Medtech Model Evidence Export
NVIDIAOfficialCreated by NVIDIA MedTech
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The Medtech Model Evidence Export skill provides a structured workflow for mirroring medical AI inference results or evidence packs into MLflow. It is designed for post-hoc documentation, allowing users to log metadata, parameters, reproducibility details, and quality metrics from completed inference runs. The tool emphasizes data privacy by requiring sanitization of identifiers and provides configurable artifact policies to control the upload of sensitive medical images.
What it does
- Exports inference metadata, parameters, and quality metrics to MLflow.
- Supports dry-run mode for inspecting data before final export.
- Calculates and logs intensity statistics and volume metrics for medical imaging.
- Provides configurable artifact policies to manage the upload of previews or raw files.
- Generates human-readable run summaries in MLflow notes.
- Integrates with local MLflow stores or remote Databricks workspaces.
When to use it
- After completing a medical AI inference run or generating an evidence pack.
- When you need to track reproducibility and quality metrics for inference experiments.
- When you need to centralize inference results in an MLflow-compatible environment.
- When you need to perform engineering checks on inference outputs without clinical validation.
Advantages
- Maintains the original evidence pack as the source of truth.
- Includes built-in safety checks for sensitive medical data.
- Provides flexible artifact management to prevent accidental exposure of raw images.
- Automates the extraction of relevant quality metrics from NIfTI files.
Usage examples
- “Preview the export of my latest inference run without uploading to MLflow.”
- The agent runs the export script in dry-run mode, displaying the parameters and metrics that would be logged without modifying any remote state.
- “Export my inference results to the local MLflow store.”
- The agent executes the export script in local mode, creating an MLflow run with the provided configuration and reproducibility metadata.
- “Upload my inference pack to Databricks including slice previews.”
- The agent performs the export to the specified Databricks workspace, applying the preview artifact policy and confirming the medical image upload.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/medtech-model-evidence-export/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip medtech-model-evidence-export.zip -d .agents/skills
Documentation by NVIDIA MedTech
Medtech Model Evidence Export to MLflow
Purpose
Mirror an existing medical-inference result or evidence pack into MLflow after
the run and emit the export_result JSON contract. Keep the original evidence
pack as the source of truth. Training skills should add MLflow inside their
training loops instead.
Instructions
Run
scripts/export_evidence_pack.pyin the defaultdry-runmode.Inspect
params,metrics,artifact_plan, andmlflow.note.content.Choose
--mode localor--mode databricksonly after checking the target.Keep
--artifact-policy metadataunless the target is approved for images.For
previeworallin a live mode, also pass--confirm-medical-artifact-upload.Keep
--source-ref,--note, config filenames, and artifact filenames free of patient or secret identifiers; always review the dry-run output first.
Hosts with a script helper can use
run_script("scripts/export_evidence_pack.py", args=["PACK_OR_RESULT", "--mode", "dry-run"]).
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/export_evidence_pack.py | Export post-hoc inference evidence through MLflow. | PACK_OR_RESULT --mode dry-run --artifact-policy metadata |
Prerequisites
Python 3.10+.
mlflow>=2.10,<4forlocalordatabricksmode.numpy>=1.24,<3andnibabel>=4,<6for NIfTI quality metrics and previews.MLFLOW_TRACKING_URImay select a caller-managed tracking server.Databricks mode uses the caller's
DATABRICKS_HOST,DATABRICKS_TOKEN, or configured Databricks profile. The declared network endpoint ishttps://<caller-provided-mlflow-or-databricks-workspace>; Docker and GPU are not required.Local mode may write the MLflow store under
<current-working-directory>/mlruns.
Examples
Preview the export without contacting MLflow:
python skills/medtech-model-evidence-export/scripts/export_evidence_pack.py \
runs/inference_pack --mode dry-run --artifact-policy metadataExport a direct NV-Generate result with reproducibility metadata:
python skills/medtech-model-evidence-export/scripts/export_evidence_pack.py \
runs/nv-generate/result.json \
--mode local \
--experiment-name medical-ai-inference \
--config configs/chest_lung_tumor.json \
--seed 0 \
--source-ref git:61c4ec709b84cad468852243c48e250bec732074Log downsampled slice previews, but not raw NIfTI files:
python skills/medtech-model-evidence-export/scripts/export_evidence_pack.py \
runs/nv-generate/result.json \
--mode databricks \
--experiment-name /Shared/medical-ai-inference \
--artifact-policy preview \
--confirm-medical-artifact-upload--artifact-policy all additionally uploads discovered or explicitly supplied
NIfTI images and masks, subject to --max-artifact-mb. Use --image and
--mask when paths are not present in the result JSON.
The exporter logs:
scalar run and quality metrics, including sampled HU mean/std/min/max for CT (generic intensity statistics otherwise), a documented intensity-SNR heuristic, mask foreground percentage, and mapped tumor volume percentage when a tumor label mapping is available;
generation parameters, model/checkpoint identity, RNG seed, and recipe hash;
source config digest or
--source-ref, plus a prompt digest when present;mlflow.note.contentwith a short human-readable run summary;a sanitized metadata bundle by default, optional PNG slice previews, and raw image/mask artifacts only under the explicit
allpolicy.
Limitations
This is post-hoc inference export, not live training-curve tracking.
Global intensity SNR and downsampled volume statistics are engineering checks, not image-quality or clinical-performance claims.
Preview and raw artifacts may contain sensitive medical information. The caller must approve the destination and data policy before upload.
The exporter does not evaluate model quality, register models, or alter the source evidence pack.
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
| Evidence source not recognized | No direct result JSON or pack manifest.json. | Pass the result file, evidence-pack directory, or trusted-run root. |
| MLflow import fails | Live mode lacks the declared package. | Install mlflow>=2.10,<4 or use --mode dry-run. |
| Preview/all confirmation error | A live image upload was not acknowledged. | Review the destination, then pass --confirm-medical-artifact-upload. |
| Referenced image not found | Result paths moved after inference. | Pass current paths with --image and --mask. |
Files
- ATTRIBUTION.mdAdded by AutoGineer953 B
- BENCHMARK.md7.7 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.0 KiB
- evals/evals.json2.4 KiB
- fixtures/sample_pack/integrity_check.json62 B
- fixtures/sample_pack/manifest.json229 B
- fixtures/sample_pack/output.json1.5 KiB
- fixtures/sample_pack/runtime_profile.json108 B
- fixtures/sample_pack/validation_summary.json121 B
- fixtures/sample_result.json1.8 KiB
- scripts/export_evidence_pack.pyScript32.0 KiB
- skill-card.md4.1 KiB
- skill.oms.sig6.9 KiB
- skill_manifest.yaml3.7 KiB
- tests/test_export_evidence_pack.pyScript8.6 KiB
- validators/output_schema.json3.2 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Needs Python
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
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This skill includes scripts that your agent may run. Read them before you use it.
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Attribution and licence
- Created by
- NVIDIA MedTech
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/medtech-model-evidence-export · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA MedTech or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download medtech-model-evidence-export.zip