Individual skill

nv-segment-ct

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Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.

skills/nv-segment-ct/SKILL.md

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Observed behavior: Network or broad shell
Last checked: Nov 9, 2026

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Repository stars (not skill ratings): 3,911
Repository last updated: Nov 6, 2026
License: Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

agent-skills Repository topic
agentic-ai Repository topic
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codex Repository topic
coding-agents Repository topic
cuda Repository topic
developer-tools Repository topic
llm Repository topic
omniverse Repository topic
medtech Declared skill tag
ct Declared skill tag
segmentation Declared skill tag
engineering Derived category

Files and excerpt

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BENCHMARK.mddoc · 3,982 bytes
SKILL.mdcore · 8,360 bytes
SKILL.mdcore · 8,360 bytes
SKILL.mdcore · 8,360 bytes
SKILL.mdcore · 8,360 bytes
evals/evals.jsonasset · 1,272 bytes
fixtures/fetch_spleen_fixture.pyruns · 5,319 bytes
fixtures/generate_preflight_fixture.pyruns · 1,919 bytes
scripts/run_vista3d.pyruns · 11,070 bytes
skill-card.mddoc · 4,211 bytes
skill.oms.sigasset · 6,025 bytes
skill_manifest.yamlasset · 8,493 bytes
tests/test_run_vista3d.pyruns · 3,081 bytes
validators/output_schema.jsonasset · 3,734 bytes

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# NV-Segment-CT

## Purpose
- Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence. Not for clinical interpretation.
- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
- Manifest I/O: inputs are `ct_volume`; outputs are `label_map` and `result_json`.

## Instructions
- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.
- Run `scripts/run_vista3d.py` through the documented command below; keep outputs under a caller-provided run directory.
- If a host agent exposes `run_script`, use `run_script("scripts/run_vista3d.py", args=[...])`; otherwise run the Bash/Python command shown below.
- Create the documented Python 3.10 virtual environment and invoke its binaries directly; do not install model dependencies into the caller's active environment.
- Check the emitted JSON and paired verifier guidance before treating the run as evidence.