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NVIDIAKnown publisherNVIDIA/TensorRT-Model-Connect/plugins/trtmc-agent-skills/skills/setup-trtmc-environment/SKILL.md

setup-trtmc-environment

Skill290 starsChanged 45 days agoApache-2.0View on GitHub

Prepare a TensorRT-Model-Connect development or validation environment from a fresh checkout on a host without a known working setup.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Start from the checkout and current repository documentation, not a remembered machine or container name.

    line 10
  2. Very specific:

    Prefer the existing apps/devtoolkit API for a checkout-owned environment.

    line 18
  3. Distinctive:

    Use its Docker path for the supported development image or its local path with an explicit existing Python interpreter.

    line 19
  4. Distinctive:

    Verify the checkout mount, Python import, TensorRT import, GPU visibility, runtime root, and requested native target before starting the …

    line 24
  5. Distinctive:

    Do not reuse, replace, or remove an unrelated container.

    line 27

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Set Up the Environment

Start from the checkout and current repository documentation, not a remembered machine or container name.

  1. Resolve the repository root and read website/docs/getting-started/source-build.md, Dockerfile, and apps/devtoolkit/README.md.
  2. Inspect host architecture, available disk, Docker access, GPU visibility, driver, and compute capability only as needed for the requested work.
  3. Prefer the existing apps/devtoolkit API for a checkout-owned environment. Use its Docker path for the supported development image or its local path with an explicit existing Python interpreter.
  4. Select one family when model work is requested. Install only that family's optional families/<family>/requirements.txt; a missing file means no family package step.
  5. Verify the checkout mount, Python import, TensorRT import, GPU visibility, runtime root, and requested native target before starting the real task.

Do not reuse, replace, or remove an unrelated container. Do not install GPU drivers, reconfigure the container runtime, or change runner settings without separate authorization. Stop and explain the unsupported host boundary when the documented paths do not apply.

Report environment preparation separately from compilation, tests, model correctness, performance, and deployment qualification.

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