Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsnpx agentmods add skills/learningmatter-mit/atomisticskills/mat-kinetic-monte-carloWrote 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-kinetic-monte-carlo)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-kinetic-monte-carlo"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-kinetic-monte-carlo.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00053 | $0.03765 |
| Opus 5 | $0.00026 | $0.01883 |
| Sonnet 5 | $0.00011 | $0.00753 |
| Haiku 4.5 | $0.00005 | $0.00377 |
Grade A, and why
mat-kinetic-monte-carlo 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kinetic Monte Carlo (KMC)
Goal
Run kinetic Monte Carlo simulations to evolve a system on experimental (long) timescales using a continuous-time Markov jump process defined by elementary events and their rates.
This skill focuses on best-practice, physics-grounded KMC:
- correct rejection-free time advancement (no time-step error),
- good event/rate bookkeeping,
- detailed balance / microreversibility checks when appropriate,
- practical handling of "flickers" / superbasins,
- and robust postprocessing (event stats, MSD -> diffusivity, Arrhenius).
This skill is designed to compose with:
- chem-neb-barrier — compute migration barriers via NEB with MLIPs.
- mat-phonon — compute vibrational frequencies for hTST prefactors (Vineyard formula).
- mat-diffusion-analysis — MSD fitting, Arrhenius analysis, and D → σ via Nernst-Einstein.
MCP Server Integration
Barrier computations and phonon calculations require MLIP models (MACE, MatGL, FairChem). These run through the corresponding MCP servers or directly via wrapper scripts:
- MACE:
src/mcp_server/mace_server.py— providesrelax_structure,predict_structuretools. Used byneb-barrierandphononscripts viasrc/utils/mlips/mace/mace_wrapper.py. - MatGL:
src/mcp_server/matgl_server.py— same interface, CHGNet/M3GNet/TensorNet models. - FairChem:
src/mcp_server/fairchem_server.py— UMA/ESEN models.
KMC scripts themselves do not call MLIPs — they consume barrier/prefactor values computed upstream by the NEB and phonon skills.
When to Use KMC (and When Not)
Use KMC when:
- Dynamics are rare-event dominated (activated hops/reactions separated by long waiting times).
- You can define a set of states + elementary transitions between states with rate constants.
- You need time/length scales unreachable by MD.
Do NOT use KMC when:
- Motion is not rare-event-like (barriers ~ few kBT or less) and recrossings dominate.
- You cannot define a reasonably complete event set (or the event set changes too rapidly without on-the-fly discovery).
- The system is strongly non-Markovian at the state resolution you chose.
What ships with it
15 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/analytical_validation/.gitignore 22 B
- examples/analytical_validation/README.md 1.7 KB
- examples/analytical_validation/validate_random_walk.py 12 KB runs code
- examples/analytical_validation/validation_plot.png 81 KB
- examples/analytical_validation/validation_summary.json 2.4 KB
- examples/kmc_config.example.json 470 B
- examples/literature_validation/.gitignore 268 B
- examples/literature_validation/compute_htst_prefactor.py 9.9 KB runs code
- examples/literature_validation/prepare_h_migration.py 7.0 KB runs code
- examples/literature_validation/README.md 6.7 KB
- examples/literature_validation/validate_h_in_bcc_w.py 20 KB runs code
- scripts/analyze_kmc_msd.py 3.3 KB runs code
- scripts/build_lattice_from_structure.py 3.1 KB runs code
- scripts/run_lattice_kmc.py 9.2 KB runs code
- scripts/validate_detailed_balance.py 3.9 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.
- 4d ago First seen · 338 lines · 53 tokens per session scan A 59821ea6c0a7
mat-kinetic-monte-carlo is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (161 stars, last pushed 5d ago), licensed MIT. It adds 53 tokens to every session and 3,765 once invoked, about $0.0003 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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