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ai4science-studio: Skill for Cursor

.cursor/skills/ai4science-material-science/SKILL.md

ai4science-material-science is a skill for Cursor from AMDResearch/ai4science-studio. It costs 28 tokens per session (487 once invoked), scanned A, original, MIT.

A set of instructions for machine learning involving materials and chemistry, including property prediction, molecule generation, and scientific simulation models.

In plain words
What is it for?
Use it when adding models or recipes for graphs, crystals, SMILES strings, materials properties, chemistry, or models that approximate DFT or molecular-dynamics calculations.
Why use it?
It keeps scientific inputs, units, benchmarks, data sources, and model placement clear when documenting these projects.

Skill for Cursor

Written for Cursor: installed under .cursor/.

This is AMDResearch/ai4science-studio's own configuration. It tells Cursor how to work on ai4science-studio itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ai4science-studio configures →

not rated 4repo changed 1mo ago A scan 28 tokens original MIT
View source ↗ AMDResearch/ai4science-studio
Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/AMDResearch/ai4science-studio/main/.cursor/skills/ai4science-material-science/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/AMDResearch/ai4science-studio

Made for: Cursor.

Wrote 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.

agentmods badge for ai4science-material-science

README.md
[![agentmods](https://agentmods.dev/badge/skills/amdresearch/ai4science-studio/ai4science-material-science/github.svg)](https://agentmods.dev/skills/amdresearch/ai4science-studio/ai4science-material-science)
Your own site
<a href="https://agentmods.dev/skills/amdresearch/ai4science-studio/ai4science-material-science"><img src="https://agentmods.dev/badge/skills/amdresearch/ai4science-studio/ai4science-material-science/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.

agentmods 80×15 button for ai4science-material-science

Your own site · 80×15
<a href="https://agentmods.dev/skills/amdresearch/ai4science-studio/ai4science-material-science"><img src="https://agentmods.dev/badge/skills/amdresearch/ai4science-studio/ai4science-material-science.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 487 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.00487
Opus 5.5 $0.00011 $0.00195
Sonnet 5.5 $0.00006 $0.00097
Haiku 4.5 $0.00003 $0.00049

Measured 1mo ago against content hash 69c142f17155, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ai4science-material-science 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 1mo 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.

.cursor/skills/ai4science-material-science/SKILL.md · 33 lines

What it actually says

Material science domain

Scope

The material_science/ domain covers materials, chemistry, and related ML: property prediction, generative models, surrogates for DFT or MD-scale workflows when exposed as Hugging Face models, and similar tasks.

Layout

  • Models: material_science/models/<model-slug>/
  • Slug rules: material_science/models/README.md (default org__model; public on-disk names such as HydraGNN are allowed when the model README.md states the canonical Hub id)
  • Structural template reference: _template/ (repo root)
  • Example: material_science/models/HydraGNN/ — atomistic graph foundation models (HydraGNN), Hub id mlupopa/HydraGNN_Predictive_GFM_2024

Agent guidance

  • Keep recipes explicit about input representations (graphs, crystals, SMILES, etc.) and any unit conventions (including energy vs force targets and graph vs node outputs where relevant).
  • Prefer citing benchmarks and baseline numbers from literature or model cards when adding evaluation snippets.
  • Do not commit large proprietary structure databases; link to public sources or describe how users supply their own data.
  • Institutional AMD clusters and staging large artifacts (Hugging Face CLI, Globus / Constellation mirrors, OLCF copy-out when applicable) belong in data-access.md-style runbooks for ADIOS or similar scientific I/O when models use them.

HydraGNN

MPI/ADIOS training, DDStore, inference branch mismatch: reference/hydragnn.md. Shared ROCm patterns: ai4science-studio/reference/rocm.md.

Safety and licensing

  • Chemistry and materials models may have use-based restrictions; mirror the model card's limitations in the local README.md.
Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 1mo ago Changed · -18 lines 69c142f17155
  2. 2mo ago First seen · 51 lines · 28 tokens per session scan A b08bced6fa87

Subscribe to this mod's changes

ai4science-material-science is a skill published in the GitHub repository AMDResearch/ai4science-studio (4 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 487 once invoked, about $0.0001 per session on Opus 5.5. 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.

Data and AIScientificai-for-scienceamddrug-discoveryearth-sciencefine-tuninghealthcare
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