Doca Common
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-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
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/doca-common/downloadUnzip 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_txqbut 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 activedoca_devinfois the runtime authority) indev +
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-leveland the app-side setter?" — worked example: "I set--sdk-log-level 60(DEBUG), my ownDOCA_LOG_DBGlines still don't print". Answered by the two-tier log model in ## logthe tier-flip iteration in ## log.
"What does this
DOCA_ERROR_*from adoca_buf_*/doca_ctx_*/doca_dev_*/doca_pe_*/doca_log_*call mean?" — worked example: "DOCA_ERROR_BAD_STATEfromdoca_ctx_start". Answered by the Common overlay on the cross-libraryDOCA_ERROR_*taxonomy inError 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_devinfovia thedoca_*_cap_*family.Wiring
doca_mmap+doca_buf_inventory+doca_buffor 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-levelvs app-side registry) model.Debugging a
DOCA_ERROR_*returned from anydoca_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 modesoverview 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 compatibilitydoca-common-specific overlay; the## Error taxonomyCommon-side view of the universalDOCA_ERROR_*set; the## Observabilitysurface (logs, PE events, capability snapshots); and the## Safety policyoverlay 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## logverb-side that covers the two-tier log model in workflow form. Plus aDeferred task verbsblock 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.cand 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 indoca-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, wherepkg-config --modversion doca-commonis the source 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.
Loading order
Read this skill first to confirm the user's question is in scope.
For the universal primitives (
log/buf/ctx/dev/progress engine), thepkg-config --modversion doca-commonanchor, the Common error overlay, observability, and safety policy, see the capabilities companion.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".
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. 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-commonis the build-time anchor every DOCA install carries; this skill's## Version compatibilityoverlays the Common-specific notes on top.doca-programming-guide/SKILL.md— general DOCA programming patterns shared by every library: the canonicalpkg-config+ meson build pattern, the universal modify-a-shipped-sample first-app workflow, the universal lifecycle, the cross-libraryDOCA_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 foundationdoca-debugbuilds its runtime-debug story on; this skill is where the two-tier log model and the universal lifecycle errors first surface, anddoca-debugcross-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 policyoverlays.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
- ATTRIBUTION.mdAdded by AutoGineer891 B
- BENCHMARK.md10.0 KiB
- CAPABILITIES.md42.3 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md14.7 KiB
- SKILLCARD.yaml2.7 KiB
- TASKS.md35.1 KiB
- evals/evals.json2.8 KiB
- skill-card.md4.4 KiB
- skill.oms.sig5.1 KiB
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-commonValidation 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:
sha256:9e22539842a371b035b6820995478cb465ac90fc3e65d8d974e6798810076bb7(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-common | nv_inference | openai | mcp_tool_poisoning | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-common | nv_inference | openai | semantic_developer_intent | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-common | nv_inference | openai | semantic_quality_policy | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-common | nv_inference | openai | semantic_security_discovery | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-common | nv_inference | openai | meta_analyzer | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / 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
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 93.8% — baseline ran, but no comparable score was available; uplift unavailable | 87.5% — 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 | 60.0% → 100.0% (+40.0 points) | 75.0% → 95.0% (+20.0 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 | 18.8% → 83.3% (+64.5 points) | 52.1% → 77.1% (+25.0 points) |
| Efficiency | 85.4% — baseline ran, but no comparable score was available; uplift unavailable | 70.6% — 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.
| 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 | 98.4% → 93.0% (-5.4 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 | 86.5% → 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.
| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
|---|---|---|---|---|---|---|
| claude-code | All cases | 720,480 | 323,716 | +396,764 | +122.57% | skill 4/4; base 4/4 |
| claude-code | doca-common.explicit-positive.v1 | 211,376 | 108,172 | +103,204 | +95.41% | skill 1/1; base 1/1 |
| claude-code | doca-common.implicit-positive.v1 | 258,876 | 107,260 | +151,616 | +141.35% | skill 1/1; base 1/1 |
| claude-code | doca-common.implicit-positive.v2 | 213,573 | 72,543 | +141,030 | +194.41% | skill 1/1; base 1/1 |
| claude-code | doca-common.negative.v1 | 36,655 | 35,741 | +914 | +2.56% | skill 1/1; base 1/1 |
| codex | All cases | 370,238 | 212,988 | +157,250 | +73.83% | skill 4/4; base 4/4 |
| codex | doca-common.explicit-positive.v1 | 120,888 | 105,360 | +15,528 | +14.74% | skill 1/1; base 1/1 |
| codex | doca-common.implicit-positive.v1 | 76,144 | 40,724 | +35,420 | +86.98% | skill 1/1; base 1/1 |
| codex | doca-common.implicit-positive.v2 | 134,251 | 44,918 | +89,333 | +198.88% | skill 1/1; base 1/1 |
| codex | doca-common.negative.v1 | 38,955 | 21,986 | +16,969 | +77.18% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 1,090,718 | 536,704 | +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
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED WITH OBSERVATIONS | 11 validator(s); 12 finding(s) |
| Tier 2 | Semantic deduplication | PASSED WITH OBSERVATIONS | 2 validator(s); 5 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-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>
| 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 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.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download doca-common.zip