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-gpmolformer.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-gpmolformer)<a href="https://agentmods.dev/commands/amdresearch/ai4science-studio/run-gpmolformer"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-gpmolformer.svg" alt="Measured on agentmods" 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 | $0.01820 |
| Opus 5 | $0.00000 | $0.00910 |
| Sonnet 5 | $0.00000 | $0.00364 |
| Haiku 4.5 | $0.00000 | $0.00182 |
Grade A, and why
run-gpmolformer 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 7d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run GP-MoLFormer molecule generation on an AMD cluster
Research / engineering use only. Outputs are novel SMILES strings for drug-discovery research. Not validated for clinical or therapeutic use.
Guide the user through running GP-MoLFormer on an AMD cluster via SLURM.
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 (ask ALL questions before doing anything)
Ask the user the following questions. Do not assume any defaults. Wait for answers to all questions before proceeding.
Q0. Container runtime Which container runtime do you want to use?
- Apptainer (recommended for HPC — supports overlays,
--rocmflag for GPU) - Docker (simpler setup, no overlay needed, but no MPI support and env vars must be appended not replaced)
Q1. (Apptainer only) SIF path
Do you have an Apptainer SIF to use? The recommended image is rocm/pytorch:rocm7.2.2_ubuntu24.04_py3.12_pytorch_release_2.10.0 (py3.12) — it has validated GPU detection on MI300A clusters with current amdgpu drivers. The older rocm7.0 py3.10 image works on MI300X but silently falls back to CPU on MI300A clusters with newer drivers.
- Yes — provide the full path
- No — I will generate the pull command
- Auto-discover — I will search the filesystem for existing ROCm PyTorch
.siffiles
Q2. Work directory Where do you want the GP-MoLFormer repo clone, model weights, and output CSV to live on the host? (full path — this directory is reused across runs so the clone is not re-downloaded each time)
Q3. Generation mode Which generation mode do you want?
- Unconditional — generates molecules freely with no structural constraint.
- Scaffold-constrained — completes molecules around a SMILES fragment you provide (e.g.
c1ccccc1for a benzene ring).
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.
- 7d ago First seen · 140 lines · 0 tokens per session scan A a88d1423bd31
run-gpmolformer 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 1,820 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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