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效率与协作 · SKILL.md

runtime-configuration

runtime-configuration

【效率与协作】Install, inspect, and configure TotalSegmentator runtime dependencies, devices, weights, licenses, offline caches, and usage statistics.

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name: runtime-configuration description: "Install, inspect, and configure TotalSegmentator runtime dependencies, devices, weights, licenses, offline caches, and usage statistics." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Runtime Configuration

Use this sub-skill when an agent needs to make TotalSegmentator importable, choose a backend device, inspect local runtime state, pre-stage model weights, configure offline/license behavior, or disable usage telemetry. It owns setup and diagnostics, not segmentation command construction.

Start Here

  • Install with pip install TotalSegmentator; verify with TotalSegmentator --version, totalseg_info --json, or the bundled checker.
  • Run python scripts/check_totalseg_runtime.py --task total --device cpu --json for a safe smoke check that imports metadata/registry/config helpers, checks PyTorch visibility, and never downloads weights or runs models.
  • Read references/install-and-backends.md for Python/PyTorch requirements, optional dependencies, and cpu/gpu/gpu:N/mps selection.
  • Read references/configuration.md for TOTALSEG_HOME_DIR, TOTALSEG_WEIGHTS_PATH, config.json, weights, offline staging, licenses, and usage-stat settings.
  • Read references/troubleshooting.md when imports, CUDA/MPS, offline weights, licenses, preview rendering, optional dependencies, or config isolation fail.

Routing Boundaries

Runtime Rules

  • Prefer totalseg_info and totalsegmentator.registry for capability decisions; they are safe, fast, and do not load models.
  • Do not log license numbers, absolute local paths, API keys, or machine-specific environment details in shared outputs.
  • Treat model downloads as explicit side effects; only run totalseg_download_weights when the user expects network and storage use.
  • Use the bundled checker for diagnostics before running expensive segmentation, especially in offline or service environments.
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