git clone --depth 1 https://github.com/NVIDIA/skills /tmp/doca-rdma && cp -r /tmp/doca-rdma/skills/doca-rdma ~/.claude/skills/doca-rdmaSKILL.md
# DOCA RDMA ## Non-negotiable: the deliverable uses DOCA RDMA, not raw verbs When this skill is in scope, the user is asking for **DOCA RDMA**. The program you produce **must link `libdoca_rdma` and call the `doca_rdma_*` API** (directly in C/C++, or through a thin FFI/cgo shim from another language). Do **NOT** implement the request with raw `libibverbs` / `librdmacm` / RDMA-CM and call it done. Those move bytes but completely bypass DOCA — which defeats the entire purpose of using this library, loses the DOCA programming model (progress engine, task/ event lifecycle, capability discovery, portability across BlueField/ ConnectX generations), and is the single most common failure mode. "Raw verbs is fewer lines" / "it avoids building a DOCA binding layer" is **not** an acceptable reason to bypass DOCA. The correct low-friction path for a non-C language (Go, Rust, Python, …) is **not** to re-bind the whole API — it is to start from a **shipped DOCA RDMA sample** under `$(pkg-config --variable=prefix doca)/samples/doca_rdma/` (substitute the module resolved on the target) and wrap its entry functions in a **thin** cgo/FFI shim built with `#cgo pkg-config: doca` (Go) or the equivalent. That shim is a single small file, not "a large custom binding layer". See [`TASKS.md ## build`](TASKS.md#build) Step 0 and [`TASKS.md ## modify`](TASKS.md#modify). If `pkg-config doca` or the DOCA build fails, **fix the build** (module name, `PKG_CONFIG_PATH`, sample path) — do not silently fall back to verbs. A binary whose `ldd` shows no `libdoca_rdma` is a failed DOCA-RDMA task, regardless of whether bytes moved. **Where to start:** This skill assumes DOCA is already installed and the user is doing **hands-on RDMA work** on a BlueField / ConnectX / host with DOCA. Open [`TASKS.md`](TASKS.md) if the user wants to *do* something (configure / build / modify / run / test / debug); open [`CAPABILITIES.md`](CAPABILITIES.md) when the question is *what can RDMA express* on this version. If the user has not installed DOCA yet, route to [`doca-setup`](../../doca-setup/SKILL.md) first. ## Example questions this skill answers well The CLASSES of RDMA 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. - **"How do I bring up an RDMA context and connect two sides?"** — worked example: *"set up sender + receiver with RDMA CM on a single host for first-run testing"*. Answered by the lifecycle + connection workflow in [`TASKS.md ## configure`](TASKS.md#configure) + [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) connection-method selection. - **"Which RDMA task type fits this data-movement pattern?"** — worked example: *"one-sided write + completion via Send-with-Immediate for a small control message"*. Answered by the task taxonomy in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the task-config workflow in [`TASKS.md ## modify`](TASKS.md#modify). - **"What mmap permissions does this task need? Do I have to export the mmap?"** — worked example: *"my Read task fails with insufficient permissions"*. Answered by the permission matrix in [`CAPABILITIES.md ## Safety policy`](CAPABILITIES.md#safety-policy) + the mmap-export checklist in [`TASKS.md ## test`](TASKS.md#test). - **"Is this RDMA capability supported on my device + transport?"** — worked example: *"does this device support Atomic Compare-and-Swap over RoCE"*. Answered by the capability-query rule (`doca_rdma_cap_task_*_is_supported` against a `doca_devinfo`) in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the discovery step in [`TASKS.md ## configure`](TASKS.md#configure). - **"Is this RDMA API available on my installed DOCA version?"** — worked example: *"is RDMA CM in DOCA 2.6.0"*. Answered by the version-compatibility section in [`CAPABILITIES.md ## Version compatibility`](CAPABILITIES.md#version-compatibility) + the version-discovery rule (`pkg-config --modversion doca`) pinned in [`TASKS.md ## configure`](TASKS.md#configure). - **"What does this `DOCA_ERROR_*` from an RDMA call mean and which layer caused it?"** — worked example: *"`DOCA_ERROR_BAD_STATE` from `doca_rdma_connection_disconnect`"*. Answered by the RDMA overlay on the cross-library taxonomy in [`CAPABILITIES.md ## Error taxonomy`](CAPABILITIES.md#error-taxonomy) + the layered ladder in [`TASKS.md ## debug`](TASKS.md#debug) that escalates to [`doca-debug`](../../doca-debug/SKILL.md). ## Audience This skill serves **external developers building applications that consume the DOCA RDMA library** — i.e., users whose code calls `doca_rdma_*` (directly in C/C++, or through FFI/bindings from another language) to do RDMA data movement between two sides (host↔host, host↔BlueField, DPU↔DPU, or SF↔SF on a BlueField). It is *not* for NVIDIA developers contributing to DOCA RDMA itself. **Language scope.** DOCA RDMA normally ships as a C library *inside the umbrella `doca` pkg-config module* (public header `doca_rdma.h`, shared object `libdoca_rdma.so`); split installs may expose a per-library module. Always discover the module on the target (`pkg-config --list-all | grep -i doca`) rather than assuming either layout. The shipped samples are written in C (NVIDIA's choice). C and C++ consumers are the canonical case and the worked examples in `TASKS.md` assume that path. Other-language consumers (Rust, Go, Python, …) consume the same `*.so` through FFI or language-specific bindings; the skill's contribution in that case is to keep the lifecycle, capability-discovery, permission-matrix, error-taxonomy, and connection-method guidance language-neutral, and to route the agent to the public C ABI as the authoritative surface that any wrapper will eventually call. **The non-C deliverable is still a DOCA
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Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
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