Agent skill · NVIDIA
doca-flow-tune
Use this skill when the user is tuning a live or captured `doca-flow` pipeline with `doca_flow_tune` — snapshotting pipe / counter / KPI state, picking a tuning axis (rule placement, resource hints / table sizing, HW-offload mode) and a matching measurement (rule-install rate, lookup latency, hardware-counter delta), running offline or online (read-only or state-changing) modes, reading the dumper CSV / analyze JSON / visualize mermaid, or applying a recommendation back into the Flow program. Trigger even when the user does not explicitly mention "doca_flow_tune" — typical implicit phrasings include "Flow rule-install rate is low on BlueField", "table sizing looks wrong for this pipe", "tune visualize step is empty", "before/after counters don't move", or "which doca-flow knob does this recommendation hit". Refuse and route elsewhere for measuring baseline numbers (doca-flow-perf, doca-flow-dpa-perf), writing the doca-flow application, DOCA install, or streaming Flow telemetry — those belong to other skills.
What it needs
About 10k tokens when loaded.
What this skill does
DOCA Flow Tune (docaflowtune) Subcommand surface correction (Run-12, verified Run-13 against doca/tools/flowtune/src/tune/common/tuneconfig.cpp). docaflowtune is a single binary whose role on a given invocation is determined by which of five top-level subcommands the user picks — dump, monitor, web, analyze, visualize (case-insensitive on the CLI; uppercased in this skill for readability). All five names are registered via docaargpcmdsetname(...) in tuneconfig.cpp (lines 1799 / 1860 / 1896 / 2074 / 2111); analyze further accepts import / export / packettrace / simtiming sub-subcommands. The dump / monitor / web subcommands 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.serveruds of the shipped flowtunecfg.json; the analyze / visualize subcommands run in offline / captured-snapshot mode against JSON / CSV files the online modes previously dropped into the configured outputsdirectory. The rest of this skill (and CAPABILITIES.md / TASKS.md) uses the legacy "server role / online mode / offline mode" framing — that framing is internally consistent with the subcommand surface here: server role = a server-attached online subcommand (dump/monitor/web); online mode = any of dump/monitor/web; offline mode = analyze/visualize. Treat the subcommand 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 docaflowtune, the unified DOCA Flow tuning tool. Open TASKS.md 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/doca-flow-tune