Borrowing it
Nothing to install: this file belongs to AMDResearch/ai4science-studio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AMDResearch/ai4science-studio/main/.claude/commands/run-swinunetr.mdgit clone --depth 1 https://github.com/AMDResearch/ai4science-studioWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/amdresearch/ai4science-studio/run-swinunetr)<a href="https://agentmods.dev/commands/amdresearch/ai4science-studio/run-swinunetr"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-swinunetr/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/amdresearch/ai4science-studio/run-swinunetr"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-swinunetr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00717 |
| Opus 5 | $0.00000 | $0.00358 |
| Sonnet 5 | $0.00000 | $0.00143 |
| Haiku 4.5 | $0.00000 | $0.00072 |
Grade A, and why
run-swinunetr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run SwinUNETR medical segmentation on an AMD cluster
Guide the user through training or inference with SwinUNETR on AMD GPUs.
Research / engineering use only. Not for clinical or diagnostic use.
Step 0 — Cluster config check
Check if .cluster-config.yaml (repo root) or ~/.config/ai4science-studio/cluster.yaml exists. If neither exists, run the /init-cluster flow first. If a config exists, read it and pre-fill container runtime and SLURM partition/account from saved values.
Step 1 — Questionnaire
Q0. Task
- Training — Train on NSCLC-Radiomics dataset
- Inference — Run optimized inference with a trained checkpoint
Q1. Container runtime
- Docker (recommended — uses Docker Compose from upstream)
- Apptainer (HPC clusters)
Q2. (Inference only) Checkpoint path
Full path to your trained .pth checkpoint?
Q3. ROI size Default is 96x96x96. MI300X can handle up to 480x480x96. What size?
Q4. (Training) Max epochs Default: 700. How many?
Q5. (SLURM) Partition and account How should I determine your SLURM partition and account/project?
- Provide manually — type your partition and account names
- Auto-discover — I will query SLURM to find available partitions and accounts on this cluster
Step 2 — Setup
Auto-discovery procedures
Run these when the user chose Auto-discover for any question. Present the results and let the user confirm or override.
SLURM partition and account (Q5):
sinfo -h -o "%P %G" | grep -i gpu
sacctmgr show associations where user=$USER format=account%30,partition%30 -n
Present the available GPU partitions and the user's associated accounts. If multiple exist, ask the user to pick.
After auto-discovery, always confirm the found values with the user before proceeding.
Docker
cd healthcare/models/SwinUNETR/examples
./docker_run.sh train # or: ./docker_run.sh inference
Apptainer
Edit the #SBATCH header in the relevant sbatch script — replace YOUR_PARTITION_HERE and YOUR_ACCOUNT_HERE with the user's values, then:
export SU_SIF=<path>
sbatch healthcare/models/SwinUNETR/examples/sbatch_train_amd.sh
# or
export SU_CHECKPOINT=<path>
sbatch healthcare/models/SwinUNETR/examples/sbatch_inference_amd.sh
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 87 lines · 0 tokens per session scan A a1d074867dbf
run-swinunetr is a command published in the GitHub repository AMDResearch/ai4science-studio (4 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 717 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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