git clone --depth 1 https://github.com/NVIDIA/skills /tmp/doca-rdmi && cp -r /tmp/doca-rdmi/skills/doca-rdmi ~/.claude/skills/doca-rdmiSKILL.md
# DOCA RDMA Initiator **Where to start:** This skill assumes DOCA is already installed and the user is doing **hands-on RDMI work** on a host or BlueField with the DOCA package set that ships the `doca-rdmi` library. Open [`TASKS.md`](TASKS.md) if the user wants to *do* something (install / configure / build / modify / run / test / debug / use); open [`CAPABILITIES.md`](CAPABILITIES.md) when the question is *what can RDMI express on this version* — the object model, the DPA-side handle types, the relationship to `doca-rdma`, the EXPERIMENTAL-tag policy, and the safety overlay. **End-to-end "walk me through doca-rdmi" questions are answerable entirely from this skill.** Go straight to [`TASKS.md ## end-to-end (quickref)`](TASKS.md#end-to-end-quickref), which carries the self-contained install-check → device/cap discovery → sample → `pkg-config` build → run → debug walkthrough with the exact commands. You do **not** need to open `doca-setup` or `doca-programming-guide` to answer an RDMI build/run/debug question. Route to [`doca-setup`](../../doca-setup/SKILL.md) when the required DOCA prerequisites are absent, partial, or version-mismatched. ## Example questions this skill answers well The CLASSES of RDMI 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. - **"Should I use `doca-rdmi` or `doca-rdma` for this case?"** — worked example: *"I have a DPA kernel that needs to fire 1 MB RDMA writes at a remote responder; which library?"*. Answered by the *initiator-side vs general-purpose* selection rule in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) surface-selection table + the routing back to [`doca-rdma`](../doca-rdma/SKILL.md) when the use case is two-sided or host-CPU initiated. - **"How do I bring up an RDMI connection on the DPA datapath?"** — worked example: *"create a `doca_rdmi_connection`, attach a DPA completion context, hand the DPA-side handle to my kernel"*. Answered by the connection-object lifecycle in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the configure walk in [`TASKS.md ## configure`](TASKS.md#configure). - **"How do connection and poster relate — when do I need both?"** — worked example: *"my application receives work requests AND posts RDMA writes; do I need a `doca_rdmi_connection` plus a `doca_rdmi_poster`, or one of them?"*. Answered by the two-object model in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the modify-from-sample slot table in [`TASKS.md ## modify`](TASKS.md#modify). - **"How do I drive completions on the DPA side?"** — worked example: *"hook the connection to a `doca_dpa_completion` so my kernel polls completions directly"*. Answered by the DPA-side completion-attach pattern in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes) + the run-side wiring in [`TASKS.md ## run`](TASKS.md#run), cross-linked into [`doca-dpa`](../doca-dpa/SKILL.md) for the DPA programming surface itself. - **"Is the symbol I want available — and is it stable enough to ship?"** — worked example: *"is `doca_rdmi_poster_post` GA on my installed DOCA, or still EXPERIMENTAL?"*. Answered by the EXPERIMENTAL-tag policy in [`CAPABILITIES.md ## Version compatibility`](CAPABILITIES.md#version-compatibility) + the version-discovery rule (`pkg-config --modversion doca-rdmi`) pinned in [`TASKS.md ## configure`](TASKS.md#configure). - **"What does this `DOCA_ERROR_*` from a `doca_rdmi_*` call mean?"** — worked example: *"`DOCA_ERROR_BAD_STATE` from `doca_rdmi_connection_dpa_completion_attach`"*. Answered by the RDMI 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 DPA-resident DOCA applications that need to *initiate* one-sided RDMA operations against a remote responder** — i.e., users whose accelerator-side code wants to post sends, writes, or reads directly from the accelerator without round-tripping through the host CPU. The canonical caller is a DPA kernel that has been compiled with `doca-dpacc-compiler` and runs on the BlueField DPA datapath; a GPU-side caller that drives the DPU's RDMA queues is the sister case routed to [`doca-gpi`](../doca-gpi/SKILL.md). This skill is *not* for NVIDIA developers contributing to DOCA RDMI itself, and it is not the right surface for general host-CPU two-sided RDMA — that belongs to [`doca-rdma`](../doca-rdma/SKILL.md). ## Language scope DOCA RDMI ships as a C library with the `pkg-config` module name `doca-rdmi`. The library's *host-side* surface (`doca_rdmi_connection_*`, `doca_rdmi_poster_*`) is C; the *DPA-side* surface that the accelerator kernel uses is also C, compiled against the DOCA DPA toolchain documented in [`doca-dpa`](../doca-dpa/SKILL.md). Other-language consumers (Rust, Go, Python, …) consume the host-side `*.so` through FFI; the skill's contribution in that case is to keep the connection / poster lifecycle, the EXPERIMENTAL-tag policy, the DPA-side handoff rules, and the safety overlay language-neutral, and to route the agent to the public C ABI as the authoritative surface that any wrapper will eventually call. The DPA-side surface is *not* wrappable in another language — it is compiled and linked into the DPA binary itself. ## When to load this skill Load this skill when the user is doing **hands-on DOCA RDMI work** on a host or BlueField with DOCA installed. Concretely: - Deciding between `doca-rdmi` and `doca-rdma` for a new one-sided RDMA workload that runs from the DPA datapath. - Creating a `doca_rdmi_connect
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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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