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-mlip-benchmarknpx skills add learningmatter-mit/AtomisticSkills --skill ml-mlip-benchmarkgit 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-mlip-benchmark)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/ml-mlip-benchmark"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/ml-mlip-benchmark.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 | $0.00034 | $0.01734 |
| Opus 5 | $0.00017 | $0.00867 |
| Sonnet 5 | $0.00007 | $0.00347 |
| Haiku 4.5 | $0.00003 | $0.00173 |
Grade A, and why
ml-mlip-benchmark 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark Machine Learning Interatomic Potentials (MLIP)
This skill evaluates the accuracy of a given MLIP against an existing ground-truth dataset (e.g., DFT calculations or a higher-fidelity foundation potential). It computes the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for both energy (per atom) and atomic forces, and optionally stress. It also generates parity plots for visual inspection of the model's correlation.
Prerequisites
- Model Loaded: An MLIP must be currently active via a
load_modelMCP tool call (e.g.,mcp_mace_load_model,mcp_fairchem_load_model,mcp_matgl_load_model). - Labeled Data: A JSON dataset where each entry contains a structural dictionary under
"structure", along with scalar/vector ground truth values for"energy","forces", and optionally"stress". This is identical to the format used inml-mlip-training. (Data can be generated using Atomate2 MongoDB queries or MD sampling + labeling).
Instructions
1. Run Benchmark metrics
Use the .agents/skills/ml-mlip-benchmark/scripts/run_benchmark.py script to perform inference across the dataset and compute global error metrics.
Environment requirement: This script instantiates the MLIP models directly and thus must be executed within the target model's conda environment (e.g., mace-agent, fairchem-agent, or matgl-agent). Run this using the run_command via conda run -n <model_agent> python ....
conda run -n <MODEL-AGENT-ENV> python .agents/skills/ml-mlip-benchmark/scripts/run_benchmark.py \
--data_path <path_to_labeled_data.json> \
--model <model_name_or_path> \
--backend <mace|fairchem|matgl> \
--output <path_to_save_benchmark_results.json>
Note: The script utilizes src.utils.mlips.loader.load_wrapper to abstract backend details.
2. Generate Parity Plots
Once run_benchmark.py finishes, it writes a comprehensive JSON file containing original targets alongside the model's predictions and numerical metrics. Visualize these using the plotting script.
What ships with it
8 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 · 117 lines · 34 tokens per session scan A cc57169a6200
ml-mlip-benchmark is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 1,734 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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