git clone --depth 1 https://github.com/NVIDIA/skills /tmp/doca-programming-guide && cp -r /tmp/doca-programming-guide/skills/doca-programming-guide ~/.claude/skills/doca-programming-guideSKILL.md
# DOCA programming guide **Where to start:** Read [`## Audience`](#audience) to confirm the user is *consuming* DOCA, not *contributing* to it. Then jump to the H2 that matches the verb (`## modify` for first-app derivation, `## build` for the canonical build pattern, `## test` for the test loop, `## debug` for the program-class debug ladder). ## Example questions this skill answers well These are the CLASSES of program-class questions the skill is built to answer, each with one worked example. Library-specific overlays (Flow / DMS / Caps / …) live in the matching library skill; this skill answers the library-agnostic shape. - **"How do I write my first DOCA program for <any library>?"** — worked example: *"I want to write my first DOCA Flow application."* Answered by the modify-a-shipped-sample workflow in [`TASKS.md ## modify`](TASKS.md#modify) plus the canonical build pattern in [`TASKS.md ## build`](TASKS.md#build). - **"What's the right build line for any DOCA library?"** — worked example: *"How do I compile a program that calls `doca_rdma_*`?"* Answered by the `pkg-config doca-<library>` pattern in [`TASKS.md ## build`](TASKS.md#build) (C/C++ Track 1) and the FFI/bindings pattern in Track 2. - **"What's the lifecycle every DOCA object follows?"** — worked example: *"What's the right order of `doca_flow_pipe_*` calls in my program?"* Answered by the cfg-create / init / start / use / stop / destroy template in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes). - **"`DOCA_ERROR_*` came back — what does it mean and what do I do?"** — worked example: *"My code got `DOCA_ERROR_BAD_STATE`."* Answered by the cross-library `doca_error_get_descr()` rule in [`CAPABILITIES.md ## Error taxonomy`](CAPABILITIES.md#error-taxonomy) + the program-class debug order in [`TASKS.md ## debug`](TASKS.md#debug). - **"My program built and started, but does nothing on the wire."** — worked example: *"My Flow program runs cleanly but no traffic is matched."* Answered by the validate-before-commit rule in [`CAPABILITIES.md ## Safety policy`](CAPABILITIES.md#safety-policy) and the layered program-class debug ladder in [`TASKS.md ## debug`](TASKS.md#debug). - **"What does <language> consumer of DOCA look like (FFI / bindings)?"** — worked example: *"How do I call DOCA Comch from Rust without writing C?"* Answered by Track 2 of [`TASKS.md ## build`](TASKS.md#build) (FFI against the public C ABI) and the language-neutral lifecycle in [`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes). - **"How should I classify and build all the shipped DOCA samples and applications? What's the difference between a sample and an application?"** — worked example: *"I tried to build all DOCA apps and 20/159 failed — which ones are real regressions vs missing optional stacks?"* Answered by the sample-vs-application model and the category / dependency / skip-vs-fail taxonomy in [`TASKS.md ## sample-and-app-categorization`](TASKS.md#sample-and-app-categorization), which separates *"the SDK is broken"* from *"the optional GPU / RMAX / MPI stack is not on this BlueField"*. If the question is env-class (install / build env / hugepages / devices), route to [`doca-setup`](../doca-setup/SKILL.md). If it is library-specific (Flow pipe topology, RDMA QP setup, DMS service deploy), layer the matching library skill on top. ## Audience This skill serves **external developers building applications that *consume* DOCA libraries** — i.e., users whose code calls one or more `doca_<library>_*` symbols (directly in C/C++, or through FFI / bindings from another language). It is *programming **with** DOCA*, not *programming **of** DOCA*: it is *not* for NVIDIA developers contributing to DOCA itself, and it does *not* assume access to the DOCA source tree, internal NVIDIA tooling, or any non-public information. The only inputs it ever points the agent at are the ones any external user has: the public docs at [`docs.nvidia.com/doca/sdk/`](https://docs.nvidia.com/doca/sdk/), the public catalog at [`catalog.ngc.nvidia.com`](https://catalog.ngc.nvidia.com/), the public GitHub repos under [`github.com/NVIDIA`](https://github.com/NVIDIA) / [`github.com/NVIDIA-DOCA`](https://github.com/NVIDIA-DOCA), the public developer forum, and the on-disk `/opt/mellanox/doca` tree that the public DOCA install (or the public NGC DOCA container, `nvcr.io/nvidia/doca/doca`) puts on the user's host. *Where to find* and *how to install* questions are routed elsewhere — see *Related skills* below. **Language scope.** DOCA itself is a C library family; every shipped sample in `/opt/mellanox/doca/samples/` and every shipped reference application in `/opt/mellanox/doca/applications/` is C. C and C++ consumers are the canonical case for every prescriptive workflow in this skill. Other-language consumers (Rust, Go, Python, …) consume the same `*.so` libraries through FFI or language-specific bindings against the public C ABI; the skill keeps the lifecycle, capability, error, observability, and safety guidance language-neutral, and routes the language-specific build / FFI work back to the consumer's own toolchain without authoring wrappers. ## When to load this skill Load this skill when the user has DOCA installed *and* the env-class preconditions are already satisfied (i.e., [`doca-setup`](../doca-setup/SKILL.md) has produced a clean install where `pkg-config doca-<library>` resolves, hugepages are mounted, and devices are visible), and is now asking a question about **how to actually program against DOCA** in a library-agnostic way: - Understanding what DOCA's pieces are (libraries, apps, services, tools) and which side of the wire they run on. - The canonical `pkg-config` + meson build pattern any DOCA application follows, regardless of which library it consumes. - The universal *derive a custom first application from
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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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