doca-flow-tune

Imported from nvidia/skills (skills/doca-flow-tune/SKILL.md). Install upstream with npx skills add nvidia/skills --skill doca-flow-tune. Copyright stays with the author (Apache-2.0).

DOCA Flow Tune (doca_flow_tune)

Examples

• "Flow rule-install rate is low on BlueField." — this is in scope. Capture a monitor-mode baseline, then pick one tuning axis.
• "Tune visualize step is empty." — check the dumper JSON/CSV / analyze JSON before changing the Flow program.

Instructions

Follow Loading order below. Open the tasks companion for configure / tune / debug. Open the capabilities companion for online vs offline modes. Do not open every companion file.

Command surface (DOCA 3.5 public Flow Tune guide). doca_flow_tune is a single binary whose role on a given invocation is determined by which of three top-level commands the user picks — monitor, analyze, visualize (confirm with --help on the installed version); use only the commands the public guide and the installed --help list. Counter dumping is part of monitor (--enable-dump, or the background sub-command for dumping without screen output), not a separate command; analyze accepts the export and packet-trace sub-commands (hyphenated). monitor and analyze run the binary in server-attached online mode against a live doca-flow application reached over a Unix- domain socket whose path lives in network.server_uds of the JSON configuration file; visualize (and offline analysis of a previously exported pipeline file) runs in offline / captured-snapshot mode against JSON / CSV files the online commands previously dropped into the configured outputs_directory. The rest of this skill (and the capabilities companion / the tasks companion) uses the legacy "server role / online mode / offline mode" framing — that framing is internally consistent with the command surface here: server role = a server-attached online command (monitor, analyze export / analyze packet-trace); online mode = any of those; offline mode = visualize over captured files. Treat the command name as the primary handle; treat server/online/offline as the downstream behavioral consequence of the subcommand pick.

Where to start: This is a tool skill for invoking doca_flow_tune, the unified DOCA Flow tuning tool. Open the tasks companion and start at ## configure to commit to the three-axis decision (target Flow pipeline × tuning axis × measurement) and pick offline vs online vs server-attach mode, then ## run for the snapshot → analyze → visualize loop, then ## test for the smoke-before-bulk overlay that gates any state-changing application of a tuning recommendation back into the Flow application's code. Open the capabilities companion when the question is what state doca_flow_tune can observe and recommend on, how its server / client roles fit inside the single artifact, which DOCA version the tool ships in, or how to interpret the dumper / monitor / analyze / visualize outputs without fooling yourself. If DOCA is not installed, route to doca-setup/SKILL.md first; if the user has no running doca-flow application yet, route to doca-flow/SKILL.md — flow-tune does not create pipes, it observes and recommends on top of pipes the library already created.

Example questions this skill answers well

The CLASSES of doca_flow_tune 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.

• "Do I measure first or tune first?" — worked example: "my doca-flow service runs on a BlueField-3 and I think the rule-install rate is below what the device can sustain". Answered by the measure, then tune rule in the capabilities companion: a monitor-mode baseline, one tuning axis, then a monitor-mode re-capture on the same axis.
• "Capture a snapshot of a live doca-flow pipeline's hardware and software counters without touching the dataplane." — worked example: "I want a side-effect-free dumper / monitor run against the running Flow ports for an operations-rate profile". Answered by the snapshot flow in the tasks companion plus the read-only-by-default posture in the capabilities companion.
• "Pick the right tuning axis — rule placement, resource hints, or hardware-offload mode — for the question I actually have." — worked example: "my Flow pipe's rule-install rate is low; is this a placement question or a table-sizing question?". Answered by the three-axis configuration in the capabilities companion
  • the configure walk in the tasks companion.
• "How do doca_flow_tune's server role and client / consumer role fit together inside the single artifact?" — worked example: "I keep reading about a Flow Tune server and a Flow Tune client; which binary am I running?". Answered by the one binary, two roles breakdown in the capabilities companion and the corresponding routing in the tasks companion.
• "How do I take a recommended parameter change from flow-tune back into my doca-flow application without breaking the dataplane?" — worked example: "the analyze step suggests a different table sizing for my pipe; how do I apply it?". Answered by the recommendation → minimum-diff modification of the Flow program loop in the tasks companion and the smoke-before-bulk rule in the tasks companion.
• "doca_flow_tune reports nothing / disagrees with the Flow app / cannot attach — what does that mean?" — worked example: "the tool runs but the visualize step produces an empty mermaid diagram". Answered by the layered error taxonomy in the capabilities companion
  • the tasks companion.

Audience

This skill serves external operators, performance engineers, DOCA Flow application developers, and AI agents who need to understand, characterize, or improve a running doca-flow pipeline's behavior on the user's actual install and device. Concretely:

• An operator wanting a read-only snapshot of which pipes exist and how their hardware / software counters progress before recommending any change.
• A performance engineer who has a measured baseline (for example a monitor-mode capture) and wants to turn measurement into optimization — pick a tuning axis and identify which knob in the doca-flow program is the lever.
• A Flow developer using the offline analyze + visualize loop to understand a pipe layout without re-instrumenting the program.
• An AI agent driving the "is this pipeline behaving as expected, and would a non-mutating tuning hint help" triage step.

It is not for users debugging the doca_flow_tune source code, not a substitute for the live public DOCA Flow Tune guide, not the place to learn the doca-flow API (see doca-flow/SKILL.md), and not a synthetic rule-rate benchmark: it measures the user's own pipeline.

doca_flow_tune is shipped as a single tool (one binary plus companion analyzer / visualizer scripts and JSON config templates) — the historical server and client roles live inside this one artifact, not in two executables. It uses the same kind: tool three-file shape as the rest of the bundle.

Language scope

This skill governs invocation, output interpretation, and recommendation-to-code-change routing for the DOCA Flow application doca_flow_tune observes. The tool is not a programming target — there is no public API to link against; the workflow is configure JSON, run, read outputs, propose minimum- diff changes to the surrounding doca-flow program. For the doca-flow API the recommendations route back into, see doca-flow/SKILL.md; for cross-language application patterns, see doca-programming-guide/SKILL.md.

When to load this skill

Load this skill when the user is — or the agent needs to — invoke doca_flow_tune against a running or planned doca-flow application to characterize, dump, visualize, analyze, or tune that pipeline. Concretely:

• Picking which role to engage (offline analyze / visualize on a captured config + state, online dumper / monitor against the live app, or attach-to-app server-role usage).
• Picking which tuning axis to ask about (rule placement, resource hints / table sizing, hardware-offload-mode).
• Picking which measurement axis to compare against (rule-install rate, lookup latency, hardware-counter delta) — not interchangeable; use the axis the baseline capture named.
• Capturing a documented before / after pair around a proposed Flow-program change (JSON config path, command line, DOCA version, device, environment, unredacted tool output).
• Diagnosing an empty session, a degenerate visualize diagram, or an analyze recommendation that contradicts live counters.

Do not load this skill for general DOCA orientation, Flow program API work, install, or pure measurement methodology — route to doca-public-knowledge-map/SKILL.md, doca-flow/SKILL.md, 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_flow_tune observes and recommends on: the unified-artifact decomposition (server role
  • client / consumer role inside one binary), the three-axis configuration model (tuning axis × measurement × scope: which pipe / port / app), the documented offline / online / attach modes, the JSON configuration-file shape (the flow_tune_cfg.json example in the public Flow Tune guide), the dumper / monitor / analyze / visualize output surfaces, the version overlay (this tool rides the doca-flow library version it observes; the canonical rules live in doca-version/SKILL.md), the layered error taxonomy (config-syntax / attach-failed / pipe-not-found / measurement-unsound / recommendation-unactionable / version / cross-cutting), the observability posture (the tool is an observability primitive for the Flow pipeline), and the safety policy that makes any mutating application of a recommendation high-stakes because the recommendation lands in live Flow state.
• the tasks companion — step-by-step workflows for the in-scope task verbs: install (route to setup; the binary is shipped), configure (the three-axis decision + JSON config + mode pick), build (route to install; the binary is shipped), modify (apply a recommendation back to the Flow program via minimum- diff), run (the snapshot → analyze → visualize flow), test (the eval loop — warm-up, steady-state, before / after pair, client / server / Flow version match), debug (walk the error taxonomy layer by layer), use (the agent-side workflow for consuming flow-tune output), plus a Deferred task verbs block and a Command appendix.

The skill assumes a host where DOCA is already installed (or the public NGC DOCA container is running) and a doca-flow application is already created and validated per the doca-flow/SKILL.md skill. Without those preconditions, the tune session has nothing to observe.

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:

• Verbatim flag inventories, subcommand names, JSON config field names, or default endpoint paths quoted as the contract. The public DOCA Flow Tune guide (reached via doca-public-knowledge-map ## DOCA tools (doca-public-knowledge-map/SKILL.md#doca-tools)) and the installed --help are the joint source of truth; the guide's configuration-file example is the second source for the JSON schema. Copying them here rots when the tool evolves.
• Pre-baked example output (dumper CSV columns, analyzer JSON field names, visualizer mermaid output). Output is install-, device-, firmware-, and version-specific; a captured example pinned to one platform misleads operators elsewhere.
• Wrappers, parsers, or scripts that consume flow-tune output. The right answer is to read the tool's own dump, analyze and visualize output files directly and quote them.
• Pre-baked tuning recommendations. Recommendations are install-, device-, firmware-, and workload-specific; the agent always re-derives them from the user's actual session.
• A samples/, templates/, or reference/ subtree. Mock tuning recipes read as production-grade and mislead.

Loading order

1. Read this skill first to confirm the user's question is in scope (the user actually wants to invoke doca_flow_tune against a doca-flow pipeline, not measure baseline perf or learn the Flow API).
2. For what doca_flow_tune observes, the one-binary / two-role decomposition, the three-axis model, the version overlay, the error taxonomy, observability surface, and safety posture, see the capabilities companion.
3. For the documented invocations and the snapshot → analyze → visualize → propose → smoke workflow — install, configure, build, modify, run, test, debug, use — see the tasks companion.

Related skills

• doca-flow/SKILL.md — the base library whose pipeline this tool observes and tunes. The pipe / entry / rule surface flow-tune reports on is created by doca-flow program code; recommendations route back into that program via the universal modify-a-sample workflow.
• doca-public-knowledge-map/SKILL.md — routing to the public DOCA Flow Tune page on docs.nvidia.com and the rest of the public DOCA documentation set.
• doca-version/SKILL.md — the canonical version-detection chain, four-way match, NGC semantics, and headers-win-over-docs rule. The ## Version compatibility overlay in this skill is a thin extension on top.
• doca-debug/SKILL.md — the cross-cutting debug ladder. Flow-tune surfaces its own error taxonomy; when the cause turns out to be below DOCA (driver, firmware, NUMA), the tune taxonomy hands off to doca-debug.
• doca-structured-tools-contract/SKILL.md — the bundle's detect → prefer → fall back → report contract. The Command appendix in the tasks companion honors it.
• doca-setup/SKILL.md — env preparation, install verification, hugepages, NUMA, and the I have no install yet path with the public NGC DOCA container.
• doca-hardware-safety/SKILL.md — the cross-cutting hardware-safety meta-policy this skill's ## Safety policy overlays.