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-md-probability-densitynpx skills add learningmatter-mit/AtomisticSkills --skill mat-md-probability-densitygit 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-md-probability-density)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-md-probability-density"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-md-probability-density.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 | $0.00026 | $0.00865 |
| Opus 5 | $0.00013 | $0.00432 |
| Sonnet 5 | $0.00005 | $0.00173 |
| Haiku 4.5 | $0.00003 | $0.00086 |
Grade A, and why
mat-md-probability-density 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MD Probability Density Visualization
Goal
To visualize the spatial probability density of mobile ions (e.g., Li, Na) from an MD simulation trajectory. This helps in understanding conduction pathways and identifying preferred occupation sites within the crystal structure. The output is a volumetric data object in CHGCAR format, which can be easily visualized using VESTA.
Instructions
-
MD Simulation: Run an MD simulation at an appropriate temperature to observe sufficient diffusion events.
Note: Short MD trajectories (e.g., ≤10 ps) often have too few discrete ion hops to naturally form continuous probability density tubes. The resulting density will look like isolated blobs exactly at the crystal lattice sites. To visualize continuous macroscopic diffusion pathways for short trajectories, use the
--logcompression flag to mathematically connect the sparse pathways.- The trajectory is typically saved to
trajectory.traj. - Ensure
supercell_min_lengthis reasonably large (>10 Å) to avoid finite-size artifacts in the density mapping. - Allow the simulation to run long enough so that the ions sample the entire available volume (e.g., 50-100 ps or more).
- The trajectory is typically saved to
-
Calculate Probability Density: Use the provided script to extract the fractional coordinates of the targeted species over time and convert them into a spatial density grid.
# Env: base-agent python .agents/skills/mat-md-probability-density/scripts/calculate_probability_density.py \ results/md_600K/trajectory.traj \ --species Li \ --interval 0.2 \ --ignore_ps 5.0 \ --output_chgcar results/md_600K/CHGCAR_proba--species: The specific diffusing ion to visualize.--interval: Grid spacing in Angstroms (0.1 to 0.5 is recommended). Smaller values give smoother isosurfaces but take longer to process and generate larger files. Defaults to0.2Å.--ignore_ps: The equilibration time to discard from the beginning of the trajectory.- The script will automatically detect the frame time step if a
.logfile is available alongside the.trajfile.
What ships with it
5 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.
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 · 52 lines · 26 tokens per session scan A f6b05b04bd2d
mat-md-probability-density is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 865 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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