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-monitorsnpx skills add learningmatter-mit/AtomisticSkills --skill mat-md-monitorsgit 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-monitors)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-md-monitors"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-md-monitors.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 | $0.00023 | $0.01026 |
| Opus 5 | $0.00012 | $0.00513 |
| Sonnet 5 | $0.00005 | $0.00205 |
| Haiku 4.5 | $0.00002 | $0.00103 |
Grade A, and why
mat-md-monitors 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molecular Dynamics
Goal
To perform stable and accurate molecular dynamics simulations using MLIPs, ensuring physical correctness and avoiding common "explosions" associated with neural network potentials.
Instructions
1. Monitoring Stability
MD stability monitoring is integrated directly into the run_md tool via ASE callbacks. This ensures zero-latency response to instabilities and simplifies the simulation workflow.
-
Enable Monitoring: Set
monitor=Trueand specifymonitor_type(single string or list).explosion: Safety check. Stops if T > 10,000K or NaN. Recommended for all unstable simulations.equilibration: Convergence check. Stops once temperature and potential energy stabilize (e.g., for production runs).overshoot: Thermostat check. Stops if T deviates significantly from target (T-target > 200K).volume: NPT stability check. Stops if volume expands by 2x or contracts to 0.2x of initial.diffusion: Convergence check for transport properties. Stops once the relative error of diffusivity for a specificspecie(default Li) falls below athreshold(default 0.1).- Parameters:
specie,threshold,check_interval_ps(default 5.0),ignore_ps(initial equilibration to skip, default 5.0).
- Parameters:
quenching: Linear temperature ramp. Updates the thermostat target every step to move fromtemperaturetotemperature_endover a specified number ofsteps.- Best Practice: Use
dyn.set_temperature(temperature_K=T)inside the ramping callback. This is critical for thermostats likeLangevinto update internal noise/coupling coefficients. - Advanced Thermostats: For
NoseHooverChainNVTandMTKNPT, whereset_temperaturemight be missing, manual updates to internal attributes (_kT,_Q,_W) are required to keep the damping frequency consistent.
- Best Practice: Use
-
Example Usage:
# MACE example with multiple monitors mace.run_md(structure, monitor=True, monitor_type=["explosion", "equilibration"])
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 · 81 lines · 23 tokens per session scan A 611892d79b81
mat-md-monitors is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 1,026 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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