Clinical Trials Database
google-deepmind/science-skills
Query ClinicalTrials.gov via APIv2. An agent skill from google-deepmind/science-skills.
Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence.
Install Dicom Series To Volume
$ npx skills add NVIDIA/skills --skill dicom-series-to-volume -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills dicom-series-to-volume --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/dicom-series-to-volume .claude/skills/dicom-series-to-volume && 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 "dicom-series-to-volume" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-series-to-volume into .claude/skills/dicom-series-to-volume/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-series-to-volume", 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/dicom-series-to-volumeType 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 dicom-series-to-volume -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills dicom-series-to-volume --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/dicom-series-to-volume .agents/skills/dicom-series-to-volume && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dicom-series-to-volume" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-series-to-volume into .agents/skills/dicom-series-to-volume/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-series-to-volume", 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 dicom-series-to-volume -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills dicom-series-to-volume --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/dicom-series-to-volume .cursor/skills/dicom-series-to-volume && 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 "dicom-series-to-volume" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-series-to-volume into .cursor/skills/dicom-series-to-volume/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-series-to-volume", 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/dicom-series-to-volume--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 dicom-series-to-volume -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills dicom-series-to-volume --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/dicom-series-to-volume .gemini/skills/dicom-series-to-volume && 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 "dicom-series-to-volume" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-series-to-volume into .gemini/skills/dicom-series-to-volume/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-series-to-volume", 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 dicom-series-to-volumeInstalls 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 dicom-series-to-volume -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/dicom-series-to-volume .github/skills/dicom-series-to-volume && 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 "dicom-series-to-volume" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-series-to-volume into .github/skills/dicom-series-to-volume/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-series-to-volume", 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 dicom-series-to-volume -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 dicom-series-to-volume --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/dicom-series-to-volume .opencode/skills/dicom-series-to-volume && 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 "dicom-series-to-volume" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-series-to-volume into .opencode/skills/dicom-series-to-volume/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-series-to-volume", 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
dicom-series-to-volume- GitHub stars
- 3.6k
- Token cost
- ~926 tokens
- SKILL.md length
- 377 words
- Files
- 10 (incl. scripts)
- Skills in repo
- 390
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence.
- Tasks that involve Clinical and healthcare research
- SKILL.md covers Purpose, Instructions, Available Scripts and Prerequisites, plus 2 more sections
- Runs Python scripts from its folder; calls python
What it does
Dicom Series To Volume is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.
Its SKILL.md is about 930 tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts (for example `BENCHMARK.md`, `evals/evals.json` and `fixtures/generate_fixtures.py`).
It sits in Research & Science, covering Clinical and healthcare research. 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 Clinical and healthcare research
Example prompts
- “/dicom-series-to-volume”
Requirements
- Python 3
- Pre-approved tools (allowed-tools): Bash
What it can do on your machine
Read from SKILL.md and the folder at commit 14a98ae. 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 no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Dicom Series To Volume loads about 926 tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 377 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 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 377 words, ~926 tokens.
Download SKILL.mdSave it as
.claude/skills/dicom-series-to-volume/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.- name
- dicom-series-to-volume
- description
- Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.
- allowed-tools
- Bash
- license
- Apache-2.0
- metadata.author
- NVIDIA MedTech Team
- metadata.tags
- MedTech, DICOM, NIfTI
dicom_series_to_volume
Purpose
- Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.
- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
- Manifest I/O: inputs are
dicom_dir; outputs arenifti_volumeandresult_json.
Instructions
- Read
skill_manifest.yamlbefore changing arguments, side effects, or validation gates. - Run
scripts/series_to_volume.pythrough the documented command below; keep outputs under a caller-provided run directory. - If a host agent exposes
run_script, userun_script("scripts/series_to_volume.py", args=[...]); otherwise run the Bash/Python command shown below. - Check the emitted JSON and the paired
dicom_volume_quality_v1verifier before treating the run as evidence.
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/series_to_volume.py | Primary entrypoint declared by skill_manifest.yaml. | PATH_TO_DICOM_DIR [--output OUT.nii.gz] |
Prerequisites
- Runtime requirements: Python packages listed in
runtime.side_effects.pip_packages. - NiBabel 5.4 or newer is required so extreme-oblique axes remain labeled consistently across reorientation.
- Run commands from the repository root unless an existing section below says otherwise.
Limitations
- Single-series only; multi-series input is rejected at preflight.
- Multi-frame DICOM (NumberOfFrames > 1 per file) not supported.
- Compressed transfer syntaxes (JPEG / JPEG2000 / RLE) not supported.
- No voxel reorientation. The affine is derived from DICOM headers and represented in NIfTI/RAS coordinates; a downstream gate (e.g. expected_axcodes) is expected to assert orientation before this volume is fed to a segmentation model.
- Not for clinical deployment, autonomous diagnosis, regulatory submission, production inference (use a vetted converter such as dcm2niix for that).
Show full SKILL.md (143 more words)Show less
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
| Missing dependency or import error | Runtime package drift from skill_manifest.yaml. | Install the packages declared in the manifest or use the documented setup command. |
| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |
| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |
Reads one DICOM series, sorts slices by ImagePositionPatient, applies
RescaleSlope and RescaleIntercept, builds an affine from orientation and
spacing tags, and writes a .nii.gz plus JSON summary.
python scripts/series_to_volume.py PATH_TO_DICOM_DIR --output PATH_TO_OUT.nii.gzFor a trusted run with the paired verifier:
python -m eval_engine.run_trusted skills/dicom-series-to-volume \
--fixture PATH_TO_DICOM_DIR \
--out runs/dicom_series_to_volume_trustedKey output fields: n_slices, series_instance_uid, output.path,
output.shape, output.spacing, output.axcodes, output.affine,
hu_range, and runtime.conversion_seconds.
Scope limits: single-series CT only; no multi-frame DICOM, compressed transfer syntax handling, RT structure sets, auto-reorientation, or clinical use.
© 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 9 other files (scripts) in skills/dicom-series-to-volume of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- evals/evals.json
- fixtures/generate_fixtures.py
- scripts/series_to_volume.py
- skill-card.md
- skill.oms.sig
- skill_manifest.yaml
- tests/test_orientation_regression.py
- validators/output_schema.json
Open the folder on GitHubat commit 14a98ae
Compare with similar skills
Dicom Series To Volume 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 |
|---|---|---|---|---|---|---|
| Dicom Series To Volume this skillNVIDIA/skills | 3.6k | — | ~926 | Automated safety check: Notes | Apache-2.0 | |
| Clinical Trials Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.2k | Automated safety check: Pass | Apache-2.0 | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Biomedical Analysis Dispatchxjtulyc/MedgeClaw | 617 | 1 repos | ~2k | Automated safety check: Pass | None | |
| Research Paperluwill/research-skills | 862 | — | ~1.9k | Automated safety check: Pass | None | |
| Research Proposalluwill/research-skills | 862 | — | ~4.5k | Automated safety check: Notes | None |
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Related
Categories
Questions about Dicom Series To Volume
What does Dicom Series To Volume do?
Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Dicom Series To Volume is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence.
When should I use Dicom Series To Volume?
Dicom Series To Volume fits situations like: tasks that involve Clinical and healthcare research.
How do I install Dicom Series To Volume in Claude Code?
Run `npx skills add NVIDIA/skills --skill dicom-series-to-volume -a claude-code`. Or copy the skill folder (skills/dicom-series-to-volume in NVIDIA/skills) into .claude/skills/dicom-series-to-volume in your project. Claude Code loads it when a task matches its description.
How do I install Dicom Series To Volume in Codex?
Run `npx skills add NVIDIA/skills --skill dicom-series-to-volume -a codex`. Or copy the skill folder (skills/dicom-series-to-volume in NVIDIA/skills) into .agents/skills/dicom-series-to-volume in your project. Codex loads it when a task matches its description.
Can I use Dicom Series To Volume 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 dicom-series-to-volume -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dicom-series-to-volume, .gemini/skills/dicom-series-to-volume, .github/skills/dicom-series-to-volume and .opencode/skills/dicom-series-to-volume in your project.
What does Dicom Series To Volume need to run?
Going by SKILL.md and its folder, Dicom Series To Volume needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash.
Does Dicom Series To Volume 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 Dicom Series To Volume 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 Dicom Series To Volume use?
Dicom Series To Volume 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 Dicom Series To Volume use?
About 926 tokens (SKILL.md is roughly 3.7k 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 Dicom Series To Volume?
Skills that share tags, products or a category with Dicom Series To Volume: Clinical Trials Database (google-deepmind/science-skills, 3.2k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars) and Research Paper (luwill/research-skills, 862 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Dicom Series To Volume?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 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.