doca-urom library API
questions, or non-UROM HPC stack topics. For those, route via
doca-public-knowledge-map/SKILL.md,
doca-setup/SKILL.md, or the matching
host-side library skill
doca-urom/SKILL.md.
What this skill provides
This is a thin loader. Substantive material lives in two companion files:
the capabilities companion — the service's architecture (long-running container on BlueField Arm that executes UROM offloads from paired hosts), the publisher / executor paired-contract model and its load-bearing version-coupling rule, the configuration axes (UCX-component / collective surface, enqueue queue sizing, DOCA Comch endpoint pairing), the deployment shape (container on BlueField Arm per the public Container Deployment Guide), the pairing surface (host
doca-uromlibrary + underlyingdoca-rdmatransport substrate), the observability surface (container state + service logs + RDMA counters), the error taxonomy (container-runtime vs service-side-resource vs transport-substrate vs paired-version-mismatch), and the safety policy (path-selection rule, version-contract rule, smoke-before-scale).the tasks companion — step-by-step workflows for the in-scope service verbs:
configure,build,modify,run,test,debug, plus aDeferred task verbsblock routing out-of-scope questions and aCommand appendixof recurring commands.
The skill assumes a BlueField where DOCA is already installed
and the operator has the privileges the public DOCA UROM
Service Guide expects to pull, run, and configure containers
on BlueField Arm. It does not cover installing DOCA — that path
goes through doca-setup/SKILL.md. It does
not cover the host-side doca-urom library API — that is
doca-urom/SKILL.md.
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:
Pre-baked DOCA UROM Service flag / env bundles (full UCX-component / collective exposure manifests, ready-to-run queue-depth
SERVICE_ARGSstrings) intended to be copy-pasted into production. Service configuration is deployment-specific (per the BlueField generation's capability cap, per the workload's collective pattern); the safe answer for an external operator is to derive the daemon's flags / env from the public DOCA UROM Service Guide against their own deployment. The agent's job is to prescribe the procedure and the configuration-axes decision, not to ship a config the user might run unmodified.Container image names, tags, or registry paths. The authoritative image source is the public DOCA UROM Service Guide reachable through
doca-public-knowledge-map ## DOCA services(doca-public-knowledge-map/SKILL.md#doca-services); the service's image tag is version-bound and changes between DOCA releases AND must match the host-side library version per the paired-contract rule. Inventing or memorizing a tag is the canonical hallucination failure mode for a service skill, and for this service in particular it can silently produce a host-library / service version mismatch.Host-side
doca-uromapplication source code, build manifests, or MPI / UCX integration glue. Those live on the host and belong todoca-urom/SKILL.md(host library API) or to upstream MPI / UCX documentation (stack-side integration). This skill names that the host side must be wired throughdoca-uromand that the version must pair with the service; the host-side bodies are out of scope.A
samples/,templates/, orreference/subtree of any kind. A mock artifact labeled "reference" reads as production-ready, and for a paired-contract service that implicitly claims a host-library version pairing the skill cannot guarantee.
Loading order
Read this skill first to confirm the user's question is in scope and that the DOCA UROM Service is the right answer at all (vs. routing entirely to the host-side library skill, or away from UROM if the workload doesn't actually benefit from offload).
For the service's deployment shape, the publisher / executor paired-contract model, the configuration axes, the host-library + RDMA-substrate pairing surface, the error taxonomy, the observability surface, the version-coupling rule, and the path-selection safety policy, see the capabilities companion.
For step-by-step workflows — configure, build, modify, run, test, debug — see the tasks companion.
Related skills
doca-urom/SKILL.md— the paired host-side library. Hosts linkdoca-uromto ENQUEUE remote memory operations; this service EXECUTES them. The two skills load together for any HPC offload deployment and they do NOT collapse into one another: the library never executes on its own; this service is never enqueued through by itself. Mismatches between the library and the service are the dominant subtle-failure mode for the paired contract.doca-rdma/SKILL.md— the underlying RDMA transport substrate this service uses to actually move bytes once a host enqueue lands on the DPU. The service does NOT replace RDMA; it sits on top of it. A failing RDMA fabric surfaces at the service as operations enqueued but never complete and at the host asDOCA_ERROR_IO_FAILED; the fix is on the substrate side, not in this service's config.doca-public-knowledge-map/SKILL.md— the routing table to the public DOCA UROM Service Guide and the rest of the public DOCA documentation set. The service URL is listed under## DOCA services(doca-public-knowledge-map/SKILL.md#doca-services).doca-setup/SKILL.md— env preparation and install verification on the BlueField where the service container will run, including the I have no install yet path via the public NGC DOCA container. This skill assumes its preconditions are satisfied on BlueField Arm.doca-version/SKILL.md— canonical DOCA version-handling rules. This service's container tag is version-bound AND its host-library pairing is version-bound; this skill's## Version compatibilitycross-links the four-way match rule and adds the host-library + DPU-service paired-version overlay that is load-bearing for UROM.doca-structured-tools-contract/SKILL.md— the bundle's structured-tools precedence rule (detect / prefer / fall back / report). The Command appendix in the tasks companion honors this contract.doca-programming-guide/SKILL.md— general DOCA patterns. The DOCA UROM Service is service-shaped not library-shaped, so the build / modify / first-app pattern there does not apply directly, but the cross-library debug discipline (env-before-program, layer-before-config) remains useful when the service reports an error that originated in the container runtime or in a DOCA library it called.doca-debug/SKILL.md— the cross-cutting debug ladder (install / version / build / link / runtime / program / driver). Service-specific debug (container not running, host-library / service version mismatch, transport substrate down, offload not actually helping) overlays on top of that ladder.doca-dms/SKILL.mdanddoca-firefly/SKILL.md— sibling service skills. The agent reading these skills should see the same service-skill shape (container on BlueField Arm, public Container Deployment Guide as the canonical recipe, env preconditions checked first, configured via the documented per-service surface — for UROM that is the daemon's CLI flags / env, not a mounted config file — smoke-before-scale) layered on top of a different per-service problem domain (DMS = device management via gNMI / gNOI; Firefly = time synchronization via PTP; UROM Service = HPC remote memory operation execution via the paireddoca-uromlibrary).
Files
- ATTRIBUTION.mdAdded by AutoGineer897 B
- BENCHMARK.md11.2 KiB
- CAPABILITIES.md30.5 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md18.2 KiB
- SKILLCARD.yaml2.7 KiB
- TASKS.md30.2 KiB
- evals/evals.json2.5 KiB
- skill-card.md5.2 KiB
- skill.oms.sig5.1 KiB
BENCHMARK.md
Skill Benchmark: doca-urom-svc
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
doca-urom-svcValidation 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:ea16cc085828d2133e3ded6b24d1d0cc8cb949802c1ddac4c03c2ddb9c1c05f9(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-urom-svc | nv_inference | openai | mcp_tool_poisoning | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-urom-svc | nv_inference | openai | semantic_developer_intent | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-urom-svc | nv_inference | openai | semantic_quality_policy | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-urom-svc | nv_inference | openai | semantic_security_discovery | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-urom-svc | nv_inference | openai | meta_analyzer | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
Controls for
doca-urom-svc: 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 | 98.4% — baseline ran, but no comparable score was available; uplift unavailable | 93.0% — 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 | 65.0% → 100.0% (+35.0 points) | 90.0% → 100.0% (+10.0 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 | 50.0% → 100.0% (+50.0 points) | 87.5% → 93.8% (+6.3 points) |
| Efficiency | 92.1% — baseline ran, but no comparable score was available; uplift unavailable | 76.0% — 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 | 98.3% → 89.8% (-8.5 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 | 84.0% → 83.8% (-0.2 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 | 676,599 | 253,595 | +423,004 | +166.80% | skill 4/4; base 4/4 |
| claude-code | doca-urom-svc.explicit-positive.v1 | 187,525 | 36,922 | +150,603 | +407.90% | skill 1/1; base 1/1 |
| claude-code | doca-urom-svc.implicit-positive.v1 | 187,659 | 73,198 | +114,461 | +156.37% | skill 1/1; base 1/1 |
| claude-code | doca-urom-svc.implicit-positive.v2 | 265,218 | 72,033 | +193,185 | +268.19% | skill 1/1; base 1/1 |
| claude-code | doca-urom-svc.negative.v1 | 36,197 | 71,442 | -35,245 | -49.33% | skill 1/1; base 1/1 |
| codex | All cases | 426,999 | 208,225 | +218,774 | +105.07% | skill 4/4; base 4/4 |
| codex | doca-urom-svc.explicit-positive.v1 | 164,601 | 75,599 | +89,002 | +117.73% | skill 1/1; base 1/1 |
| codex | doca-urom-svc.implicit-positive.v1 | 135,362 | 39,613 | +95,749 | +241.71% | skill 1/1; base 1/1 |
| codex | doca-urom-svc.implicit-positive.v2 | 96,390 | 47,845 | +48,545 | +101.46% | skill 1/1; base 1/1 |
| codex | doca-urom-svc.negative.v1 | 30,646 | 45,168 | -14,522 | -32.15% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 1,103,598 | 461,820 | +641,778 | +138.97% | 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/services/doca-urom-svc)HIGH DUPLICATE/duplicate: Duplicate content found across CAPABILITIES.md and SKILL.md and TASKS.md: "## Pattern overview" in CAPABILITIES.md (lines 23-30) vs "## Pattern overview" in CAPABILITIES.md (lines 32-49) vs "### Service shape" in CAPABILITIES.md (lines 57-98) vs "### Publisher / executor paired-contract model" in CAPABILITIES.md (lines 99-122) vs "## Deployment shape" in CAPABILITIES.md (lines 123-153) vs "### Configuration axes" in CAPABILITIES.md (lines 162-167) vs "### Pairing surface — host library plus RDMA substrate" in CAPABILITIES.md (lines 184-201) vs "## Version compatibility" in CAPABILITIES.md (lines 202-257) vs "## Error taxonomy" in CAPABILITIES.md (lines 267-272) vs "## Safety policy" in CAPABILITIES.md (lines 351-451) vs "# DOCA UROM Service" in SKILL.md (lines 1-8) vs "## Instructions" in SKILL.md (lines 18-45) vs "## Example questions this skill answers well" in SKILL.md (lines 50-111) vs "## Audience" in SKILL.md (lines 115-155) vs "## When to load this skill" in SKILL.md (lines 156-202) vs "## What this skill provides" in SKILL.md (lines 203-236) vs "## What this skill deliberately does not ship" in SKILL.md (lines 237-282) vs "## Related skills" in SKILL.md (lines 300-361) vs "## configure" in TASKS.md (lines 24-134) vs "## run" in TASKS.md (lines 280-286) vs "## debug" in TASKS.md (lines 369-441) vs "## Cross-cutting" in TASKS.md (lines 550-571) (
CAPABILITIES.md:23)MEDIUM SCHEMA/metadata_key_style: Metadata key 'doca_version' is not kebab-case (
skills/services/doca-urom-svc/SKILL.md)MEDIUM SCHEMA/folder_hierarchy: Unexpected nesting depth for general skill (
skills/services/doca-urom-svc)LOW QUALITY/quality_discoverability: Description very long (663 chars, recommend 50-150) (
skills/services/doca-urom-svc/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: BlueField-Arm-only DOCA service container; pulled from NVIDIA NGC and started under the BlueField OS container runtime. Host-side install is irrelevant — the host's relationship to this service is via the paired `doca-urom` library over a `doca-rdma` substrate. The validation steps require a live DOCA install.
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-urom-svc · 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 or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download doca-urom-svc.zip