Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/learningmatter-mit/atomisticskills/ml-generative-aditnpx skills add learningmatter-mit/AtomisticSkills --skill ml-generative-aditgit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/skills/learningmatter-mit/atomisticskills/ml-generative-adit)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/ml-generative-adit"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/ml-generative-adit.svg" alt="Measured on agentmods" 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 | $0.00030 | $0.01174 |
| Opus 5 | $0.00015 | $0.00587 |
| Sonnet 5 | $0.00006 | $0.00235 |
| Haiku 4.5 | $0.00003 | $0.00117 |
Grade A, and why
ml-generative-adit 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 today.
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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADiT Structure Generation Skill
Goal
Generate novel crystal structures and molecules using ADiT (All-atom Diffusion Transformers, ICML 2025), a unified latent diffusion framework from Meta FAIR Chemistry that jointly generates both periodic materials and non-periodic molecular systems from a shared latent space.
1. Prerequisites
[!IMPORTANT] GPU Required: ADiT requires a CUDA-compatible GPU. CPU inference is extremely slow.
- The
adit-agentconda environment must be installed and configured. - The AADT repository must be cloned to
.agents/tmp/adit/. - Pre-trained weights are automatically downloaded from HuggingFace on first use.
2. Available Models
ADiT provides a joint pre-trained model trained on:
- MP20: Materials Project 2020 dataset (inorganic crystals, ~45K structures)
- QM9: Small organic molecules (~134K molecules)
The single checkpoint handles both crystal and molecule generation, selected via the generation_type parameter.
3. MCP Tool Usage
Crystal Generation
Generate novel periodic crystal structures (saved as CIF files):
mcp_adit_generate_structures(
generation_type="crystals", # Generate periodic crystals
num_structures=10, # Number of structures to generate
batch_size=100, # Batch size for GPU efficiency
cfg_scale=2.0, # Classifier-free guidance scale
output_dir="research/my_project/crystals"
)
Molecule Generation
Generate novel non-periodic molecules (saved as XYZ files):
mcp_adit_generate_structures(
generation_type="molecules", # Generate molecules
num_structures=10,
batch_size=100,
cfg_scale=2.0,
output_dir="research/my_project/molecules"
)
4. Parameters
| Parameter | Default | Description |
|---|---|---|
generation_type |
"crystals" |
"crystals" for periodic structures (CIF), "molecules" for non-periodic (XYZ) |
num_structures |
10 |
Total number of structures to generate |
batch_size |
100 |
Batch size (larger = faster on GPU) |
cfg_scale |
2.0 |
Classifier-free guidance scale. Higher = more typical but less diverse |
device |
"auto" |
Device: "auto", "cpu", or "cuda" |
output_dir |
auto | Output directory. Auto-creates under research dir |
What ships with it
12 files 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.
- examples/molecules/generation_metadata.json 164 B
- examples/molecules/molecule_0000.xyz 1.7 KB
- examples/molecules/molecule_0001.xyz 1.6 KB
- examples/molecules/molecule_0002.xyz 1.3 KB
- examples/molecules/molecule_0003.xyz 1.4 KB
- examples/molecules/molecule_0004.xyz 1.3 KB
- examples/molecules/molecule_0005.xyz 1.4 KB
- examples/molecules/molecule_0006.xyz 1.1 KB
- examples/molecules/molecule_0007.xyz 1.5 KB
- examples/molecules/molecule_0008.xyz 1012 B
- examples/molecules/molecule_0009.xyz 1.4 KB
- examples/molecules/README.md 1.0 KB
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.
- today First seen · 128 lines · 30 tokens per session scan A dfe3c554cc70
ml-generative-adit is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 1,174 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
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