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-archesweather.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-archesweather)<a href="https://agentmods.dev/commands/amdresearch/ai4science-studio/run-archesweather"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-archesweather/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-archesweather"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-archesweather.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 | $0.01039 |
| Opus 5 | $0.00000 | $0.00519 |
| Sonnet 5 | $0.00000 | $0.00208 |
| Haiku 4.5 | $0.00000 | $0.00104 |
Grade A, and why
run-archesweather 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run ArchesWeather inference or training on an AMD cluster
Guide the user through running ArchesWeather end-to-end on an AMD cluster.
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. Task Which task do you want to run?
- Inference — evaluate pretrained checkpoints on ERA5 test data
- Training — pretrain or fine-tune ArchesWeather / ArchesWeatherGen
Q1. Container runtime Which container runtime?
- Docker (recommended for interactive workstations)
- Apptainer (recommended for HPC — set
AW_SIFto SIF path)
Q2. (Inference) Model variant Which model checkpoint?
archesweather-m-seed0throughseed3(deterministic)archesweathergen(generative, flow matching)
Q3. (Training) Phase and model
- Model:
archesweatherorarchesweathergen - Phase:
pretrainorfinetune - If fine-tuning, what is the pretrained checkpoint path?
Q4. ERA5 data path Where is the ERA5 dataset? (~735 GB for training, ~35 GB for one test year)
Q5. (Apptainer only) SIF path Do you have an Apptainer SIF built from the silogen/ai-samples geoarches-training Dockerfile?
- Yes — provide the full path
- No — I will generate the build/pull command
- Auto-discover — I will search the filesystem for existing
.siffiles
Q6. Partition and account How should I determine your SLURM partition and account/project? (if using SLURM)
- 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 — Act on answers
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 · 118 lines · 0 tokens per session scan A c803d97ee287
run-archesweather 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 1,039 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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