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-panguweather.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-panguweather)<a href="https://agentmods.dev/commands/amdresearch/ai4science-studio/run-panguweather"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-panguweather/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-panguweather"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-panguweather.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 | $0.00622 |
| Opus 5 | $0.00000 | $0.00311 |
| Sonnet 5 | $0.00000 | $0.00124 |
| Haiku 4.5 | $0.00000 | $0.00062 |
Grade A, and why
run-panguweather 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run PanguWeather inference on an AMD cluster
Guide the user through running PanguWeather 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. CDS credentials Do you have a Copernicus CDS API key? If not, create a free account at cds.climate.copernicus.eu.
Q1. Forecast start date and time What date and time to forecast from? (YYYYMMDD format, time defaults to 0000)
Q2. Lead time How many hours ahead? (default: 24, max ~168 = 7 days)
Q3. Container runtime Which container runtime?
- Docker (recommended —
docker_run.shbuilds and launches automatically)
Step 2 — Act on answers
Read earth_science/models/PanguWeather/model.yaml for full env var details.
Configure credentials
cd earth_science/models/PanguWeather/examples
cp env_file.template env_file
# Set CDSAPI_KEY in env_file
Launch container
bash docker_run.sh
Builds jaxweather:latest (shared with GenCast — skip if already built). Clones silogen/ai-samples for the Dockerfiles.
Run forecast (inside container)
DATE=<YYYYMMDD> LEAD_TIME=<hours> bash /examples/run_inference.sh
Step 3 — Monitor
Output is written to /predictions/panguweather.grib.
# Visualize
python3 /recipe/grib_visualizer.py --input /predictions/panguweather.grib
Produces per-level GIFs under /predictions/outputs/level_*/.
Expected results
| Metric | Value |
|---|---|
| Resolution | 0.25° (~28 km) |
| Forecast horizon | Up to 7 days (168 hours) |
| Weights | ~1.1 GB (auto-downloaded on first run) |
| Output | GRIB2 with z, q, t, u, v (13 levels) + msl, 10u, 10v, 2t |
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 · 82 lines · 0 tokens per session scan A 84b7fae855e8
run-panguweather 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 622 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.
Other commands, from other repositories
by:campaign-auto
Full autonomous campaign — research, design, screen, rank with minimal interruption.
by:load
Load a protein target and analyze it for design.
by:approve-lab
Approve lab submission to Adaptyv Bio (triple-gated).
ml-project
Start a professional AI/ML research-engineer workflow for a task (any domain - CV, medical imaging, NLP/LLM, tabular, time-series). Researches papers first, picks the best method, trains/evaluates rigorously and honestly.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.