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Doca Common

NVIDIAOfficialCreated by NVIDIA

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk

From the author

Use this skill whenever the user is doing hands-on DOCA programming on a BlueField DPU or ConnectX NIC and needs the foundation primitives every per-library context rests on — walking the doca_ctx lifecycle, discovering doca_dev / doca_devinfo and gating on doca_*_cap_* before trusting a feature, wiring doca_mmap / doca_buf_inventory / doca_buf for zero-copy I/O across libraries, driving doca_pe for completions, or DOCA Log's two-tier (--sdk-log-level vs app-side) model. Trigger even when the user does not say "DOCA Common" — typical implicit phrasings include "my tasks submit but nothing completes", "DOCA_ERROR_BAD_STATE from doca_ctx_start", "--sdk-log-level does nothing for my DOCA_LOG_DBG lines", "share a buf between doca_dma and doca_rdma", or "crashes far from the offending line". Refuse and route elsewhere for per-library questions in isolation (load doca-flow / doca-rdma / doca-eth alongside), installing DOCA (doca-setup), or doc lookup (doca-public-knowledge-map).
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The doca-common skill serves as the essential foundation for all DOCA-based development. It covers the core primitives required before interacting with higher-level libraries like Flow, RDMA, or Ethernet. This includes managing the doca_ctx lifecycle, discovering hardware via doca_dev and doca_devinfo, implementing zero-copy I/O with doca_buf and doca_mmap, driving the progress engine (doca_pe), and configuring the two-tier logging system. It is designed to be loaded alongside per-library skills to provide the underlying context and structural requirements for any DOCA application.

What it does

  • Guides the initialization and lifecycle management of doca_ctx and doca_dev.
  • Explains the wiring of doca_buf, doca_mmap, and doca_buf_inventory for zero-copy I/O.
  • Provides patterns for driving the doca_pe progress engine to ensure task completion.
  • Clarifies the two-tier DOCA logging model for effective application debugging.
  • Assists in capability discovery using doca_devinfo and doca_*_cap_* functions.
  • Helps interpret common DOCA_ERROR_* codes encountered during application startup.

When to use it

  • When setting up the initial skeleton for a new DOCA application.
  • When debugging task submission issues where tasks are submitted but never complete.
  • When needing to share buffers across different DOCA libraries.
  • When troubleshooting initialization errors like DOCA_ERROR_BAD_STATE.
  • When configuring logging levels to capture application-specific debug output.
  • When verifying hardware capabilities before executing library-specific features.

Advantages

  • Ensures a consistent and correct foundation for all DOCA library interactions.
  • Reduces debugging time by addressing lifecycle and primitive-level issues first.
  • Provides a unified approach to zero-copy buffer management across libraries.
  • Standardizes the integration of the progress engine and logging across projects.

Usage examples

“My DOCA RDMA tasks are submitted but nothing completes. What am I missing?”
The agent will guide you through the doca_pe (progress engine) connection and the required run-loop pattern to ensure tasks are drained correctly.
“I am getting a DOCA_ERROR_BAD_STATE from doca_ctx_start. How do I fix this?”
The agent will help you verify the lifecycle order of your context initialization and ensure all required dependencies are configured before starting.
“How do I share a buffer between doca_dma and doca_rdma?”
The agent will explain the wiring of doca_mmap and doca_buf_inventory to create a shared buffer surface that both libraries can reference.
“My DOCA_LOG_DBG lines are not appearing even with --sdk-log-level 60.”
The agent will explain the two-tier logging model and how to correctly register your application-side log levels.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/doca-common/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip doca-common.zip -d .agents/skills

Documentation by NVIDIA

DOCA Common

Examples

  • "Tasks submit but nothing completes." — start at the progress engine and ctx lifecycle, not a per-library rewrite.

  • "DOCA_ERROR_BAD_STATE from doca_ctx_start." — walk the Common lifecycle order before blaming Flow or RDMA.

Instructions

Follow Loading order below. Open the tasks companion for configure / build / run. Open the capabilities companion for ctx / buf / pe / log. Do not open every companion file.

Where to start: This skill is the foundation every DOCA app loads first — before doca-flow, doca-rdma, doca-eth, doca-comch, or any other higher-level library. Every doca_<library>_* context is built on top of doca_ctx, every device handle is a doca_dev discovered through doca_devinfo, every zero-copy buffer is a doca_buf from a doca_buf_inventory over a doca_mmap, every task completion drains through a doca_pe, and every log line emits through doca_log. Open the capabilities companion when the question is what does Common express on this install; open the tasks companion when the user wants to do something (configure / build / modify / run / test / debug). If the user has not installed DOCA yet, route to doca-setup/SKILL.md first. If the user is already past the foundation and asking a library-specific question (e.g. "how do I program a Flow pipe"), load the matching per-library skill alongside this one — they cross-link back here for the shared primitives.

Example questions this skill answers well

The CLASSES of doca-common questions this skill is built to answer, each with one worked example. The agent should treat the class as the load-bearing piece — the worked example is a single instance.

  • "What is the doca-common foundation I have to set up BEFORE I open a doca-flow / doca-rdma / doca-eth / … context?" — worked example: "I'm starting a brand-new DOCA Flow program on BlueField-3; what's the doca-common skeleton I need before I open the Flow port?". Answered by the universal foundation walk in

    configure +

    ctx +

    dev +

    progress engine.

  • "How do I discover a device and gate on its capabilities before trusting the public docs?" — worked example: "I want to use doca_eth_txq but the docs hint at a feature only on certain firmware bands". Answered by the capability-discovery rule (doca_devinfo_create_list → doca_*_cap_* against the active doca_devinfo is the runtime authority) in

    dev +

    use.

  • "What's the doca_buf / doca_mmap / doca_buf_inventory wiring for zero-copy I/O, and what's the lifecycle order?" — worked example: "I want to register a user-space buffer with my device, carve it into N data-plane buffers, and reference-count them across multiple DOCA libraries". Answered by the zero-copy buffer model in

    buf +

    the buffer-lifecycle walk in

    configure +

    use.

  • "How does the progress engine work and where do I have to call it?" — worked example: "my doca_rdma task submits cleanly but nothing completes — what loop am I missing?". Answered by the PE surface in

    progress engine

    • the run-loop pattern in ## run.

  • "Why don't my DOCA log lines appear at the level I expect, and what's the difference between --sdk-log-level and the app-side setter?" — worked example: "I set --sdk-log-level 60 (DEBUG), my own DOCA_LOG_DBG lines still don't print". Answered by the two-tier log model in ## log

    • the tier-flip iteration in ## log.

  • "What does this DOCA_ERROR_* from a doca_buf_* / doca_ctx_* / doca_dev_* / doca_pe_* / doca_log_* call mean?" — worked example: "DOCA_ERROR_BAD_STATE from doca_ctx_start". Answered by the Common overlay on the cross-library DOCA_ERROR_* taxonomy in

    Error taxonomy

    • the layered ladder in

    debug that escalates to

    doca-debug/SKILL.md.

Audience

This skill serves every external developer building applications that consume any DOCA library — i.e., users whose code calls any doca_* symbol (directly in C/C++, or through FFI/bindings from another language). Whether the user's primary library is doca-flow, doca-rdma, doca-eth, doca-comch, doca-dma, doca-rmax, doca-sha, doca-aes-gcm, doca-erasure-coding, or any other, the doca-common surface is under it and the user will hit doca_buf, doca_ctx, doca_dev, doca_pe, and doca_log as part of the first-app journey. It is not for NVIDIA developers contributing to DOCA Common itself.

Language scope

DOCA Common ships as a C library with pkg-config module name doca-common. The shipped samples that demonstrate Common primitives live inside every per-library samples tree (any /opt/mellanox/doca/samples/<library>/<sample>/*_main.c is a worked example of the universal foundation — doca_devinfo_create_list → doca_dev_open → per-library doca_ctx create → doca_pe_create → doca_pe_connect_ctx → doca_ctx_start → submit work → drive doca_pe_progress → drain completions → doca_ctx_stop → destroy). C and C++ consumers are the canonical case and the worked examples in the tasks companion assume that path. Other-language consumers (Rust, Go, Python, …) consume the same *.so through FFI or language-specific bindings; the skill's contribution in that case is to keep the universal foundation walk, the lifecycle, the capability-discovery rule, the PE-drives-completion rule, and the two-tier log model language-neutral, and to route the agent to the public C ABI as the authoritative surface that any wrapper will eventually call.

When to load this skill

Load this skill whenever the user is doing any hands-on DOCA work — it is the foundation. Concretely:

  • Setting up the universal DOCA-side skeleton before opening any per-library context (Flow, RDMA, Eth, Comch, DMA, Rmax, …).

  • Discovering devices and representors and gating capability use on the active doca_devinfo via the doca_*_cap_* family.

  • Wiring doca_mmap + doca_buf_inventory + doca_buf for zero-copy I/O that crosses libraries (e.g. doca-eth feeds doca-dma feeds doca-rdma — they share the same buf surface).

  • Driving the progress engine (doca_pe_create / doca_pe_connect_ctx / doca_pe_progress) — the universal task-completion drain every DOCA Core context relies on.

  • Wiring DOCA Log into a fresh app or modifying a shipped sample to add the user's own per-component log lines via the two-tier (--sdk-log-level vs app-side registry) model.

  • Debugging a DOCA_ERROR_* returned from any doca_buf_* / doca_ctx_* / doca_dev_* / doca_pe_* / doca_log_* call — the Common surface is where most lifecycle / capability / permission errors first surface for higher-level libraries.

  • Designing or extending non-C bindings (Rust, Go, Python, …) that wrap any DOCA library — for the universal foundation surface (buf, ctx, dev, pe, log) the wrapper has to expose first.

Do not load this skill for library-specific Flow, RDMA, Eth, Comch, DMA, Rmax, … questions in isolation — load the matching per-library skill alongside this one. Do not load this skill for general DOCA orientation, install of DOCA itself, or "where do I find docs". For those, use doca-public-knowledge-map/SKILL.md or doca-setup/SKILL.md.

What this skill provides

This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive Common material lives in two companion files:

  • the capabilities companion — what doca-common expresses on this install: the ## Capabilities and modes overview of the universal primitives every DOCA application touches; the five subsystem H2s (## log, ## buf, ## ctx, ## dev, ## progress engine) that own the per-primitive surface; the ## Version compatibility doca-common-specific overlay; the ## Error taxonomy Common-side view of the universal DOCA_ERROR_* set; the ## Observability surface (logs, PE events, capability snapshots); and the ## Safety policy overlay on the bundle-wide hardware-safety meta-policy.

  • the tasks companion — step-by-step workflows for the universal verbs (configure, build, modify, run, test, debug, use) PLUS a ## log verb-side that covers the two-tier log model in workflow form. Plus a Deferred task verbs block that points install / deploy / rollback questions at the right next skill.

The skill assumes a host or BlueField where DOCA is already installed at the standard location and the user has the privileges their public install profile expects. It does not cover installing DOCA — 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 application source code, in any language. The verified Common usage shows up inside every shipped DOCA sample at /opt/mellanox/doca/samples/<library>/<sample>/*_main.c and inside every shipped reference application. 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 in doca-programming-guide/SKILL.md.

  • Standalone build manifests (meson.build, CMakeLists.txt, Cargo.toml, setup.py, go.mod, …) parked inside the skill. The agent constructs the build manifest in the user's project directory against the user's installed DOCA, where pkg-config --modversion doca-common is the source of truth.

  • A samples/, bindings/, or reference/ 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.

Loading order

  1. Read this skill first to confirm the user's question is in scope.

  2. For the universal primitives (log / buf / ctx / dev / progress engine), the pkg-config --modversion doca-common anchor, the Common error overlay, observability, and safety policy, see the capabilities companion.

  3. For step-by-step workflows — configure, build, modify, run, test, debug, use, log — see the tasks companion.

Both companion files cross-link to each other, doca-version/SKILL.md for the canonical version-handling rules, doca-programming-guide/SKILL.md for the universal modify-a-shipped-sample workflow and the cross-library DOCA_ERROR_* taxonomy, doca-debug/SKILL.md for the cross-cutting debug ladder, and doca-public-knowledge-map/SKILL.md whenever the right answer is "look it up in the public docs or the installed package layout" rather than "Common-specific guidance".

  • 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. Always available alongside this skill; this skill expects to be able to defer documentation-finding and install-layout questions there instead of duplicating them.

  • doca-setup/SKILL.md — env preparation, install verification, and the I have no install yet path with the public NGC DOCA container (nvcr.io/nvidia/doca/doca) as the universal Stage-1 fallback. This skill assumes its preconditions are satisfied.

  • doca-version/SKILL.md — canonical DOCA version-handling rules (four-way match, NGC semantics, headers-win-over-docs). pkg-config --modversion doca-common is the build-time anchor every DOCA install carries; this skill's ## Version compatibility overlays the Common-specific notes on top.

  • doca-programming-guide/SKILL.md — general DOCA programming patterns shared by every library: the canonical pkg-config + meson build pattern, the universal modify-a-shipped-sample first-app workflow, the universal lifecycle, the cross-library DOCA_ERROR_* taxonomy, and the program-side debug order. This skill is the primitives layer the programming-guide patterns rest on.

  • doca-debug/SKILL.md — the cross-cutting debug ladder (install / version / build / link / runtime / program / driver) and the verbosity-escalation surface. DOCA Log is the foundation doca-debug builds its runtime-debug story on; this skill is where the two-tier log model and the universal lifecycle errors first surface, and doca-debug cross-links here for both.

  • 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-hardware-safety/SKILL.md — the cross-cutting hardware-safety meta-policy this skill's ## Safety policy overlays.

  • Per-library skills (doca-flow, doca-rdma, doca-eth, doca-comch, doca-dma, doca-rmax, doca-sha, doca-aes-gcm, doca-erasure-coding, doca-compress, doca-dpa, doca-gpunetio, doca-pcc, doca-sta, doca-telemetry, doca-urom, doca-verbs, doca-argp, doca-devemu, doca-dpdk-bridge, …) — every per-library skill cross-links back to this skill for the foundation primitives. Loading this skill alongside any of them is the recommended default.

Files

BENCHMARK.md

Skill Benchmark: doca-common

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

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

Evaluation Metadata

  • Skill: doca-common

  • Validation status: passed

  • Evaluation date: 2026-10-28

  • Evaluator version: 1.6.2

  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-5-5), Codex (openai/openai/gpt-6.1-sol)

  • Tasks: 4 evaluation tasks (3 positive, 1 negative)

  • Dataset digest: sha256:9e22539842a371b035b6820995478cb465ac90fc3e65d8d974e6798810076bb7 (skill-evaluator-dataset-snapshot/1)

  • Attempts per task: 1

  • Environment: k8s-sandbox

  • Tier 2 evidence: required for publication

  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

Tier 1 LLM Provenance

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

SkillProviderAdapterAnalyzerModelRevisionDeterminism
doca-commonnv_inferenceopenaimcp_tool_poisoningaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-commonnv_inferenceopenaisemantic_developer_intentaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-commonnv_inferenceopenaisemantic_quality_policyaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-commonnv_inferenceopenaisemantic_security_discoveryaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-commonnv_inferenceopenaimeta_analyzeraws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
  • Controls for doca-common: 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

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall93.8% — baseline ran, but no comparable score was available; uplift unavailable87.5% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)87.5% → 100.0% (+12.5 points)
Correctness60.0% → 100.0% (+40.0 points)75.0% → 95.0% (+20.0 points)
Discoverability100.0% — baseline ran, but no comparable score was available; uplift unavailable95.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness18.8% → 83.3% (+64.5 points)52.1% → 77.1% (+25.0 points)
Efficiency85.4% — baseline ran, but no comparable score was available; uplift unavailable70.6% — 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.

AgentDimensionBasisBaseline → With skill (uplift)Explanation
claude-codeDiscoverabilityDimension comparisonN/ADiscoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
claude-codeEfficiencyToken-only comparison98.4% → 93.0% (-5.4 points)Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.
codexDiscoverabilityDimension comparisonN/ADiscoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
codexEfficiencyToken-only comparison86.5% → 84.1% (-2.4 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.

AgentDataset caseWith skillWithout skillDeltaChangeCoverage
claude-codeAll cases720,480323,716+396,764+122.57%skill 4/4; base 4/4
claude-codedoca-common.explicit-positive.v1211,376108,172+103,204+95.41%skill 1/1; base 1/1
claude-codedoca-common.implicit-positive.v1258,876107,260+151,616+141.35%skill 1/1; base 1/1
claude-codedoca-common.implicit-positive.v2213,57372,543+141,030+194.41%skill 1/1; base 1/1
claude-codedoca-common.negative.v136,65535,741+914+2.56%skill 1/1; base 1/1
codexAll cases370,238212,988+157,250+73.83%skill 4/4; base 4/4
codexdoca-common.explicit-positive.v1120,888105,360+15,528+14.74%skill 1/1; base 1/1
codexdoca-common.implicit-positive.v176,14440,724+35,420+86.98%skill 1/1; base 1/1
codexdoca-common.implicit-positive.v2134,25144,918+89,333+198.88%skill 1/1; base 1/1
codexdoca-common.negative.v138,95521,986+16,969+77.18%skill 1/1; base 1/1
ALL AGENTSDataset aggregate1,090,718536,704+554,014+103.23%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

TierPurposeStatusEvidence
Tier 1Static validationPASSED WITH OBSERVATIONS11 validator(s); 12 finding(s)
Tier 2Semantic deduplicationPASSED WITH OBSERVATIONS2 validator(s); 5 finding(s)
Tier 3Live agent evaluationPASS2 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-common)

  • CRITICAL CONTENT_DEDUP/llm_error: LLM analysis failed for a content cluster (skills/libs/doca-common)

  • CRITICAL CONTENT_DEDUP/llm_error: LLM analysis failed for a content cluster (skills/libs/doca-common)

  • HIGH DUPLICATE/duplicate: Duplicate content found within CAPABILITIES.md: "## dev" in CAPABILITIES.md (lines 304-308) vs "## dev" in CAPABILITIES.md (lines 309-315) (CAPABILITIES.md:304)

  • HIGH DUPLICATE/duplicate: Duplicate content found within CAPABILITIES.md: "## buf" in CAPABILITIES.md (lines 155-159) vs "## buf" in CAPABILITIES.md (lines 160-160) vs "## dev" in CAPABILITIES.md (lines 329-333) (CAPABILITIES.md:155)

  • 12 additional finding(s) are available in the full evaluation artifacts.

</details>

Scoring Methodology

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

DimensionQuestionScored signals
SecurityIs it safe to use?security (100%)
CorrectnessIs the answer correct?accuracy (100%)
DiscoverabilityWas the right skill loaded when needed?skill_execution (100%)
EffectivenessDid the skill help complete the task?goal_accuracy (50%) + behavior_check (50%)
EfficiencyDid it avoid wasted tool calls and token usage?skill_efficiency (50%) + token_efficiency (50%)
  • Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.

  • Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.

  • Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.

  • The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.

  • Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).

  • Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

  • security (Security): unsafe operations, secret leakage, and unauthorized access.

  • skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.

  • skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).

  • accuracy (Accuracy): final-answer correctness against the reference answer.

  • goal_accuracy (Goal Accuracy): whether the user's goal was achieved.

  • behavior_check (Behavior Check): whether the expected workflow behavior was followed.

  • token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

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

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

The author says: Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU or ConnectX NIC attached. doca-common is present on every healthy DOCA install; reads the user's local install via `pkg-config --modversion doca-common` and inspects /opt/mellanox/doca/{lib,include,samples,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-common · 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-common.zip