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Doca Mgmt

NVIDIAOfficialCreated by NVIDIA

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

From the author

Use this skill when the user is doing hands-on DOCA Management programming against BlueField / ConnectX devices — standing up a management or representor context (doca_mgmt_dev_ctx / doca_mgmt_dev_rep_ctx), querying device caps (data-direct, caps-general), toggling congestion-control global status, modifying diagnostics-data, setting ICM quotas, or issuing a raw firmware command via doca_mgmt_raw_cmd with the right scope (CONFIGURATION / DEBUG_READ_ONLY / DEBUG_WRITE / DEBUG_WRITE_FULL). Trigger even when the user does not say "DOCA Management" — typical implicit phrasings include "fleet tool that walks every BlueField and reads device state", "toggle data-direct on a VF", "set an ICM quota per representor", "send a raw firmware command from C", "DOCA_ERROR_IO_FAILED from raw_cmd", or "fwctl ioctl is failing". Refuse and route elsewhere for mlxconfig direct operation, BFB / firmware reflash, streaming telemetry, doca_caps CLI snapshots, or DOCA install itself — those belong to other skills.
— the description by NVIDIA

AutoGineer summary

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

This skill assists developers in building management-plane applications that interact with BlueField DPUs and ConnectX NICs. It covers the lifecycle of management and representor contexts, querying device capabilities, modifying congestion control settings, managing ICM quotas, and executing raw firmware commands. It is designed for developers integrating device-level state management into fleet tools or orchestration software, emphasizing safe modification patterns and proper error handling within the DOCA ecosystem.

What it does

  • Guides the setup of doca_mgmt_dev_ctx and doca_mgmt_dev_rep_ctx contexts.
  • Explains how to query and modify device-level capabilities and feature flags.
  • Provides the command-scope ladder for safe execution of raw firmware commands.
  • Details the management-specific overlay for the DOCA error taxonomy.
  • Outlines procedures for modifying diagnostics data and ICM quotas.
  • Integrates with hardware safety policies for state-changing operations.

When to use it

  • When developing fleet management or orchestration tools that require programmatic access to DPU/NIC state.
  • When you need to toggle features like data-direct on a VF representor.
  • When debugging DOCA_ERROR_IO_FAILED or other management-plane API errors.
  • When you need to issue raw firmware commands with specific security scopes.
  • When you need to query device-wide attributes programmatically rather than via CLI snapshots.

Advantages

  • Ensures safe interaction with device state through structured workflows.
  • Provides clear boundaries between management-plane tasks and other DOCA domains.
  • Standardizes the approach to firmware command scopes and error handling.
  • Promotes the use of verified reference samples for application development.

Usage examples

“How do I toggle data-direct on a VF?”
The agent will guide you to open a representor context, capture the pre-state, apply the toggle, and verify the change while following the hardware-safety policy.
“I am getting DOCA_ERROR_IO_FAILED from doca_mgmt_raw_cmd, what should I check?”
The agent will help you verify the four-way version match, the opcode, and ensure the command scope (e.g., DEBUG_READ_ONLY) is appropriate for the operation.
“How do I set an ICM quota per representor?”
The agent will provide the steps to use the doca_mgmt_icm_quota_* functions within a valid representor context.

Install

  1. Download the skill as a ZIP:

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

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

Documentation by NVIDIA

DOCA Management

Examples

  • "Toggle data-direct on a VF." — this is in scope. Use the management / representor context; do not drop to raw mlxconfig.

  • "DOCA_ERROR_IO_FAILED from raw_cmd." — the firmware rejected the command: re-check the four-way version match, the opcode, and the narrowest correct scope (CONFIGURATION, DEBUG_READ_ONLY, DEBUG_WRITE, DEBUG_WRITE_FULL). fwctl or permission failures surface as DOCA_ERROR_OPERATING_SYSTEM instead.

Instructions

Follow Loading order below. Open the tasks companion for configure / raw-cmd / debug. Open the capabilities companion for scopes and device caps. Do not open every companion file.

Where to start: This skill assumes DOCA is already installed and the user is doing hands-on management-plane work against a BlueField / ConnectX device — typically a fleet-management or orchestration tool that needs to query or modify device-level state programmatically. Open the tasks companion if the user wants to do something (install / configure / build / modify / run / test / debug / use); open the capabilities companion when the question is what can doca-mgmt express on this version — the management context model, the raw-command scope ladder, the sub-domain surfaces (caps-general, cc-global-status, diagnostics- data, icm-quota), version compatibility, and the safety overlay. If the user has not installed DOCA yet, route to doca-setup/SKILL.md first.

How to use this skill. Read this file, then search the companion file for the ## <name> heading the question needs and read that whole section. Answer completely from it, quoting only its symbols and commands. Do not browse docs; use the entry points in doca-public-knowledge-map. Give environment checks as commands for the user's DOCA host. Any change to device state also follows doca-hardware-safety: say so and cite it. For out-of-scope requests, name the skill that owns them and stop.

Example questions this skill answers well

The CLASSES of management-plane 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.

  • "Is doca-mgmt even the right surface, or do I want telemetry / bench / caps?" — worked example: "I'm building a fleet inventory tool — do I use doca-mgmt to query each BlueField's data-direct capability, or doca-telemetry, or doca_caps?". Answered by the management-plane vs observability-plane vs read-only-CLI selection rule in

    Capabilities and modes

    surface-selection table.

  • "How do I stand up a management context on a device (and a representor)?" — worked example: "open a doca_mgmt_dev_ctx on the device, then a doca_mgmt_dev_rep_ctx on a specific VF representor for caps-general programming". Answered by the management-context lifecycle in

    Capabilities and modes

    • the configure walk in

    configure.

  • "How do I query a device capability — say, whether it supports data-direct?" — worked example: "create a caps-general handle, call doca_mgmt_device_caps_general_get on the representor context, read the data-direct flag". Answered by the capability-query pattern in

    Capabilities and modes

    • the test step in ## test.

  • "How do I modify a device-level feature flag safely?" — worked example: "toggle data_direct on a representor; capture pre-state, write, verify, prepare rollback". Answered by the apply-with-rollback workflow in

    modify layered on the

    bundle-wide hardware-safety meta-policy referenced from

    Safety policy.

  • "What does the doca_mgmt_raw_cmd scope mean and which scope should I use?" — worked example: "I have a vendor- documented opcode for a DEBUG_READ_ONLY query — what scope does that need and what is the blast radius?". Answered by the command-scope ladder in

    Capabilities and modes

    • the raw-command verb in ## use.

  • "What does this DOCA_ERROR_* from a doca_mgmt_* call mean and which layer caused it?" — worked example: "DOCA_ERROR_IO_FAILED from doca_mgmt_raw_cmd". Answered by the mgmt overlay on the cross-library taxonomy in ## Error taxonomy

    • the layered ladder in

    debug that escalates to

    doca-debug/SKILL.md and to doca-hardware-safety/SKILL.md when the cause is a device-state change.

When to load this skill

Load this skill when the user is doing hands-on DOCA Management work on a host with one or more BlueField / ConnectX devices. Concretely:

  • Standing up a doca_mgmt_dev_ctx on a doca_dev to inspect or modify device-level state.

  • Standing up a doca_mgmt_dev_rep_ctx on a doca_dev_rep (or via doca_mgmt_dev_rep_ctx_create_by_pci_addr when the representor is not available) to inspect or modify a VF / function-level configuration.

  • Querying a device's general capabilities via doca_mgmt_device_caps_general_* (data-direct support and similar device-wide attributes).

  • Programming a representor's general capabilities via the same set path.

  • Querying or modifying congestion-control global status via doca_mgmt_cc_global_status_* (RP / NP protocol type, priority, enable / disable).

  • Modifying or querying multi-domain diagnostics data via doca_mgmt_diagnostics_data_* against a specific device.

  • Setting ICM quota limits per device or per representor via doca_mgmt_icm_quota_* and reading the current allocation / max-reached.

  • Issuing a doca_mgmt_raw_cmd against the device's firmware-control endpoint, choosing the right enum doca_mgmt_cmd_scope for the operation's blast radius.

  • Debugging a DOCA_ERROR_* returned by a doca_mgmt_* call and deciding whether the cause is a configuration mistake, a lifecycle ordering bug, a missing capability on the device, an fwctl ioctl failure, or a firmware-side rejection.

Do not load this skill for general DOCA orientation, install of DOCA itself, live performance benchmarking, or stream-based telemetry. For those, use doca-public-knowledge-map/SKILL.md, doca-setup/SKILL.md, doca-bench/SKILL.md, and doca-telemetry/SKILL.md respectively.

What this skill provides

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

  • the capabilities companion — what doca-mgmt can express on this version: the management-context object model (doca_mgmt_dev_ctx + doca_mgmt_dev_rep_ctx), the raw- command scope ladder (DOCA_MGMT_CMD_SCOPE_CONFIGURATION, DOCA_MGMT_CMD_SCOPE_DEBUG_READ_ONLY, DOCA_MGMT_CMD_SCOPE_DEBUG_WRITE, DOCA_MGMT_CMD_SCOPE_DEBUG_WRITE_FULL), the sub-domain surfaces (caps-general, cc-global-status, diagnostics-data, icm-quota) with their capability-query gates, the EXPERIMENTAL symbol-set policy (every public doca_mgmt_* symbol is tagged EXPERIMENTAL), the mgmt overlay on the cross-library DOCA_ERROR_* taxonomy, the observability surface (pre-state capture + post-write re-query), and the per-artifact safety overlay on the bundle-wide hardware-safety meta-policy.

  • the tasks companion — step-by-step workflows for the eight in-scope verbs: install, configure, build, modify, run, test, debug, use. Plus a Deferred task verbs block that points out-of-scope 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 (management- plane operations typically require root or an equivalent device-administration capability). 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 Management application source code, in any language. Management-plane code touches device state and a wrong write can take the device offline; authoring it from documentation prose, especially against an EXPERIMENTAL symbol set whose shape can change between releases, is forbidden by this skill. The agent's job is to route the user to verified reference code (the shipped DOCA samples on the installed package set are the canonical worked examples) and to prescribe a minimum-diff modification via the universal modify-a-sample workflow in doca-programming-guide/SKILL.md.

  • Standalone build manifests 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-mgmt is the source of truth.

  • Raw-command opcode catalogs. The doca_mgmt_raw_cmd in-payload and out-payload are device / firmware specific; the skill names the scope of a command class and the discipline the agent applies (pre-state capture, scope ladder, rollback path) but it does NOT enumerate opcodes — those are vendor / device documentation and looked up via doca-public-knowledge-map/SKILL.md.

  • 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 management-context object model, the raw-command scope ladder, the sub-domain surfaces, version compatibility, the error taxonomy, observability, and the per-artifact safety overlay, see the capabilities companion.

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

Both companion files cross-link to each other and to 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 "mgmt-specific guidance".

  • doca-hardware-safety/SKILL.md — the bundle-wide hardware-safety meta-policy. Because doca-mgmt is the surface through which agents most commonly modify device state programmatically, the safety overlay in the capabilities companion cross-links the meta-policy load-bearingly and the agent walks the hardware-safety ladder whenever a write is recommended.

  • doca-telemetry/SKILL.md and doca-telemetry-exporter/SKILL.md — the observability surfaces for streaming telemetry from the device. Use those when the user wants live metrics; use doca-mgmt when the user wants point-in-time programmatic query or modification.

  • doca-caps/SKILL.md — the read-only CLI for inspecting device capabilities. Use the tool when the user wants an interactive snapshot; use doca-mgmt when the user wants the same information through the C API in a fleet tool.

  • doca-bench/SKILL.md — the live-performance benchmarking tool. Out of scope for doca-mgmt; the skill names the boundary.

  • doca-pcc/SKILL.md and doca-pcc-counters/SKILL.md — programmable congestion control. doca-mgmt's doca_mgmt_cc_global_status_* surface is the global enable/disable + protocol selection control plane that layers on the deeper programmable-CC surface owned by doca-pcc.

  • 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.

  • doca-setup/SKILL.md — env preparation, install verification, and the I have no install yet path with the public NGC DOCA container.

  • 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 cross-library DOCA_ERROR_* taxonomy. This skill layers management-plane specifics on top.

  • doca-debug/SKILL.md — the cross-cutting debug ladder. Management-specific debug overlays on top of it.

  • doca-version/SKILL.md — the version detection / four-way match rule every per-artifact ## Version compatibility anchor builds on. This skill quotes the management-specific overlay only.

  • doca-structured-tools-contract/SKILL.md — the JSON-schema contracts for the agent-preferred structured helpers; the ## Command appendix in the tasks companion defers to them before falling back to the manual chain.

Files

BENCHMARK.md

Skill Benchmark: doca-mgmt

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

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

Evaluation Metadata

  • Skill: doca-mgmt

  • 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:d675611fce16769fe5dbc30305f35bc343f0872106413cec0c5cdf6cbd7a0ebb (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-mgmtnv_inferenceopenaimcp_tool_poisoningaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-mgmtnv_inferenceopenaisemantic_developer_intentaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-mgmtnv_inferenceopenaisemantic_quality_policyaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-mgmtnv_inferenceopenaisemantic_security_discoveryaws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
doca-mgmtnv_inferenceopenaimeta_analyzeraws/anthropic/bedrock-claude-opus-5-52.13.1nondeterministic / provider_defaults
  • Controls for doca-mgmt: 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)
Overall92.5% — baseline ran, but no comparable score was available; uplift unavailable83.0% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)87.5% → 87.5% (±0.0 points)
Correctness45.0% → 100.0% (+55.0 points)95.0% → 100.0% (+5.0 points)
Discoverability100.0% — baseline ran, but no comparable score was available; uplift unavailable95.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness25.0% → 77.1% (+52.1 points)64.6% → 58.3% (-6.3 points)
Efficiency85.5% — baseline ran, but no comparable score was available; uplift unavailable74.2% — 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.

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 comparison98.0% → 90.9% (-7.1 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 comparison79.1% → 82.3% (+3.2 points)Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

AgentDataset caseWith skillWithout skillDeltaChangeCoverage
claude-codeAll cases788,054362,146+425,908+117.61%skill 4/4; base 4/4
claude-codedoca-mgmt.explicit-positive.v1263,690107,973+155,717+144.22%skill 1/1; base 1/1
claude-codedoca-mgmt.implicit-positive.v1263,443109,723+153,720+140.10%skill 1/1; base 1/1
claude-codedoca-mgmt.implicit-positive.v2224,19772,635+151,562+208.66%skill 1/1; base 1/1
claude-codedoca-mgmt.negative.v136,72471,815-35,091-48.86%skill 1/1; base 1/1
codexAll cases706,672332,367+374,305+112.62%skill 4/4; base 4/4
codexdoca-mgmt.explicit-positive.v1173,058194,118-21,060-10.85%skill 1/1; base 1/1
codexdoca-mgmt.implicit-positive.v1154,39347,841+106,552+222.72%skill 1/1; base 1/1
codexdoca-mgmt.implicit-positive.v2284,61155,800+228,811+410.06%skill 1/1; base 1/1
codexdoca-mgmt.negative.v194,61034,608+60,002+173.38%skill 1/1; base 1/1
ALL AGENTSDataset aggregate1,494,726694,513+800,213+115.22%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); 15 finding(s)
Tier 2Semantic deduplicationPASSED WITH OBSERVATIONS2 validator(s); 2 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-mgmt)

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

  • MEDIUM QUALITY/quality_discoverability: Description contains vague words (skills/libs/doca-mgmt/SKILL.md)

  • MEDIUM SCHEMA/metadata_key_style: Metadata key 'doca_version' is not kebab-case (skills/libs/doca-mgmt/SKILL.md)

  • MEDIUM SCHEMA/folder_hierarchy: Unexpected nesting depth for general skill (skills/libs/doca-mgmt)

  • 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. Reads the user's local install via `pkg-config doca-mgmt` + `doca-common` and inspects /opt/mellanox/doca/{lib,include,samples,applications}; runtime operations require root (or device-admin cap_*) and an /dev/fwctl* character device exposed by the host kernel. 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-mgmt · 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-mgmt.zip