How do I install the Ct Segmentation Quality V1 skill?

Run npx skillmds@latest add nvidia-medtech/ct-segmentation-quality-v1 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ct Segmentation Quality V1 safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ct Segmentation Quality V1?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ct Segmentation Quality V1 free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published Ct Segmentation Quality V1?

NVIDIA-Medtech (@nvidia-medtech) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ct Segmentation Quality V1 by nvidia-medtech

Name: Ct Segmentation Quality V1
Rating: 4.2 (5 reviews)
Author: NVIDIA-Medtech

Ct Segmentation Quality V1

Audits nv_segment_ct evidence packs for label-map readability, anatomy plausibility (organ volume bounds, fragmentation, bilateral symmetry, liver-larger-than-spleen), and optional per-class Dice/IoU against a referenced ground-truth label map. Engineering verification only.

File contents

ct_segmentation_quality_v1

Purpose

Audits nv_segment_ct evidence packs for label-map readability, anatomy plausibility, requested-label containment, and optional Dice/IoU against a recorded ground-truth label map.
Use this after the source segmentation skill has produced an evidence pack. Engineering verification only.
Manifest I/O: inputs are nv_segment_ct_evidence_pack; outputs are ct_segmentation_quality_report.

Instructions

Run scripts/grade.py on the evidence-pack directory that should be audited.
If a host agent exposes run_script, use run_script("scripts/grade.py", args=["RUNS/PACK_DIR"]).
Prefer the eval-engine command when you need a verifier evidence pack; use the direct Python command for quick local inspection.

Available Scripts

Script	Purpose	Arguments
`scripts/grade.py`	Primary verifier entrypoint declared by `skill_manifest.yaml`.	`EVIDENCE_PACK_DIR`

Prerequisites

The target pack must contain manifest.json, validation_summary.json, and output.json.
The label-map path recorded in output.path must resolve from the pack directory or current repo root.

Limitations

Single-pack verifier only; dataset-level aggregation belongs in benchmarks/.
Anatomy bounds are engineering floors and may fail pediatric, post-resection, or pathological cases intentionally.
Not for clinical interpretation.

Troubleshooting

Error	Cause	Fix
Missing pack files	The fixture path is not an evidence-pack directory.	Point the verifier at the completed source-skill run directory.
Label map unreadable	`output.path` is missing or stale.	Preserve source-skill artifacts with the evidence pack before auditing.
Plausibility failure	Output violates an engineering floor.	Keep the failed verifier output and inspect per-tier reasons.

Paired verifier for skills/nv-segment-ct.

python eval_engine/run.py verifiers/ct_segmentation_quality_v1 \
  --fixture runs/spleen_clean \
  --out runs/spleen_clean_quality

The verifier reads the target pack's manifest.json, validation_summary.json, and output.json. It loads the label-map NIfTI referenced at output.path, recomputes per-class voxel counts and converts to volumes using the recorded spacing. It then runs three tiers:

artifact_inventory — label-map file exists, integer dtype, shape and affine match the recorded input geometry.
anatomy_plausibility — per-class volume bounds (e.g. spleen 50-500 mL), largest connected component dominates, fragmentation cap, bilateral organ symmetry, and liver_volume > spleen_volume when both are present.
gt_metrics — runs only if input.ground_truth_path is recorded in the evidence pack and the file is readable. Computes per-class Dice and IoU against the reference and checks per-class Dice floors.

Per-class anatomy bounds live in validators/anatomy_bounds.json. Bounds are population-typical adult ranges; pediatric or post-resection cases will fail intentionally for v0.1.

This is a single-pack verifier. Dataset-level aggregation (mean Dice across N cases) belongs in benchmarks/.

Not for clinical interpretation.

---name: ct-segmentation-quality-v1description: Audits nv_segment_ct evidence packs for label-map readability, anatomy plausibility (organ volume bounds, fragmentation, bilateral symmetry, liver-larger-than-spleen), and optional per-class Dice/IoU against a referenced ground-truth label map. Engineering verification only.license: Apache-2.0---# ct_segmentation_quality_v1## Purpose- Audits `nv_segment_ct` evidence packs for label-map readability, anatomy plausibility, requested-label containment, and optional Dice/IoU against a recorded ground-truth label map.- Use this after the source segmentation skill has produced an evidence pack. Engineering verification only.- Manifest I/O: inputs are `nv_segment_ct_evidence_pack`; outputs are `ct_segmentation_quality_report`.## Instructions- Run `scripts/grade.py` on the evidence-pack directory that should be audited.- If a host agent exposes `run_script`, use `run_script("scripts/grade.py", args=["RUNS/PACK_DIR"])`.- Prefer the eval-engine command when you need a verifier evidence pack; use the direct Python command for quick local inspection.## Available Scripts| Script | Purpose | Arguments ||---|---|---|| `scripts/grade.py` | Primary verifier entrypoint declared by `skill_manifest.yaml`. | `EVIDENCE_PACK_DIR` |## Prerequisites- The target pack must contain `manifest.json`, `validation_summary.json`, and `output.json`.- The label-map path recorded in `output.path` must resolve from the pack directory or current repo root.## Limitations- Single-pack verifier only; dataset-level aggregation belongs in `benchmarks/`.- Anatomy bounds are engineering floors and may fail pediatric, post-resection, or pathological cases intentionally.- Not for clinical interpretation.## Troubleshooting| Error | Cause | Fix ||---|---|---|| Missing pack files | The fixture path is not an evidence-pack directory. | Point the verifier at the completed source-skill run directory. || Label map unreadable | `output.path` is missing or stale. | Preserve source-skill artifacts with the evidence pack before auditing. || Plausibility failure | Output violates an engineering floor. | Keep the failed verifier output and inspect per-tier reasons. |Paired verifier for `skills/nv-segment-ct`.```bashpython eval_engine/run.py verifiers/ct_segmentation_quality_v1 \ --fixture runs/spleen_clean \ --out runs/spleen_clean_quality```The verifier reads the target pack's `manifest.json`, `validation_summary.json`,and `output.json`. It loads the label-map NIfTI referenced at `output.path`,recomputes per-class voxel counts and converts to volumes using the recordedspacing. It then runs three tiers:- **artifact_inventory** — label-map file exists, integer dtype, shape and affine match the recorded input geometry.- **anatomy_plausibility** — per-class volume bounds (e.g. spleen 50-500 mL), largest connected component dominates, fragmentation cap, bilateral organ symmetry, and `liver_volume > spleen_volume` when both are present.- **gt_metrics** — runs only if `input.ground_truth_path` is recorded in the evidence pack and the file is readable. Computes per-class Dice and IoU against the reference and checks per-class Dice floors.Per-class anatomy bounds live in `validators/anatomy_bounds.json`. Bounds arepopulation-typical adult ranges; pediatric or post-resection cases will failintentionally for v0.1.This is a single-pack verifier. Dataset-level aggregation (mean Dice acrossN cases) belongs in `benchmarks/`.Not for clinical interpretation.

Frequently asked questions