Clinical Trials Database
google-deepmind/science-skills
Query ClinicalTrials.gov via APIv2. An agent skill from google-deepmind/science-skills.
A skill your agent uses when choosing the model for a medical-imaging study.
Install Model Selection
$ npx skills add Aperivue/medsci-skills --skill model-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Aperivue/medsci-skills model-selection --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/Aperivue/medsci-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/model-selection .claude/skills/model-selection && 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 "model-selection" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/model-selection into .claude/skills/model-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection", 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/Aperivue/medsci-skills/tree/main/skills/model-selectionType 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 Aperivue/medsci-skills --skill model-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Aperivue/medsci-skills model-selection --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/model-selection .agents/skills/model-selection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "model-selection" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/model-selection into .agents/skills/model-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection", 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 Aperivue/medsci-skills --skill model-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Aperivue/medsci-skills model-selection --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/model-selection .cursor/skills/model-selection && 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 "model-selection" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/model-selection into .cursor/skills/model-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection", 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/Aperivue/medsci-skills.git --path skills/model-selection--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 Aperivue/medsci-skills --skill model-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Aperivue/medsci-skills model-selection --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/model-selection .gemini/skills/model-selection && 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 "model-selection" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/model-selection into .gemini/skills/model-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection", 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 Aperivue/medsci-skills model-selectionInstalls 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 Aperivue/medsci-skills --skill model-selection -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/model-selection .github/skills/model-selection && 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 "model-selection" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/model-selection into .github/skills/model-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection", 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 Aperivue/medsci-skills --skill model-selection -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Aperivue/medsci-skills model-selection --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/model-selection .opencode/skills/model-selection && 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 "model-selection" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/model-selection into .opencode/skills/model-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection", 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
model-selection- GitHub stars
- 329
- Token cost
- ~2.7k tokens
- SKILL.md length
- 1,043 words
- Files
- 20 (incl. scripts, references)
- Skills in repo
- 54
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when choosing the model for a medical-imaging study.
- Works in 6 steps: Frame the question → Walk the decision tree, read the family… → Write the architecture decision note → …
- Choosing the model for a medical-imaging study
- SKILL.md covers Workflow and Outputs and hand-off
- Runs Python scripts from its folder; calls bash and python3
What it does
Model Selection is an agent skill from Aperivue/medsci-skills. Use when choosing the model for a medical-imaging study. Picks a paper-grounded architecture family (CNN/ViT, U-Net/nnU-Net, detection, SAM/foundation, GNN), then vets the concrete repo or checkpoint for licence, version pin, weight provenance and benchmark overlap.
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 24 other files, including scripts and reference files (for example `references/classification.md`, `references/detection.md` and `references/foundation_models.md`).
It sits in Research & Science, covering Clinical and healthcare research. The repository describes itself as: Agent Skills for medical research — literature search, reporting-guideline & citation checks, statistics, publication figures, submission. Works with Claude Code, Codex, Cursor &… The licence is MIT.
When your agent uses it
- Choosing the model for a medical-imaging study
- Tasks that involve Clinical and healthcare research
Example prompts
- “/model-selection”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 3b14ae2. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 8 files in scripts/ (Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
bashpython3
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
Model Selection loads about 2.7k tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 1,043 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 passed
The automated check found no risky patterns in SKILL.md.
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 Aperivue/medsci-skills at commit 3b14ae2, republished under its MIT licence (© Aperivue). 1,043 words, ~2,664 tokens.
Download SKILL.mdSave it as
.claude/skills/model-selection/SKILL.md (or your agent's skills folder). This skill also uses 19 other files; get the full folder from GitHub.- name
- model-selection
- description
- Use when choosing the model for a medical-imaging study. Picks a paper-grounded architecture family (CNN/ViT, U-Net/nnU-Net, detection, SAM/foundation, GNN), then vets the concrete repo or checkpoint for licence, version pin, weight provenance and benchmark overlap.
- metadata.triggers
- architecture zoo, model sourcing, which architecture, choose a model, model selection, ResNet vs ViT, U-Net vs nnU-Net, what backbone, foundation model for…
Model-Selection Skill
Two questions, in this order. Phases 1–3 answer a literature question with a stable answer: which architecture family suits the task. Phases 4–6 answer a provenance question: which concrete artifact — repository, revision, checkpoint — will be run, and what its numbers may claim. The skill writes decision notes and runs one stdlib gate; it never downloads, runs, trains or benchmarks a model, and it describes archetypes, not a live SOTA leaderboard.
Elsewhere: building the repo → /model-scaffold; profiling data and planning preprocessing →
/imaging-data; validation design, metrics, uncertainty, explainability → /model-assessment;
documenting a model you built → /model-card; an LLM/MLLM, including benchmark contamination in
that setting → /mllm-eval; AI-vs-expert reader study → /design-ai-benchmarking.
Workflow
Phase 1 — Frame the question
State the task (classification / segmentation / detection / synthesis / transfer), the modality and dimensionality (2-D vs 3-D volume), the labelled-data scale (events / structures, not just images), label availability (lots / few / unlabelled pool), and the constraints (class imbalance, small structures, interpretability, deployment compute).
Phase 2 — Walk the decision tree, read the family card
Follow ${CLAUDE_SKILL_DIR}/references/index.md (task → constraints → default pick). It routes to
one card; each gives the source paper, core idea, when-to-use, medical-imaging use, reference
implementation, and the typical validation setup for that class.
| Card | Families |
|---|---|
${CLAUDE_SKILL_DIR}/references/classification.md | ResNet / DenseNet / EfficientNet / Inception / ConvNeXt / ViT / Swin / DeiT |
${CLAUDE_SKILL_DIR}/references/segmentation.md | U-Net / 3-D U-Net / V-Net / Attention & Residual U-Net / nnU-Net (+ ResEnc, MedNeXt, STU-Net) / SegResNet / Swin-UNETR / Mask R-CNN |
${CLAUDE_SKILL_DIR}/references/detection.md | nnDetection / Faster R-CNN + FPN / Mask R-CNN / RetinaNet / YOLO / DETR |
${CLAUDE_SKILL_DIR}/references/synthesis.md | Pix2Pix / CycleGAN / SPADE / diffusion (DDPM, latent) / VAE / fastMRI reconstruction |
${CLAUDE_SKILL_DIR}/references/foundation_models.md | SAM / MedSAM / MedSAM2 / nnInteractive / VISTA3D / TotalSegmentator / SegVol / BiomedCLIP / DINO / MAE / SimCLR / MoCo |
${CLAUDE_SKILL_DIR}/references/graph.md | GCN / GraphSAGE / GAT / GIN / BrainGNN for connectomes and population graphs (PyTorch Geometric / DGL; not scaffolded by /model-scaffold) |
Never recommend an architecture for a modality or data scale it does not suit (a from-scratch ViT on a few hundred images, 2-D slices for a volumetric structure) — the decision-tree constraints exist to prevent exactly that. If asked for "the best" model, say the zoo is a curated archetype map, not a current SOTA ranking.
Phase 3 — Write the architecture decision note
Write decisions/architecture_choice.md: the task, the chosen architecture, its source paper
(mandatory — the Methods cite it), the reason against the constraints, the runner-up and why not,
and the matching /model-scaffold template. Never invent a benchmark number or paper claim: cite it
(verify via /search-lit), or write [VERIFY] and ask. A reference without a /search-lit-confirmed
DOI/PMID is marked [UNVERIFIED - NEEDS MANUAL CHECK].
Phase 4 — Write the model dossier for the concrete artifact
When a concrete candidate exists (a GitHub repo, a Hugging Face checkpoint, a paper's released
weights), write model_dossier.json recording what is known; leave unknowns unstated, never
guessed — the gate turns an absence into a finding.
{
"model": "OrganSeg-3D v2.5.1",
"source": {"kind": "github", "url": "...", "version": "v2.5.1", "commit": "abc1234"},
"licence": {"spdx": "Apache-2.0", "verified_from": "LICENSE at commit abc1234"},
"intended_use": "research",
"weights": {"pretrained": false},
"task": {"model": "3d_ct_organ_segmentation", "study": "3d_ct_organ_segmentation"},
"reported_validation": [{"dataset": "ExampleBench", "metric": "Dice", "source": "J Ex 2021"}],
"developed_on": ["ExampleBench"],
"evaluation_arms": [{"name": "external", "dataset": "OtherCohort-2026"}],
"hardware": {"claimed": "any CUDA GPU", "verified_on": "GTX 1080 Ti", "verified": true}
}Read each field from the artifact, not from memory:
- Licence — only from the licence file at the pinned revision, and record which file
(
verified_from). Never from a README badge, a model-card summary, or memory; an unstated licence isLICENCE_UNSTATED, never "probably MIT". developed_on— the paper's own account of where the method was built and tuned. It is the field people skip and the one the gate needs: a method that won a challenge was tuned on it.hardware.verified— true only after it actually ran. A support matrix is a different claim: a CUDA capability one compiler accepts may still be refused by another in the same stack.
Show full SKILL.md (496 more words)Show less
Phase 5 — Gate the dossier
python3 ${CLAUDE_SKILL_DIR}/scripts/check_model_provenance.py --dossier model_dossier.json \
--out qc/model_provenance.json --strictStdlib-only and network-free: nothing is fetched and no licence resolved online.
| Verdict | Severity | Fires when |
|---|---|---|
BENCHMARK_PROVENANCE_CONFLICT | Major | an evaluation arm uses a dataset the model was developed or tuned on |
EVAL_DATA_IN_TRAINING | Major | an evaluation arm's dataset is inside the pretraining corpus |
LICENCE_UNSTATED | Major | no licence recorded — not the same as a permissive one |
LICENCE_INCOMPATIBLE | Major | non-commercial / research-only licence under commercial or deployment intent |
WEIGHTS_PROVENANCE_UNKNOWN | Major | pretrained weights whose training corpus is not stated |
DEVELOPED_ON_UNSTATED | Major | the developed_on key is absent, so the conflict check could not run |
EVALUATION_ARMS_UNSTATED | Major | the evaluation_arms key is absent, so neither provenance check could run |
INTENDED_USE_UNSTATED | Major | intended_use is absent, so licence compatibility could not be checked |
TASK_MISMATCH | Minor | the model's task is not the study's task |
NO_VERSION_PIN | Minor | no commit, tag or revision |
VALIDATION_UNREPORTED | Minor | no reported validation (dataset + metric + source) |
HARDWARE_UNVERIFIED | Minor | hardware support claimed but never executed |
LICENCE_UNVERIFIED | Minor | a licence is named but not the file it was read from |
The gate flags a relationship, not a reputation: developed_on: ExampleBench passes cleanly as
long as no evaluation arm uses ExampleBench (the clean fixture proves this). Dataset names match as
token sequences with a small family-alias table — MSD Task09 Spleen matches MSD,
MS Cohort 2026 does not — never by substring.
An explicit empty list ("developed_on": []) states "none" and is not a finding; an absent key is.
intended_use must be research, commercial or clinical_deployment, and developed_on /
weights.trained_on must be lists of strings; anything else is an input error (exit 2), never a pass.
Known limits. LICENCE_INCOMPATIBLE recognises non-commercial / research-only markers in the
licence string (NC, non-commercial, research-only, in SPDX or spaced form). A free-text licence
that restricts use without those markers (e.g. "academic use only"), or a copyleft licence under
closed deployment, is not detected: read the licence file and record the conclusion yourself.
Phase 6 — Turn a Major into a study decision
A BENCHMARK_PROVENANCE_CONFLICT rarely means abandoning the model (it is often the best-engineered
option precisely because it was tuned hard). It changes what the arm may claim:
- Report that arm as a demonstration that the pipeline runs end to end, not as evidence the method works.
- Put the evidential weight on an arm whose data post-dates the model, and say so with dates.
- State the conflict in Methods and Limitations rather than leaving a reviewer to find it.
Never report a flagged arm as independent validation. An EVAL_DATA_IN_TRAINING arm is different in
kind: it produces a training-set score and cannot be reported as validation at all. Write this
arm-by-arm decision into the study record.
Outputs and hand-off
decisions/architecture_choice.md— the architecture decision note (Phase 3).model_dossier.json— the provenance record (Phase 4);qc/model_provenance.json— the audit (Phase 5).- The arm-by-arm decision (Phase 6).
Carry them to /model-scaffold (instantiate the template), /model-assessment (arm and validation
design, what each arm may claim, metrics), /model-card (provenance section), and /write-paper
(Methods cite the source paper; Limitations state any conflict). Gate regression:
bash ${CLAUDE_SKILL_DIR}/scripts/check_model_provenance_challenge/verify.sh and
bash ${CLAUDE_SKILL_DIR}/tests/test_model_provenance.sh.
© Aperivue, MIT. 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 19 other files (scripts, references) in skills/model-selection of Aperivue/medsci-skills.
- SKILL.md
- references/classification.md
- references/detection.md
- references/foundation_models.md
- references/graph.md
- references/index.md
- references/segmentation.md
- references/synthesis.md
- scripts/check_model_provenance.py
- scripts/check_model_provenance_challenge/expected/clean.txt
- scripts/check_model_provenance_challenge/expected/defect.txt
- scripts/check_model_provenance_challenge/expected/unstated.txt
- scripts/check_model_provenance_challenge/fixture/dossier_clean.json
- scripts/check_model_provenance_challenge/fixture/dossier_defect.json
- scripts/check_model_provenance_challenge/fixture/dossier_unstated.json
- scripts/check_model_provenance_challenge/problem.md
- … and 4 more
Open the folder on GitHubat commit 3b14ae2
Compare with similar skills
Model Selection 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 |
|---|---|---|---|---|---|---|
| Model Selection this skillAperivue/medsci-skills | 329 | — | ~2.7k | Automated safety check: Pass | MIT | |
| Clinical Trials Databasegoogle-deepmind/science-skills | 3.2k | 3 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 Proposalluwill/research-skills | 858 | — | ~4.4k | Automated safety check: Notes | None | |
| Medical Imaging ReviewLeonChaoX/qinyan-academic-skills | 938 | 3 repos | ~1.1k | Automated safety check: Notes | MIT |
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Research Proposal
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Related
Categories
Questions about Model Selection
What does Model Selection do?
A skill your agent uses when choosing the model for a medical-imaging study. Model Selection is an agent skill from Aperivue/medsci-skills. Use when choosing the model for a medical-imaging study.
When should I use Model Selection?
Model Selection fits situations like: choosing the model for a medical-imaging study; tasks that involve Clinical and healthcare research.
How do I install Model Selection in Claude Code?
Run `npx skills add Aperivue/medsci-skills --skill model-selection -a claude-code`. Or copy the skill folder (skills/model-selection in Aperivue/medsci-skills) into .claude/skills/model-selection in your project. Claude Code loads it when a task matches its description.
How do I install Model Selection in Codex?
Run `npx skills add Aperivue/medsci-skills --skill model-selection -a codex`. Or copy the skill folder (skills/model-selection in Aperivue/medsci-skills) into .agents/skills/model-selection in your project. Codex loads it when a task matches its description.
Can I use Model Selection 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 Aperivue/medsci-skills --skill model-selection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-selection, .gemini/skills/model-selection, .github/skills/model-selection and .opencode/skills/model-selection in your project.
What does Model Selection need to run?
Going by SKILL.md and its folder, Model Selection needs Python for the scripts in its folder and the command-line tools its instructions call (bash and python3). Our summary lists: Python 3.
Does Model Selection 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 Model Selection safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. 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 Model Selection use?
Model Selection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Model Selection use?
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 12k tokens, read only when the agent opens those files.
What are the alternatives to Model Selection?
Skills that share tags, products or a category with Model Selection: 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 Proposal (luwill/research-skills, 858 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Model Selection?
Aperivue (a GitHub organization) maintains it in Aperivue/medsci-skills, which has 329 GitHub stars. The repository holds 54 skills in this directory. The repository was last updated on October 5, 2026.
Source: Aperivue/medsci-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.