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-hydragnn.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-hydragnn)<a href="https://agentmods.dev/commands/amdresearch/ai4science-studio/run-hydragnn"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-hydragnn/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-hydragnn"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-hydragnn.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 | $0.00592 |
| Opus 5 | $0.00000 | $0.00296 |
| Sonnet 5 | $0.00000 | $0.00118 |
| Haiku 4.5 | $0.00000 | $0.00059 |
Grade A, and why
run-hydragnn 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run HydraGNN ensemble inference on an AMD cluster
Guide the user through running HydraGNN predictive GFM inference on AMD GPUs.
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
- Inference — Load a checkpoint and run predictions
- Training — Smaller-scale training (the full GFM pretraining is Frontier-scale)
Q1. (Inference) Checkpoint and config
First, auto-discover: run find <paths.projects> -maxdepth 5 -name "*.pk" 2>/dev/null and find <paths.projects> -maxdepth 5 -name "config.json" 2>/dev/null (substituting paths.projects from cluster config) to check for existing checkpoints on shared storage. Present any results to the user. If nothing is found, ask:
- Do you have a
.pkcheckpoint and matchingconfig.jsonfrom the HF Hubmlupopa/HydraGNN_Predictive_GFM_2024? - Options: Yes, provide paths / No, download for me / Auto-discovered (use found path)
Q2. Output directory
Where to write predictions? Default: <paths.projects>/hydragnn-results (read paths.projects from cluster config, never use $HOME for large outputs).
Step 2 — Download weights (if needed)
pip install huggingface-hub
huggingface-cli download mlupopa/HydraGNN_Predictive_GFM_2024 \
--include "Ensemble_of_models/gfm_0.229/*" \
--local-dir ./hydragnn-weights
Step 3 — Launch
Docker
cd material_science/models/HydraGNN/examples
HG_CHECKPOINT=/path/to/gfm_0.229_epoch_100.pk \
HG_CONFIG=/path/to/config.json \
./docker_run.sh inference
Manual
export HG_CHECKPOINT=/path/to/checkpoint.pk
export HG_CONFIG=/path/to/config.json
bash material_science/models/HydraGNN/examples/run_inference.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 · 58 lines · 0 tokens per session scan A a8b4e4ed4258
run-hydragnn 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 592 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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