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/mat-diffusion-analysisnpx skills add learningmatter-mit/AtomisticSkills --skill mat-diffusion-analysisgit 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/mat-diffusion-analysis)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-diffusion-analysis"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-diffusion-analysis.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 | $0.00021 | $0.00975 |
| Opus 5 | $0.00010 | $0.00487 |
| Sonnet 5 | $0.00004 | $0.00195 |
| Haiku 4.5 | $0.00002 | $0.00097 |
Grade A, and why
mat-diffusion-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diffusion Analysis
Goal
To accurately calculate the ionic diffusivity ($D$) and activation energy ($E_a$) of specific atomic species in a material using Molecular Dynamics (MD) trajectories and the Arrhenius relation: $D(T) = D_0 \exp\left(-\frac{E_a}{k_B T}\right)$.
Instructions
-
MD Preparation: Run NVT or NPT MD simulations at multiple temperatures (typically 4-6 points between 600K and 1200K).
- Use the
run_mdtool from a relevant potential skill (e.g., mace or matgl). - Batch Processing: You can pass a directory or a list of CIF paths to
structure_datato run multiple MD simulations concurrently via the MCP tool. - Supercell Expansion: Ensure supercells are sufficiently large (> 10 Å in all dimensions). The
run_mdtool natively supports this via thesupercell_min_lengthargument (defaults to 10.0 Å) which performs orthogonal expansion automatically. - Optimization: Use the
diffusionmonitor (see mat-md-monitors) to automatically stop simulations once the transport properties have converged.
Note: If themace.run_md( structure_data=["candidates/A.cif", "candidates/B.cif"], temperature=600, supercell_min_length=10.0, monitor=True, monitor_type="diffusion", monitor_params={"specie": "Li", "threshold": 0.05, "check_interval_ps": 5.0} )diffusionmonitor triggers an early stop, it will automatically save thediffusion_{specie}.jsonandmsd_{specie}.pngdirectly into the trajectory output directory. You can skip Step 2 and proceed directly to Step 3 for any trajectories that converged early.
- Use the
-
Individual Diffusivity Analysis: For each temperature directory that did not hit the early stopping criteria, run the analysis script to extract the diffusivity and Mean Square Displacement (MSD).
# Env: base-agent python .agents/skills/mat-diffusion-analysis/scripts/analyze_diffusion.py \ results/md_600K/trajectory.traj \ --species Li \ --temperature 600 \ --ignore_ps 5.0 \ --output_dir results/md_600K--ignore_ps: Time to skip for equilibration. Default is 5.0 ps.- The script automatically detects the frame interval from the
.logfile if present.
What ships with it
10 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/LGPS/arrhenius_plot.png 40 KB
- examples/LGPS/LGPS_221.cif 11 KB
- examples/LGPS/msd_Li_1000K.png 45 KB
- examples/LGPS/msd_Li_600K.png 46 KB
- examples/LGPS/msd_Li_700K.png 46 KB
- examples/LGPS/msd_Li_800K.png 46 KB
- examples/LGPS/msd_Li_900K.png 47 KB
- examples/LGPS/README.md 2.7 KB
- scripts/analyze_diffusion.py 6.9 KB runs code
- scripts/calculate_activation_energy.py 8.4 KB runs code
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 · 69 lines · 21 tokens per session scan A 5eea08823da3
mat-diffusion-analysis is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 975 once invoked, about $0.0001 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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