Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →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. Tr
Passed 20 of 21 test cases with the skill, and 3 of 21 without it.
and 17 more in the full report
18 fixed3 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
We want to launch an interactive live online monitoring web service for our DOCA Flow application. Which top-level subcommand of `doca_flow_tune` serves this live browser-based monitoring role?
| Checked | Without skill | With skill |
|---|---|---|
| Names web as the top-level subcommand for live browser-based monitoring. | Missed | Passed |
The instructions your model reads when the skill loads.
doca_flow_tune)Subcommand surface correction (Run-12, verified Run-13 against doca/tools/flow_tune/src/tune/common/tune_config.cpp).
doca_flow_tuneis 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 viadoca_argp_cmd_set_name(...)intune_config.cpp(lines 1799 / 1860 / 1896 / 2074 / 2111);analyzefurther acceptsimport/export/packet_trace/sim_timingsub-subcommands. Thedump/monitor/websubcommands run the binary in server-attached online mode against a livedoca-flowapplication reached over a Unix- domain socket whose path lives innetwork.server_udsof the shippedflow_tune_cfg*.json; theanalyze/visualizesubcommands run in offline / captured-snapshot mode against JSON / CSV files the online modes previously dropped into the configuredoutputs_directory. The rest of this skill (andCAPABILITIES.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 ofdump/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 doca_flow_tune,
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. Open
CAPABILITIES.md when the question is *what
test cases passed, vs 3 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.