Doca Mgmt
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
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
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/doca-mgmt/downloadUnzip 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_SYSTEMinstead.
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-mgmteven 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 inCapabilities 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_ctxon the device, then adoca_mgmt_dev_rep_ctxon a specific VF representor for caps-general programming". Answered by the management-context lifecycle inCapabilities 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_geton the representor context, read the data-direct flag". Answered by the capability-query pattern inCapabilities and modes
the test step in ## test.
"How do I modify a device-level feature flag safely?" — worked example: "toggle
data_directon a representor; capture pre-state, write, verify, prepare rollback". Answered by the apply-with-rollback workflow inmodify layered on the
bundle-wide hardware-safety meta-policy referenced from
Safety policy.
"What does the
doca_mgmt_raw_cmdscope mean and which scope should I use?" — worked example: "I have a vendor- documented opcode for aDEBUG_READ_ONLYquery — what scope does that need and what is the blast radius?". Answered by the command-scope ladder inCapabilities and modes
the raw-command verb in ## use.
"What does this
DOCA_ERROR_*from adoca_mgmt_*call mean and which layer caused it?" — worked example: "DOCA_ERROR_IO_FAILEDfromdoca_mgmt_raw_cmd". Answered by the mgmt overlay on the cross-library taxonomy in ## Error taxonomythe layered ladder in
debug that escalates to
doca-debug/SKILL.mdand todoca-hardware-safety/SKILL.mdwhen 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_ctxon adoca_devto inspect or modify device-level state.Standing up a
doca_mgmt_dev_rep_ctxon adoca_dev_rep(or viadoca_mgmt_dev_rep_ctx_create_by_pci_addrwhen 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
setpath.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_cmdagainst the device's firmware-control endpoint, choosing the rightenum doca_mgmt_cmd_scopefor the operation's blast radius.Debugging a
DOCA_ERROR_*returned by adoca_mgmt_*call and deciding whether the cause is a configuration mistake, a lifecycle ordering bug, a missing capability on the device, anfwctlioctl 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 publicdoca_mgmt_*symbol is tagged EXPERIMENTAL), the mgmt overlay on the cross-libraryDOCA_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 aDeferred task verbsblock 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-mgmtis the source of truth.Raw-command opcode catalogs. The
doca_mgmt_raw_cmdin-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 viadoca-public-knowledge-map/SKILL.md.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 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.
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".
Related skills
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.mdanddoca-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.mdanddoca-pcc-counters/SKILL.md— programmable congestion control. doca-mgmt'sdoca_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 canonicalpkg-config+ meson build pattern, the universal modify-a-shipped-sample first-app workflow, the cross-libraryDOCA_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 compatibilityanchor 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 appendixin the tasks companion defers to them before falling back to the manual chain.
Files
- ATTRIBUTION.mdAdded by AutoGineer885 B
- BENCHMARK.md9.8 KiB
- CAPABILITIES.md29.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md14.1 KiB
- SKILLCARD.yaml2.7 KiB
- TASKS.md29.6 KiB
- evals/evals.json2.8 KiB
- skill-card.md5.4 KiB
- skill.oms.sig5.1 KiB
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-mgmtValidation 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:d675611fce16769fe5dbc30305f35bc343f0872106413cec0c5cdf6cbd7a0ebb(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-mgmt | nv_inference | openai | mcp_tool_poisoning | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-mgmt | nv_inference | openai | semantic_developer_intent | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-mgmt | nv_inference | openai | semantic_quality_policy | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-mgmt | nv_inference | openai | semantic_security_discovery | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-mgmt | nv_inference | openai | meta_analyzer | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / 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
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 92.5% — baseline ran, but no comparable score was available; uplift unavailable | 83.0% — baseline ran, but no comparable score was available; uplift unavailable |
| Security | 100.0% → 100.0% (±0.0 points) | 87.5% → 87.5% (±0.0 points) |
| Correctness | 45.0% → 100.0% (+55.0 points) | 95.0% → 100.0% (+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 | 25.0% → 77.1% (+52.1 points) | 64.6% → 58.3% (-6.3 points) |
| Efficiency | 85.5% — baseline ran, but no comparable score was available; uplift unavailable | 74.2% — 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% → 90.9% (-7.1 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 | 79.1% → 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.
| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
|---|---|---|---|---|---|---|
| claude-code | All cases | 788,054 | 362,146 | +425,908 | +117.61% | skill 4/4; base 4/4 |
| claude-code | doca-mgmt.explicit-positive.v1 | 263,690 | 107,973 | +155,717 | +144.22% | skill 1/1; base 1/1 |
| claude-code | doca-mgmt.implicit-positive.v1 | 263,443 | 109,723 | +153,720 | +140.10% | skill 1/1; base 1/1 |
| claude-code | doca-mgmt.implicit-positive.v2 | 224,197 | 72,635 | +151,562 | +208.66% | skill 1/1; base 1/1 |
| claude-code | doca-mgmt.negative.v1 | 36,724 | 71,815 | -35,091 | -48.86% | skill 1/1; base 1/1 |
| codex | All cases | 706,672 | 332,367 | +374,305 | +112.62% | skill 4/4; base 4/4 |
| codex | doca-mgmt.explicit-positive.v1 | 173,058 | 194,118 | -21,060 | -10.85% | skill 1/1; base 1/1 |
| codex | doca-mgmt.implicit-positive.v1 | 154,393 | 47,841 | +106,552 | +222.72% | skill 1/1; base 1/1 |
| codex | doca-mgmt.implicit-positive.v2 | 284,611 | 55,800 | +228,811 | +410.06% | skill 1/1; base 1/1 |
| codex | doca-mgmt.negative.v1 | 94,610 | 34,608 | +60,002 | +173.38% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 1,494,726 | 694,513 | +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
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED WITH OBSERVATIONS | 11 validator(s); 15 finding(s) |
| Tier 2 | Semantic deduplication | PASSED WITH OBSERVATIONS | 2 validator(s); 2 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-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>
| 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. 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.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download doca-mgmt.zip