LaminDB Biological Data Management
davila7/claude-code-templates
Manages biological datasets with LaminDB: versioned artifacts, run lineage, ontology-based annotation, schema validation and links to workflow managers and ML tools.
Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow.
Install Medtech Model Evidence Export
$ npx skills add NVIDIA/skills --skill medtech-model-evidence-export -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills medtech-model-evidence-export --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/medtech-model-evidence-export .claude/skills/medtech-model-evidence-export && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "medtech-model-evidence-export" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/medtech-model-evidence-export into .claude/skills/medtech-model-evidence-export/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "medtech-model-evidence-export", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/medtech-model-evidence-exportType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill medtech-model-evidence-export -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills medtech-model-evidence-export --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/medtech-model-evidence-export .agents/skills/medtech-model-evidence-export && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "medtech-model-evidence-export" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/medtech-model-evidence-export into .agents/skills/medtech-model-evidence-export/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "medtech-model-evidence-export", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill medtech-model-evidence-export -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills medtech-model-evidence-export --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/medtech-model-evidence-export .cursor/skills/medtech-model-evidence-export && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "medtech-model-evidence-export" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/medtech-model-evidence-export into .cursor/skills/medtech-model-evidence-export/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "medtech-model-evidence-export", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/medtech-model-evidence-export--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill medtech-model-evidence-export -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills medtech-model-evidence-export --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/medtech-model-evidence-export .gemini/skills/medtech-model-evidence-export && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "medtech-model-evidence-export" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/medtech-model-evidence-export into .gemini/skills/medtech-model-evidence-export/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "medtech-model-evidence-export", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills medtech-model-evidence-exportInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill medtech-model-evidence-export -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/medtech-model-evidence-export .github/skills/medtech-model-evidence-export && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "medtech-model-evidence-export" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/medtech-model-evidence-export into .github/skills/medtech-model-evidence-export/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "medtech-model-evidence-export", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill medtech-model-evidence-export -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills medtech-model-evidence-export --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/medtech-model-evidence-export .opencode/skills/medtech-model-evidence-export && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "medtech-model-evidence-export" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/medtech-model-evidence-export into .opencode/skills/medtech-model-evidence-export/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "medtech-model-evidence-export", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
medtech-model-evidence-export- GitHub stars
- 3.5k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 470 words
- Files
- 15 (incl. scripts)
- Skills in repo
- 380
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow.
- Works in 6 steps: Run scripts/export_evidence_pack.py in… → Inspect params, metrics, artifact_plan,… → Choose --mode local or --mode databricks… → …
- Tasks that involve Reproducible research
- SKILL.md covers Purpose, Instructions, Available Scripts and Prerequisites, plus 3 more sections
- Runs Python scripts from its folder; calls python; needs DATABRICKS_TOKEN
What it does
Medtech Model Evidence Export is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Use after inference, including NV-Generate runs; not for live training tracking, model registration, or clinical use.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 20 other files, including scripts (for example `BENCHMARK.md`, `evals/evals.json` and `fixtures/sample_pack/integrity_check.json`).
It sits in Research & Science, covering Reproducible research. It works with MLflow and Databricks. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Reproducible research
Example prompts
- “Use the medtech-model-evidence-export skill to export sanitized metadata, parameters, reproducibility details, quality metrics, and optional review…”
- “/medtech-model-evidence-export”
Requirements
- Python 3
- Docker
- A credential in DATABRICKS_TOKEN
- Pre-approved tools (allowed-tools): Bash
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Run scripts/export_evidence_pack.py in the default dry-run mode.
- Inspect params, metrics, artifact_plan, and mlflow.note.content.
- Choose --mode local or --mode databricks only after checking the target.
- Keep --artifact-policy metadata unless the target is approved for images.
- For preview or all in a live mode, also pass
- Keep --source-ref, --note, config filenames, and artifact filenames free
What it can do on your machine
Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
Bash
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
DATABRICKS_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Medtech Model Evidence Export loads about 1.3k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 470 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
allowed-tools: Bash
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 470 words, ~1,285 tokens.
Download SKILL.mdSave it as
.claude/skills/medtech-model-evidence-export/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.- name
- medtech-model-evidence-export
- description
- Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Use after inference, including NV-Generate runs; not for live training tracking, model registration, or clinical use.
- allowed-tools
- Bash
- license
- Apache-2.0
- permissions
- env, file_read, file_write, network, shell
- metadata.author
- NVIDIA MedTech <noreply@nvidia.com>
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.
Show full SKILL.md (131 more words)Show less
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. |
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 14 other files (scripts) in skills/medtech-model-evidence-export of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- evals/evals.json
- fixtures/sample_pack/integrity_check.json
- fixtures/sample_pack/manifest.json
- fixtures/sample_pack/output.json
- fixtures/sample_pack/runtime_profile.json
- fixtures/sample_pack/validation_summary.json
- fixtures/sample_result.json
- scripts/export_evidence_pack.py
- skill-card.md
- skill.oms.sig
- skill_manifest.yaml
- tests/test_export_evidence_pack.py
- validators/output_schema.json
Open the folder on GitHubat commit 0e0d506
Compare with similar skills
Medtech Model Evidence Export next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Medtech Model Evidence Export this skillNVIDIA/skills | 3.5k | — | ~1.3k | Automated safety check: Notes | Apache-2.0 | |
| LaminDB Biological Data Managementdavila7/claude-code-templates | 32k | 12 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Lamindbaipoch/medical-research-skills | 2k | — | ~4.8k | Automated safety check: Pass | MIT | |
| Experiment Tracking Setuprevfactory/harness-100 | 1.3k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| Skill Testdatabricks-solutions/ai-dev-kit | 1.9k | — | ~1.9k | Automated safety check: Pass | Custom licence |
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Experiment Tracking Setup
revfactory/harness-100
Guide for experiment tracking tool setup (MLflow, Weights & Biases, etc.), reproducibility assurance, model registry, and experiment comparison methodology.
Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
Skill Test
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Questions about Medtech Model Evidence Export
What does Medtech Model Evidence Export do?
Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Medtech Model Evidence Export is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow.
When should I use Medtech Model Evidence Export?
Medtech Model Evidence Export fits situations like: tasks that involve Reproducible research.
How do I install Medtech Model Evidence Export in Claude Code?
Run `npx skills add NVIDIA/skills --skill medtech-model-evidence-export -a claude-code`. Or copy the skill folder (skills/medtech-model-evidence-export in NVIDIA/skills) into .claude/skills/medtech-model-evidence-export in your project. Claude Code loads it when a task matches its description.
How do I install Medtech Model Evidence Export in Codex?
Run `npx skills add NVIDIA/skills --skill medtech-model-evidence-export -a codex`. Or copy the skill folder (skills/medtech-model-evidence-export in NVIDIA/skills) into .agents/skills/medtech-model-evidence-export in your project. Codex loads it when a task matches its description.
Can I use Medtech Model Evidence Export in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill medtech-model-evidence-export -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/medtech-model-evidence-export, .gemini/skills/medtech-model-evidence-export, .github/skills/medtech-model-evidence-export and .opencode/skills/medtech-model-evidence-export in your project.
What does Medtech Model Evidence Export need to run?
Going by SKILL.md and its folder, Medtech Model Evidence Export needs Python for the scripts in its folder, the command-line tools its instructions call (python) and credentials named DATABRICKS_TOKEN. Our summary lists: Python 3; Docker; A credential in DATABRICKS_TOKEN. Its frontmatter pre-approves these tools: Bash.
Does Medtech Model Evidence Export access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Medtech Model Evidence Export safe to install?
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Medtech Model Evidence Export use?
Medtech Model Evidence Export is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Medtech Model Evidence Export use?
About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Medtech Model Evidence Export?
Skills that share tags, products or a category with Medtech Model Evidence Export: LaminDB Biological Data Management (davila7/claude-code-templates, 32k stars), Lamindb (aipoch/medical-research-skills, 2k stars), Experiment Tracking Setup (revfactory/harness-100, 1.3k stars) and Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Medtech Model Evidence Export?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.