Doca Dpa
NVIDIAOfficialCreated by NVIDIA
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The doca-dpa skill provides guidance for developers performing host-side interactions with the NVIDIA DOCA Data-Path Accelerator (DPA). It covers the lifecycle of DPA applications, including context creation, loading DPACC-compiled images, thread management, kernel launching, and completion handling. This skill is intended for host-side C/C++ applications that interface with the DPA processor on a BlueField DPU, ensuring proper configuration, capability discovery, and error handling while maintaining a clear separation from DPA-side kernel development.
What it does
- Initializes the doca_dpa core context on host-side applications.
- Manages the loading of DPACC-compiled DPA application images.
- Handles DPA thread creation and kernel launch operations.
- Provides mechanisms for draining doca_dpa_completion queues.
- Facilitates DPA capability discovery via doca_dpa_cap_* APIs.
- Assists in debugging DOCA_ERROR_* codes related to DPA host-side operations.
When to use it
- When developing host-side code to control and offload compute to the DPA.
- When troubleshooting DPA kernel hangs or completion issues from the host.
- When verifying DPA feature support for a specific BlueField generation.
- When resolving version skew between DOCA and the DPACC compiler.
- When designing host-side bindings that interact with DPA application images.
Advantages
- Provides a structured approach to host-side DPA lifecycle management.
- Clarifies the boundary between host-side control and DPA-side kernel execution.
- Offers specific guidance for debugging common DPA integration errors.
- Ensures consistent handling of DPA capability and version requirements.
Usage examples
- “Run compute on the DPA from my host.”
- The agent will guide the user through initializing the doca_dpa context, loading the DPACC-compiled image, and launching the kernel.
- “DPA kernel hangs, no completion.”
- The agent will help verify that the host is correctly driving doca_pe_progress and that the completion queue is properly attached.
- “Does this BlueField expose a DPA?”
- The agent will instruct on using the doca_dpa_cap_* discovery family to query the hardware capabilities.
- “DOCA_ERROR_DRIVER on launch.”
- The agent will assist in diagnosing the error, checking for version mismatches or invalid kernel launch parameters.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/doca-dpa/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip doca-dpa.zip -d .agents/skills
Documentation by NVIDIA
DOCA DPA
Examples
"Run compute on the DPA from my host." — this is in scope. Stay on
doca_dpa+ a DPACC-built image; do not rewrite it as host-CPU or GPUNetIO."DPA kernel hangs, no completion." — drain
doca_dpa_completionand check DOCA/DPACC version skew before changing the kernel.
Instructions
Follow Loading order below. Open the tasks companion for configure / launch / debug. Open the capabilities companion for threads vs kernels and the DPA cap query. Do not open every companion file.
Where to start: This skill assumes DOCA is already installed,
the user's BlueField has a DPA processor and the host can see it
through DOCA, and the user is doing hands-on DPA work from the
host side — i.e. using doca-dpa to load a DPA application
image, launch DPA kernels, and exchange data with the DPA
processor. Open the tasks companion if the user wants to do
something (configure / build / modify / run / test / debug); open
the capabilities companion when the question is what
can the host-side DPA API express on this version + this
BlueField generation. If the user has not installed DOCA yet,
route to doca-setup/SKILL.md first; if the
user is asking how to write the DPA-side kernel itself (the
code that runs on the DPA processor, compiled by dpacc), that
is a different scope — route via
doca-public-knowledge-map/SKILL.md
to the public DOCA DPA / DPACC / DPA-Comms / DPA-Verbs guides
(this skill does not redefine those DPA-side surfaces).
Audience
This skill serves external developers building applications
that consume the DOCA DPA library from the host side — i.e.,
users whose code calls doca_dpa_* from host C / C++ to stand
up the per-DPA-instance context, load a DPA application image
that dpacc produced from their DPA-side source, create one or
more DPA threads, launch DPA kernels with arguments, and drain
completions. It is not for NVIDIA developers contributing to
DOCA DPA itself, nor is it the place to learn how to write
the DPA-side kernel code (that path goes through the public
DOCA DPA, DPACC, DPA-Comms, and DPA-Verbs guides via
doca-public-knowledge-map/SKILL.md).
Language scope. DOCA DPA ships as a host-side C library
with pkg-config module name doca-dpa. The host-side API is
C; the DPA-side kernel is a separate translation unit written
in the language the DPACC compiler accepts and compiled by
dpacc into a binary that the host packages into the
executable as the DPA application image. The shipped samples
under /opt/mellanox/doca/samples/doca_dpa/ are written in C
plus DPA-side source (NVIDIA's choice). Other-language
consumers are limited in practice — the DPA-side kernel has no
FFI escape hatch because it must be a translation unit dpacc
accepts — but a Rust / Go / Python host-side wrapper that drives
doca_dpa_* setup and launches a DPA kernel image built
separately is still useful, and the skill keeps the lifecycle,
capability-discovery, env-precondition, and error-taxonomy
guidance language-neutral.
When to load this skill
Load this skill when the user is doing hands-on DOCA DPA work
from the host side, in any host language plus a DPA-side
translation unit built by dpacc. Concretely:
Initializing a
doca_dpaagainst adoca_devthat maps to a BlueField with a DPA processor visible to the host.Loading a DPA application image (
doca_dpa_app) thatdpaccproduced from the user's DPA-side source, into thedoca_dpacontext.Creating one or more DPA execution contexts (
doca_dpa_thread) so DPA kernels have somewhere to run on the DPA processor.Launching a DPA kernel function with arguments from the host via the
doca_dpa_kernel_launch_update_*family, and reasoning about which argument shape is supported on this install.Attaching a
doca_dpa_completionto observe when async DPA work finishes, and draining it from the host side.Checking which DPA features are supported on the active
doca_devinfovia thedoca_dpa_cap_*family — BlueField generations differ in DPA hardware support.Debugging a
DOCA_ERROR_*returned from adoca_dpa_*call — in particular disambiguating DPA not present on this BlueField from DPA feature too new for this hardware generation from DPACC-produced image mismatched against the host-side DOCA install from DPA driver layer reporting failure.Designing host-side bindings in a non-C language that drive a DPA application image they built separately with
dpacc— the env-precondition and capability-discovery rules in this skill still apply.
"My DPA kernel hangs and I never get a completion" is a host-side question first. Launch calls are asynchronous, so a successful return means submitted, not finished. Before reading a line of kernel code, establish that the host is actually in a position to observe completion: a
doca_dpa_completionis attached, and the host is drivingdoca_pe_progressin parallel with the outstanding launches. A host that submits and then blocks without progressing the PE sees no completions and concludes "the DPA is hung" incorrectly — the kernel may have finished already. Only once that is excluded is a non-terminating kernel body the suspect. Both steps and the ordering are in ## run steps 2-3.
Writing DPA-side kernel code that calls the DPA device-side comm component
libdoca_dpa_dev_comm.a(headerdoca_dpa_dev_comch_msgq.h) — for inter-DPA-thread messaging or coordination signals between DPA threads on the samedoca_dpa_app. The DPA-Comms routing rule, primitive families, capability rule (there is no per-primitive host cap-query family — host-side DPA discovery is onlydoca_dpa_cap_is_supported/doca_dpa_cap_get_max_kernel_time_alive_supported), error overlay (_AGAIN→ kernel must yield;_BAD_STATEdisambiguation from the parent's host-side_BAD_STATE), and the configure / build / modify / run / test / debug overlay live in ## comms andcomms under this same skill.
Writing DPA-side kernel code that calls the DPA device-side verbs component
libdoca_dpa_dev_verbs.a(headerdoca_dpa_dev_verbs.h) — for RDMA from inside the DPA kernel to a remote peer when the host round-trip is the measured latency bottleneck. The 4-way RDMA matrix, the host-configures-QP / DPA-uses-QP coupling rule, the capability rule (no per-verb host cap-query family exists; verb availability follows the BlueField generation + matched DOCA/DPACC install, read fromdoca_dpa_dev_verbs.hand the shipped sample), the IO_FAILED → CQE-inspection overlay, and the climb-back rule live inverbs and
verbs under this same skill.
Do not load this skill for general DOCA orientation,
install of DOCA or the DPACC compiler, the DPA-side
programming model itself (how to write a DPA kernel; the
DPA device-side comm and verbs components
(libdoca_dpa_dev_comm.a / libdoca_dpa_dev_verbs.a) that
run inside the DPA kernel), or non-DPA library questions.
For those, route through
doca-public-knowledge-map/SKILL.md
to the matching upstream guide.
What this skill provides
This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive DPA-specific material lives in two companion files:
the capabilities companion — what the host-side DPA API can express on this version + this BlueField generation: the per-DPA-instance
doca_dpacontext, the loadeddoca_dpa_appimage produced bydpacc, thedoca_dpa_threadexecution context, the host-initiated kernel launch surface (doca_dpa_kernel_launch_update_*), thedoca_dpa_completionmechanism, the capability-query surface (doca_dpa_cap_*), the DPA error taxonomy mapped onto the cross-libraryDOCA_ERROR_*set, the observability surface (host-side completions plus the public DPA developer tools surface reachable viadoca-public-knowledge-map), and the safety policy that gates env preconditions (DPA-capable BlueField, matched DOCA + DPACC versions, DPA-side image and host-side expected entry points agree).the tasks companion — step-by-step workflows for the six in-scope DPA verbs:
configure,build,modify,run,test,debug. Plus aDeferred task verbsblock that points out-of-scope questions at the right next skill.
The skill assumes a host where DOCA is already installed at
the standard location, a BlueField with a DPA processor is
physically present and visible to the host, the DPACC compiler
is installed at a version matched to the DOCA install per the
DOCA Compatibility Policy, and the user already knows how (at
least at a sketch level) to write the DPA-side kernel that
dpacc will compile. It does not cover installing DOCA or the
DPACC compiler — that path goes through
doca-setup/SKILL.md.
What this skill deliberately does not ship
This skill is agent guidance, not a samples or templates bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:
Pre-written DOCA DPA application source code or DPA-side kernel source, in any language. The verified DPA source is the shipped C + DPA-side samples at
/opt/mellanox/doca/samples/doca_dpa/. The agent's job is to route the user to those files and prescribe a minimum-diff modification on them via the universal modify-a-sample workflow indoca-programming-guide/SKILL.md, layered with the DPA-specific overrides inmodify.
Standalone build manifests (
meson.build,CMakeLists.txt, …) parked inside the skill. The agent constructs the build manifest in the user's project directory against the user's installed DOCA + DPACC compiler, wherepkg-config --modversion doca-dpaand the installeddpaccare the two sources of truth.A
samples/,bindings/, orreference/subtree of any kind. A mock or incomplete artifact in this skill's tree, even one labeled "reference", is misleading: users will read it as buildable.DPA device-side content for the comm / verbs components (
libdoca_dpa_dev_comm.a/libdoca_dpa_dev_verbs.a). These are DPA-side archives shipped as part ofdoca-dpa(NOT separate pkg-config modules): their symbols are called from inside the DPA kernel and linked into the DPA image bydpacc, not from the host. Their public guides are reachable viadoca-public-knowledge-map/SKILL.md. This skill names them and routes; it does not redefine them.
Loading order
Read this skill first to confirm the user's question is in scope (host-side DPA work, not DPA-side kernel-writing).
For the DPA capability matrix, the
doca_dpaper-instance context, the loadeddoca_dpa_appimage, thedoca_dpa_threadexecution context, the kernel-launch + completion model, the dual capability query, the env-precondition policy, the error taxonomy, the observability surface, and the safety policy, see the capabilities companion.For step-by-step workflows — configure, build, modify, run, test, debug — see the tasks companion.
Both companion files cross-link to each other,
doca-version/SKILL.md for the canonical
DOCA version-handling rules (with the DPA overlay that DOCA
must match the DPACC compiler), and
doca-public-knowledge-map/SKILL.md
whenever the right answer is "look it up in the public DOCA
DPA, DPACC, DPA-Comms, or DPA-Verbs guide, or in the on-disk
install layout" rather than "DPA host-side-specific guidance".
Related skills
doca-public-knowledge-map/SKILL.md— the routing table for every public DOCA documentation source and the on-disk layout of an installed DOCA package. The DPA public guide is at https://docs.nvidia.com/doca/sdk/DOCA-DPA/index.html; the DPACC compiler guide, the DPA-Comms guide (DPA-side communications), the DPA-Verbs guide (DPA-side verbs), and the DPA Tools umbrella (developer / admin CLIs for DPA) live in the same routing table and are companion surfaces to this skill rather than redefined by it.doca-setup/SKILL.md— env preparation, install verification, DPACC compiler install / verification, and the I have no install yet path with the public NGC DOCA container. This skill assumes its preconditions are satisfied AND that DPACC is installed at a version that matches DOCA.doca-version/SKILL.md— canonical DOCA version-handling rules. This skill's## Version compatibilitycross-links the four-way match rule and adds the DPA-specific DOCA-and-DPACC must match overlay per the DOCA Compatibility Policy.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— shared build, first-app, Core-lifecycle and cross-libraryDOCA_ERROR_*patterns; this skill layers DPA specifics on top.doca-debug/SKILL.md— the cross-cutting debug ladder; this skill's## debugadds the DPA overlay (DOCA + DPACC skew, no DPA on this BlueField, kernel hangs with no host-side completion, launch-argument / signature mismatch).
DPA device-side components
DOCA DPA's DPA device-side components — the comm archive
libdoca_dpa_dev_comm.a (communication primitives the DPA
kernel itself calls, header doca_dpa_dev_comch_msgq.h) and
the verbs archive libdoca_dpa_dev_verbs.a (ibverbs-like RDMA
verbs the DPA kernel itself calls, header
doca_dpa_dev_verbs.h) — are DPA-side archives shipped
within doca-dpa, not separate pkg-config modules, and each
has its own public guide. This skill covers them in its
## comms / ## verbs sections; for per-symbol detail, route via
doca-public-knowledge-map/SKILL.md
to the public DOCA DPA Comms and DOCA DPA Verbs guides
and to the shipped /opt/mellanox/doca/samples/doca_dpa/
samples (which include both host-side and DPA-side
translation units). Conflating them with doca-dpa is the
single most common DPA first-app design error.
Files
- ATTRIBUTION.mdAdded by AutoGineer882 B
- BENCHMARK.md10.0 KiB
- CAPABILITIES.md40.9 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md15.5 KiB
- SKILLCARD.yaml2.8 KiB
- TASKS.md45.5 KiB
- evals/evals.json3.0 KiB
- skill-card.md5.1 KiB
- skill.oms.sig5.1 KiB
BENCHMARK.md
Skill Benchmark: doca-dpa
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
doca-dpaValidation status:
passedEvaluation date: 2026-10-28
Evaluator version:
1.6.3Agents: 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:
sha256:99ac207da052504949cf6f114412975da1858f1bcea2374f3fac8c667696bb9c(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-dpa | nv_inference | openai | mcp_tool_poisoning | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-dpa | nv_inference | openai | semantic_developer_intent | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-dpa | nv_inference | openai | semantic_quality_policy | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-dpa | nv_inference | openai | semantic_security_discovery | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-dpa | nv_inference | openai | meta_analyzer | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
Controls for
doca-dpa: 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 | 94.0% — baseline ran, but no comparable score was available; uplift unavailable | 86.3% — baseline ran, but no comparable score was available; uplift unavailable |
| Security | 100.0% → 100.0% (±0.0 points) | 87.5% → 100.0% (+12.5 points) |
| Correctness | 50.0% → 100.0% (+50.0 points) | 80.0% → 85.0% (+5.0 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 | 37.5% → 87.5% (+50.0 points) | 52.1% → 87.5% (+35.4 points) |
| Efficiency | 82.6% — baseline ran, but no comparable score was available; uplift unavailable | 64.2% — 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 | 98.0% → 93.5% (-4.5 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 | 79.4% → 79.8% (+0.4 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 | 855,595 | 362,078 | +493,517 | +136.30% | skill 4/4; base 4/4 |
| claude-code | doca-dpa.explicit-positive.v1 | 258,557 | 72,144 | +186,413 | +258.39% | skill 1/1; base 1/1 |
| claude-code | doca-dpa.implicit-positive.v1 | 256,940 | 109,639 | +147,301 | +134.35% | skill 1/1; base 1/1 |
| claude-code | doca-dpa.implicit-positive.v2 | 303,250 | 72,304 | +230,946 | +319.41% | skill 1/1; base 1/1 |
| claude-code | doca-dpa.negative.v1 | 36,848 | 107,991 | -71,143 | -65.88% | skill 1/1; base 1/1 |
| codex | All cases | 431,764 | 312,263 | +119,501 | +38.27% | skill 4/4; base 4/4 |
| codex | doca-dpa.explicit-positive.v1 | 147,717 | 177,312 | -29,595 | -16.69% | skill 1/1; base 1/1 |
| codex | doca-dpa.implicit-positive.v1 | 123,642 | 48,212 | +75,430 | +156.45% | skill 1/1; base 1/1 |
| codex | doca-dpa.implicit-positive.v2 | 125,015 | 47,427 | +77,588 | +163.59% | skill 1/1; base 1/1 |
| codex | doca-dpa.negative.v1 | 35,390 | 39,312 | -3,922 | -9.98% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 1,287,359 | 674,341 | +613,018 | +90.91% | 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); 14 finding(s) |
| Tier 2 | Semantic deduplication | PASSED WITH OBSERVATIONS | 2 validator(s); 3 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/libs/doca-dpa)HIGH DUPLICATE/duplicate: Duplicate content found within CAPABILITIES.md: "## Capabilities and modes" in CAPABILITIES.md (lines 124-124) vs "## verbs" in CAPABILITIES.md (lines 341-341) (
CAPABILITIES.md:124)HIGH DUPLICATE/duplicate: Duplicate content found within CAPABILITIES.md: "## comms" in CAPABILITIES.md (lines 199-218) vs "## verbs" in CAPABILITIES.md (lines 309-323) (
CAPABILITIES.md:199)MEDIUM QUALITY/quality_discoverability: Description uses first/second person (
skills/libs/doca-dpa/SKILL.md)MEDIUM SCHEMA/metadata_key_style: Metadata key 'doca_version' is not kebab-case (
skills/libs/doca-dpa/SKILL.md)12 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: Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU whose generation exposes the DPA processor to the host, plus the matching DPACC compiler at a version listed compatible by the DOCA Compatibility Policy. Reads `pkg-config --modversion doca-dpa` and the installed `dpacc` version, and inspects /opt/mellanox/doca/{lib,include,samples/doca_dpa,applications}. 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-dpa · 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-dpa.zip