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-perf-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-perf-hydragnn)<a href="https://agentmods.dev/commands/amdresearch/ai4science-studio/run-perf-hydragnn"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-perf-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-perf-hydragnn"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/run-perf-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 | $0.01779 |
| Opus 5 | $0.00000 | $0.00890 |
| Sonnet 5 | $0.00000 | $0.00356 |
| Haiku 4.5 | $0.00000 | $0.00178 |
Grade A, and why
run-perf-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 10d 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run HydraGNN perf-analysis or perf-optimizer-loop on an AMD cluster
Guide the user through performance engineering for HydraGNN Predictive GFM: one-shot perf-analysis (2-node training + TraceLens + Omnistat diagnosis) or iterative perf-optimizer-loop (accept/revert on epoch_time_s). This is not ensemble inference — use /run-hydragnn for that.
Recipe docs: material_science/models/HydraGNN/recipes/perf-analysis/README.md, material_science/models/HydraGNN/recipes/perf-optimizer-loop/README.md.
Attribution / levers: dispatch-attribution.md, lever_catalog.yaml.
Orchestration reference: .cursor/skills/ai4science-perf-analysis/SKILL.md (launcher → parallel analysts → parallel verifiers → synthesizer).
Step 0 — Cluster config check (required)
Read .cluster-config.yaml (repo root) or ~/.config/ai4science-studio/cluster.yaml.
Required for perf recipes:
- Set
AI4S_SHARED_DIRfrom cluster config (paths.scratchor documented scratch root). - Set
PERF_TOOLS_DIRfromperf_tools.dir. If missing, stop and send the user to/init-clusterQ9 (Performance tooling).
Pre-fill SLURM partition/account from config.
Runtime: Apptainer + SLURM only (sbatch_train_perf_amd.sh).
Step 1 — Questionnaire (ask ALL before acting)
Q0. Mode
- perf-analysis — One 2-node perf job + post-hoc multi-subagent analysis (default topology for this recipe).
- perf-optimizer-loop — Iterative tuning; primary FOM
epoch_time_s(lower is better); seerecipes/perf-optimizer-loop/README.mdfor stop rules anddo_not_retry.json.
Q1. HydraGNN checkout (HG_REPO_DIR)
Default in sbatch: $AI4S_SHARED_DIR/models/HydraGNN/code/HydraGNN (cloned at pinned SHA if missing).
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.
- 10d ago First seen · 147 lines · 0 tokens per session scan A a870cede2573
run-perf-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 1,779 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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