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How to answer user questions about what models are available, filter by domain/task/license, and compare models in AI4Science Studio.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Applies to Earth-system ML in AI4Science Studio—climate, weather, geospatial data, and model folders under earth_science/models/. Includes validated AMD/ROCm HPC patterns for earth science models.
Applies to material science and chemistry ML recipes under material_science/models/ in AI4Science Studio.
Runs the AMD AI agents bottleneck-analysis workflow on a HydraGNN or ORBIT-2 training job using TraceLens + Omnistat user-mode + paired analyst/verifier subagents, or drives the iterative perf-optimizer-loop (accept/revert on throughput or epoch time). Use when the user wants to analyze a perf run, analyze a 2-node HydraGNN run, find bottlenecks with TraceLens/Omnistat, run the AMD AI agents on a job, run the perf-optimizer-loop, tune ORBIT-2 throughput, or optimize HydraGNN training performance.
Applies when working in the AI4Science Studio repository. Describes domain layout, model slug rules, where recipes live, safety expectations, and AMD/ROCm HPC patterns (Apptainer and Docker) validated in practice.
Step-by-step instructions for how Cursor should run any model in AI4Science Studio — read model.yaml, check preflight, select container runtime, launch.
Applies to healthcare and life sciences ML recipes under healthcare/models/ in AI4Science Studio, with privacy and non-clinical-use guardrails.
| name | ai4science-discover |
| description | How to answer user questions about what models are available, filter by domain/task/license, and compare models in AI4Science Studio. |
Use this skill when a user asks what models are available, wants to compare models, or asks questions like "what can I do with this repo?"
Read models.yaml at the repo root. It lists every model with:
slug — folder namedomain — earth_science, material_science, healthcare, physics_simulation, protein_foldinghf_id — Hugging Face model id (or N/A)license — SPDX idtask — one-line descriptiontasks_available — list of recipe tasks (inference, train, finetune, ensemble)path — relative path to model folderParse models.yaml and present a table:
| Model | Domain | Task | Available recipes |
|---|---|---|---|
| StormCast | Earth science | Weather prediction | inference, ensemble |
| ... | ... | ... | ... |
When the user asks for a subset:
domain fieldtasks_available (e.g. "which models support fine-tuning?" → filter for finetune in tasks_available)license (e.g. "which models are MIT licensed?")hf_id != N/A for models with HF weightsWhen the user wants details on a specific model:
<path>/model.yaml for structured metadata<path>/README.md for prose description<path>/examples/README.md for available scripts<path>/recipes/ subdirectories for available recipesWhen the user asks to compare two or more models:
model.yamlWhen asked "which models are ready to run?":
models.yaml, check:
examples/docker_run.sh exist?examples/preflight_*.py exist?recipes/*/README.md exist?examples/sbatch_*_amd.sh exist?