Build Applications on NVIDIA BlueField Faster with NVIDIA DOCA Agent Skills

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AI agents are becoming a standard part of development workflows, but general-purpose agents weren’t built with specialized infrastructure software such as NVIDIA DOCA in mind. Without domain-specific knowledge, agents may fall back on guesswork. This is an issue in infrastructure development because every correction cycle takes time away from deployment.

DOCA is the unified software platform that unlocks the full potential of NVIDIA BlueField data processing units (DPUs) for agentic AI infrastructure. It spans accelerated networking, AI-native storage, in-silicon security, telemetry, and lifecycle management. It’s the development platform for teams building on NVIDIA BlueField.

DOCA AI agent skills are now available on GitHub. These skills provide a structured, verified foundation to address where general-purpose AI agents fall short on DOCA development. They provide agents with verified API signatures, hardware capability requirements, and build constraints.

This post explains what DOCA AI agent skills are and how they perform across four impactful DOCA development scenarios. It includes a side-by-side demo and how to get started using DOCA agent skills to build faster and ship more stable code on BlueField infrastructure.

DOCA AI agent skills are a standardized way to give AI agents new capabilities and expertise. The lightweight, open format is built around a SKILL.md file containing real API signatures, hardware capability requirements, and build constraints. Together, the skills provide agents with a reasoning and operational framework for DOCA.

Skills span the full DOCA library, including Flow, GPUNetIO, PCC, and more. They don’t replace the agent, but give it the domain knowledge to reason like an experienced DOCA developer.

Each skill is scoped to a specific DOCA component or workflow. When an agent loads a skill for DOCA Flow, for example, it gets the real function signatures, the correct pkg-config module names, the build-container constraints, and the common failure modes and their mitigations. The skills are not just a summary of documentation, but a machine-readable specification the agent can reason against directly.

When you ask a general-purpose AI agent to set up a DOCA Comch (Comm Channel), configure an RDMA context, or debug a link failure in a DOCA Flow program, the agent is working from pattern-matching across general training data—not from verified DOCA API contracts, hardware capability manifests, or build system specifications. The DOCA library surface is large, rapidly evolving, and hardware-specific in ways that general training data does not capture.

There is no machine-readable contract for agents to reason from, no guaranteed stable interface between what the agent knows and what the hardware and software actually support.

To quantify the gap, the NVIDIA team ran 65 real DOCA developer prompts comparing agent performance with and without the skills. These