Doca Bench Agent Skill

DOCA Bench (doca_bench)

Where to start: This is a tool skill for invoking doca_bench,

the cross-library micro-benchmark harness. Open

TASKS.md and start at

## configure for the three-axis decision

(target library × workload shape × measurement axis), then

## run for the smoke-before-bulk flow. Open

CAPABILITIES.md when the question is *what

doca_bench can measure*, *which DOCA libraries it can drive*, or

*how to interpret throughput / latency / op-rate output without

fooling yourself on warm-up or steady-state*. If DOCA is not

installed yet, route to

doca-setup first; if the install

version is < 2.7.0, doca_bench is not shipped on this host.

Example questions this skill answers well

The CLASSES of doca_bench 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.

• "What does this DOCA library actually deliver on this device?" —

worked example: *"throughput of DOCA Compress on my BlueField-3"*.

Answered by the three-axis configuration in

CAPABILITIES.md ## Capabilities and modes

+ the smoke-before-bulk flow in

TASKS.md ## run. The *same* shape answers

*"send-side throughput of DOCA RDMA"* — doca_bench is

cross-library, not single-library.

• **"Which DOCA libraries can doca_bench actually drive on this

install?"** — worked example: *"is doca_sha enumerable on a

granular-build install"*. Answered by the built-in query system

surfaced in

CAPABILITIES.md ## Capabilities and modes

+ TASKS.md ## configure step 2

(probe-before-bench). Empty enumeration = library not installed,

not bench failure.

• "Is this number reliable, or did I miss the warm-up?" —

worked example: *"why does my first-second number differ from my

steady-state number"*. Answered by the measurement-soundness

overlay in

CAPABILITIES.md ## Error taxonomy

layer 5 + TASKS.md ## test (the eval-loop

overlay treats warm-up / steady-state / outliers as

re-iteration triggers, not one-shot facts).

• **"Bench reports zero throughput / hangs at start / disagrees

with the public docs."** — worked example: *"doca_bench shows

zero ops for AES-GCM but doca_caps says the device supports

it"*. Answered by the layered error taxonomy in

CAPABILITIES.md ## Error taxonomy

(config-syntax → device-binding → library-precondition →

workload-precondition → measurement-soundness → version →

cross-cutting) + TASKS.md ## debug.

• **"How do I capture a baseline I can later regression-test

against?"** — worked example: *"snapshot decompress throughput

on this BlueField + DOCA version before a firmware update"*.

Answered by the CSV output + version-overlay rule in

TASKS.md ## test (capture command line +

version + device + as-deployed environment alongside the

numbers; quoting numbers without the four-tuple is the

cross-version regression-hunt failure mode).

• **"doca_bench returns nothing for library X — what does that

mean?"** — worked example: *"empty output for DOCA SHA"*.

Answered by the empty-output interpretation rules in

TASKS.md ## debug +

CAPABILITIES.md ## Error taxonomy.

Re-route through

doca-caps for the coarse

per-device per-library capability ground truth, then back

into bench once the capability is confirmed present.

Audience

This skill serves **external operators, developers, and AI agents

who need a reproducible, vendor-supported way to measure DOCA

library performance on the user's actual install and device**.

Concretely:

• An external developer choosing between DOCA libraries (e.g.

COMPRESS vs SHA vs DMA throughput) before committing an

application design.

• A platform operator validating a tuning change (NUMA pinning,

driver upgrade, firmware burn) by re-running a captured

doca_bench baseline against the new state.

• An SRE / performance engineer producing a *"this is what the

device delivers today"* artifact that downstream consumers

(capacity planning, regression bisection) can cite.

• An AI agent answering *"what throughput / latency should I

expect from DOCA library X on device Y?"* honestly — with a

measured number, the command line that produced it, and the

version + device + environment that scopes it — instead of

guessing from datasheet headlines.

It is not for users debugging the doca_bench source code,

and not a substitute for the live public DOCA Bench guide on

docs.nvidia.com.

doca_bench is shipped as a tool (a single CLI binary plus a

companion app for the remote half of remote-memory / RDMA / Eth

scenarios), not a library you link against. The skill uses the

same kind: tool three-file shape as the rest of the bundle so

the agent's task-verb contract

(configure / build / modify / run / test / debug) is uniform

across libraries, services, and tools — even when individual

verbs collapse to a routing stub for a shipped binary.

When to load this skill

Load this skill when the user is — or the agent needs to — invoke

doca_bench on a real host with DOCA ≥ 2.7.0 installed (or

inside the public NGC DOCA container with the equivalent version)

to measure performance of a DOCA library. Concretely:

• Picking *which* DOCA library to benchmark for a candidate

workload (RDMA vs COMPRESS vs DMA, etc.).

• Picking *which* measurement axis to ask for (throughput vs bulk

latency vs precision latency vs max-bandwidth) — the four modes

defined in tools/bench/doca_bench/configuration.hpp are not

interchangeable.

• Probing the install's granular-build state so the agent can

honestly report *"this library is not exposed on this install"*

instead of inventing a workload.

• Capturing a documented baseline (command line + version + device

+ as-deployed environment + numbers) for later regression hunts.

• Requiring the workload owner to predeclare acceptable variance

and obtaining two consecutive runs within that tolerance before

reporting a stable result; otherwise escalating the variance.

• Diagnosing why a bench run reported zero / unstable / unexpected

results (the error-taxonomy walk in

TASKS.md ## debug).

Do not load this skill for general DOCA orientation, library

API work, or installation. For those, use

doca-public-knowledge-map,

the matching libs/<library> skill, or

doca-setup. Do not load it for

*application-level* end-to-end benchmarking either — doca_bench

measures the DOCA library surface, not the user's application

above it.

What this skill provides

This is a thin loader. Substantive material lives in two

companion files:

• CAPABILITIES.md — what doca_bench can measure (the

cross-library scope, the three-axis configuration model, the

documented operating modes, the warm-up / pipeline / multi-core

concepts that constrain measurement soundness), the version

overlay (doca-bench-specific facts on top of the canonical

doca-version rules), the layered error taxonomy

(config-syntax / device-binding / library-precondition /

workload-precondition / measurement-soundness / version /

cross-cutting), the observability surface (screen + CSV

output, real-time stats, query system), and the safety

posture (the public guide's *"not for production"* warning,

the host vs BlueField execution rule, the companion-app

attack surface).

• TASKS.md — step-by-step workflows for the in-scope task

verbs: configure (the three-axis decision + the

probe-before-bench step), build (route to install — the

binary is shipped, the companion app is shipped), modify

(refuse — do not patch the bench binary; modify the bench

*invocation* instead), run (the smoke-before-bulk flow),

test (the eval loop — warm-up, steady-state, outliers,

cross-version), debug (walk the error taxonomy layer by

layer), plus a Deferred task verbs block routing

out-of-scope questions and a Command appendix of

doca_bench-specific invocation classes.

The skill assumes a host where DOCA ≥ 2.7.0 is already installed

(or the public NGC DOCA container is running at an equivalent

version) and the operator has whatever permissions the public

guide requires for doca_bench to bind devices and allocate

resources on their platform.

What this skill deliberately does not ship

This skill is agent guidance, not a samples or scripts

bundle. To keep the boundary clean, it deliberately does not

contain — and pull requests should not add:

• **Specific flag strings or scenario / metric / attribute names

beyond what the public DOCA Bench guide documents.** The flag

surface evolves and is install-specific; the documented

invocations + --help on the installed version are the

authoritative answer. Inventing a flag is the most common

hallucination failure for this skill.

• Pre-baked example output or expected throughput numbers.

Bench output is device-, version-, firmware-, NUMA-, and

tuning-specific. A captured number pinned to one platform and

one DOCA version misleads operators on a different

platform / version.

• Wrappers, parsers, or scripts in any language that consume

doca_bench CSV or stdout. The output formats are documented;

if a user wants to script against them, the right answer is

"read the live guide, write the parser against your installed

version".

• A samples/ or reference/ subtree. This is a thin

loader for a documented CLI; substantive material lives on

the public page and in --help.

Loading order

• Read this SKILL.md first to confirm the user's question is

in scope (the user actually wants to invoke doca_bench for

measurement, not learn about a DOCA library in general).

• **For what doca_bench measures, the three-axis model, the

version overlay, the error taxonomy, observability surface,

and safety posture, see CAPABILITIES.md.**

• **For the documented invocations and the smoke-before-bulk

workflow — configure, build, modify, run, test,

debug — see TASKS.md.**

Related skills

• doca-public-knowledge-map

— routing to the public DOCA Bench page on docs.nvidia.com

and the rest of the public DOCA documentation set.

• doca-version — the canonical

version-detection chain, four-way match rule, NGC container

semantics, and headers-win-over-docs rule. The

## Version compatibility section in this skill is a thin

overlay on top of doca-version; the body lives there.

• doca-structured-tools-contract

— the bundle-wide contract for structured-output helper tools.

Bench-runner / bench-snapshot executables that satisfy the

detect-prefer-fallback-report loop are deferred to PR2; the

contract is consumed here in advance so the

## Command appendix in TASKS.md is infra-aware

from PR1.

• doca-setup — env preparation,

install verification, hugepages, NUMA awareness, and the *I

have no install yet* path with the public NGC DOCA container.

• doca-debug — the cross-cutting

debug ladder. Bench surfaces *its own* error taxonomy in

CAPABILITIES.md ## Error taxonomy;

when the cause turns out to be below DOCA (driver, firmware,

NUMA), the bench taxonomy hands off to doca-debug.

• doca-caps — the sibling DOCA tool

for the coarse per-device per-library capability snapshot.

Bench probes capability at finer grain via its own query

system; doca_caps is the cheaper first step to confirm the

device is even visible to DOCA.

• The matching libs/<library> skill — e.g.

doca-comch,

doca-compress — for

the workload-side preconditions, capability-query rules, and

error-taxonomy overlays of the library under test. Bench

drives the library; the library skill explains *what

"healthy" means for it*.