Doca Caps
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-caps skill provides instructions for using the doca_caps command-line tool, a read-only utility designed to report the state of DOCA on a host. It enables users to enumerate DOCA devices, identify available libraries, list representor devices, and capture capability snapshots for debugging or installation verification. This skill acts as a thin loader, directing users to specific companion files for detailed capability reporting and task-based workflows. It is strictly limited to read-only operations and does not perform DOCA installations or library-internal configuration.
What it does
- Enumerates DOCA devices and their associated PCIe addresses.
- Lists DOCA libraries available on the current operating system.
- Identifies visible representor devices for specific PFs.
- Captures side-effect-free capability snapshots for debug or smoke-test purposes.
- Scopes capability output to specific PCIe addresses.
- Interprets empty or missing output from the tool.
When to use it
- When you need to verify if DOCA hardware is visible on a host.
- When you need to confirm which DOCA libraries are supported on your current OS.
- When you need to generate a capability snapshot to attach to a debug session.
- When you need to perform a quick smoke-test of a DOCA installation.
- When you need to identify representor devices for network configuration.
Advantages
- Provides a standardized, side-effect-free way to inspect system state.
- Helps verify installation health without modifying system configuration.
- Facilitates consistent data collection for debugging and support.
- Integrates cleanly with other DOCA-related workflows and skills.
Usage examples
- “What DOCA devices does this host see?”
- The agent invokes doca_caps --list-devs to provide a list of visible DOCA devices and their PCIe addresses.
- “Is Flow available on my RHEL host?”
- The agent checks the library-availability surface and invokes the tool to confirm if the Flow library is present.
- “Enumerate VF representors for pf0.”
- The agent uses the tool to list representor devices, specifically filtering for those associated with pf0.
- “Save device and library capability output to a file.”
- The agent executes the snapshot workflow to capture the current system state into a file for later debugging.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/doca-caps/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip doca-caps.zip -d .agents/skills
Documentation by NVIDIA
DOCA Capabilities Print Tool (doca_caps)
Examples
"What DOCA devices does this host see?" — this is in scope. Stay on
doca_caps; do not start a library-internal capability matrix here."Is Flow available on my RHEL host?" — list libraries then scope per-device if needed.
Instructions
Follow Loading order below. Open the tasks companion for run / test. Open the capabilities companion for what the CLI reports. Do not open every companion file.
Where to start: This is a tool skill for invoking doca_caps,
a side-effect-free CLI. Open the tasks companion and start at
## run for the documented invocations, or
## test when using doca_caps as an install
smoke-test. Open the capabilities companion when the
question is what kinds of capability families doca_caps reports.
Boundary: this skill never installs DOCA.
doca_capsverifies an install and never performs one. A request to install DOCA — choosing packages, adding repositories, running a package manager — is out of scope here, and stays out of scope even though this skill's## testverb uses the tool as an install smoke-test afterwards. Hand the whole request todoca-setup/SKILL.mdby name. Naming the owner and then listing the steps anyway is the failure mode to avoid: do not walk the installation, not even as a summary or a caveat.This narrows installation only. It is not a reason to answer narrowly about tooling: when the user's question reaches past
doca_caps— what else can I ask this hardware, how do I measure it, how do I inspect a running pipeline — name the sibling tool that owns it (doca_bench,doca_flow_tune,pcc_counters.sh,doca_comm_channel_admin, …) and tie it to the question. Pointing at the rest of the catalog is this skill's job, not a boundary breach.
Example questions this skill answers well
The CLASSES of doca_caps questions this skill is built to answer,
each with one worked example. The class is the load-bearing piece;
the worked example is one instance.
"What DOCA devices does this host see?" — worked example: "is my BlueField PF visible to DOCA". Answered by the device enumeration in the capabilities companion
the
--list-devsinvocation in the tasks companion.
"Which DOCA libraries are available on this OS?" — worked example: "is Flow available on my RHEL host". Answered by the library-availability surface in the capabilities companion
the library-listing invocation in the tasks companion.
"Does this device support library X capability Y?" — worked example: "does this device support Flow hairpin?". Answered by the per-device per-library capability matrix in the capabilities companion
the
--pci-addr-scoped invocation in the tasks companion.
"What representors are visible to DOCA?" — worked example: "enumerate VF representors for pf0". Answered by the representor enumeration in the capabilities companion
the
--list-rep-devsinvocation in the tasks companion.
"I want a snapshot of state to attach to my debug session." — worked example: "save device + library + capability output to a file". Answered by the snapshot workflow in the tasks companion and consumed by
doca-debug/SKILL.mdstep 3 (read-only triple) anddoca-programming-guide/SKILL.md."
doca_capsreturned nothing for capability Y — what does that mean?" — worked example: "empty output for RDMA". Answered by the empty-output interpretation rules in the tasks companion + the capabilities companion.
Audience
This skill serves external operators, developers, and AI agents who need a side-effect-free way to ask "what does DOCA see on this host?" before doing anything that changes state. Concretely:
An external developer who installed DOCA (or is using the public NGC DOCA container per
doca-setup/SKILL.md) and wants to confirm the install can see hardware before writing code.A platform operator deploying a DOCA service who wants a documented, read-only snapshot of which DOCA libraries this host actually supports and which capabilities each DOCA device offers.
An AI agent producing a capability snapshot artifact during the documented setup or programming-guide debug procedures (it's listed as the canonical first step in
doca-setup/SKILL.mdanddoca-programming-guide/SKILL.md).
It is not for users debugging doca_caps itself, and not a
substitute for the live public Capabilities Print Tool guide.
doca_caps is shipped as a tool (a single CLI binary), not a
library you link against. The skill uses the same kind: tool
three-file shape as the rest of the bundle so the agent's task-verb
contract (configure / build / modify / run / test / debug) is uniform
across libraries, services, and tools — even when individual verbs
collapse to a routing stub for a shipped read-only binary.
When to load this skill
Load this skill when the user is — or the agent needs to — invoke
doca_caps on a real host with DOCA installed (or inside the public
NGC DOCA container). Concretely:
Running
doca_caps --list-devsto enumerate DOCA devices.Running
doca_caps --list-rep-devsto enumerate representor devices.Scoping output to a specific PCIe address with
--pci-addr.Listing the DOCA libraries the install reports as available on the current OS.
Listing the available DOCA logger names.
Capturing a documented, side-effect-free capability snapshot as prerequisite evidence for later
## debugworkflows.
Do not load this skill for general DOCA orientation, library API
work, or installation. For those, use
doca-public-knowledge-map/SKILL.md,
the matching libs/<library> skill, or
doca-setup/SKILL.md.
What this skill provides
This is a thin loader. Substantive material lives in two companion files:
the capabilities companion — what
doca_capsreports (the five documented capability families: devices, representors, libraries, library capabilities, loggers), version availability and execution environment, the tool's narrow error surface, its observability role inside other skills' workflows, and its read-only safety posture.the tasks companion — step-by-step workflows for the in-scope task verbs:
configure(route to install),build(route to install),modify(refuse),run(the documented invocations),test(capability snapshot as install smoke-test),debug(what to do when the tool reports nothing or fails), plus aDeferred task verbsblock routing out-of-scope questions.
The skill assumes a host where DOCA is already installed (or the public
NGC DOCA container is running) and the operator has whatever
permissions the public guide requires for doca_caps to enumerate
devices on their platform.
What this skill deliberately does not ship
This skill is agent guidance, not a samples or scripts bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:
Pre-baked example output. Output is install- and hardware-specific. A captured example pinned to one platform and one DOCA version misleads operators on a different platform / version.
Wrappers, parsers, or scripts in any language that consume
doca_capsoutput. The output format is documented; if a user wants to script against it, the right answer is "read the live guide, write the parser against your installed version".A
samples/orreference/subtree. This is a thin loader for a documented CLI; substantive material lives on the public page and in--help.
Loading order
Read this skill first to confirm the user's question is in scope (the user actually wants to invoke
doca_caps, not learn about DOCA in general).For what
doca_capsreports, version availability, error surface, and safety posture, see the capabilities companion.For the documented invocations and the capability-snapshot workflow —
configure,build,modify,run,test,debug— see the tasks companion.
Related skills
doca-public-knowledge-map/SKILL.md— routing to the public Capabilities Print Tool guide and the rest of the public DOCA documentation set.doca-setup/SKILL.md— env preparation, install verification (doca_capsis the canonical first step there), and the I have no install yet path with the public NGC DOCA container.doca-programming-guide/SKILL.md— cross-library programming patterns, including the## debugprocedure where the saveddoca_capssnapshot is consumed.The matching
libs/<library>skill — for fine-grained, library-specific capability questions that go beyond whatdoca_capsexposes.
Files
- ATTRIBUTION.mdAdded by AutoGineer885 B
- BENCHMARK.md9.9 KiB
- CAPABILITIES.md14.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md10.3 KiB
- SKILLCARD.yaml2.7 KiB
- TASKS.md17.1 KiB
- evals/evals.json2.3 KiB
- skill-card.md5.0 KiB
- skill.oms.sig5.1 KiB
BENCHMARK.md
Skill Benchmark: doca-caps
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
doca-capsValidation status:
passedEvaluation date: 2026-10-28
Evaluator version:
1.6.3Agents: Claude Code (
aws/anthropic/bedrock-claude-opus-5-5), Codex (openai/openai/gpt-6.1-sol)Tasks: 4 evaluation tasks (3 positive, 1 negative)
Dataset digest:
sha256:4b2b0da62d51bb794d989f58e8d89cb39e1ebfc2cd1c7e72c9654057f2085cc9(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-caps | nv_inference | openai | mcp_tool_poisoning | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-caps | nv_inference | openai | semantic_developer_intent | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-caps | nv_inference | openai | semantic_quality_policy | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-caps | nv_inference | openai | semantic_security_discovery | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-caps | nv_inference | openai | meta_analyzer | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
Controls for
doca-caps: 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 | 90.1% — baseline ran, but no comparable score was available; uplift unavailable | 94.4% — baseline ran, but no comparable score was available; uplift unavailable |
| Security | 100.0% → 100.0% (±0.0 points) | 75.0% → 100.0% (+25.0 points) |
| Correctness | 65.0% → 90.0% (+25.0 points) | 90.0% → 100.0% (+10.0 points) |
| Discoverability | 98.3% — 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% → 75.0% (+50.0 points) | 62.5% → 100.0% (+37.5 points) |
| Efficiency | 87.3% — baseline ran, but no comparable score was available; uplift unavailable | 76.9% — 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% → 94.6% (-3.4 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 | 81.4% → 88.1% (+6.7 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 | 740,853 | 433,909 | +306,944 | +70.74% | skill 4/4; base 4/4 |
| claude-code | doca-caps.explicit-positive.v1 | 255,197 | 143,306 | +111,891 | +78.08% | skill 1/1; base 1/1 |
| claude-code | doca-caps.implicit-positive.v1 | 208,641 | 146,932 | +61,709 | +42.00% | skill 1/1; base 1/1 |
| claude-code | doca-caps.implicit-positive.v2 | 240,688 | 72,089 | +168,599 | +233.88% | skill 1/1; base 1/1 |
| claude-code | doca-caps.negative.v1 | 36,327 | 71,582 | -35,255 | -49.25% | skill 1/1; base 1/1 |
| codex | All cases | 334,453 | 378,134 | -43,681 | -11.55% | skill 4/4; base 4/4 |
| codex | doca-caps.explicit-positive.v1 | 93,676 | 161,126 | -67,450 | -41.86% | skill 1/1; base 1/1 |
| codex | doca-caps.implicit-positive.v1 | 96,951 | 66,942 | +30,009 | +44.83% | skill 1/1; base 1/1 |
| codex | doca-caps.implicit-positive.v2 | 97,698 | 39,070 | +58,628 | +150.06% | skill 1/1; base 1/1 |
| codex | doca-caps.negative.v1 | 46,128 | 110,996 | -64,868 | -58.44% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 1,075,306 | 812,043 | +263,263 | +32.42% | 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); 13 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/tools/doca-caps)HIGH DUPLICATE/duplicate: Duplicate content found within SKILL.md: "## Instructions" in SKILL.md (lines 11-41) vs "## What this skill provides" in SKILL.md (lines 135-156) vs "## Loading order" in SKILL.md (lines 174-184) (
SKILL.md:11)MEDIUM QUALITY/quality_discoverability: Description uses first/second person (
skills/tools/doca-caps/SKILL.md)MEDIUM SCHEMA/metadata_key_style: Metadata key 'doca_version' is not kebab-case (
skills/tools/doca-caps/SKILL.md)MEDIUM SCHEMA/folder_hierarchy: Unexpected nesting depth for general skill (
skills/tools/doca-caps)10 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 ≥ 2.6.0 installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU or ConnectX NIC; runs identically on the host or on the BlueField Arm side. Invokes /opt/mellanox/doca/tools/doca_caps and reads `pkg-config doca-common` to confirm the install. 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-caps · 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-caps.zip