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-mace-finetunenpx skills add learningmatter-mit/AtomisticSkills --skill ml-mace-finetunegit 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-mace-finetune)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/ml-mace-finetune"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/ml-mace-finetune.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 | $0.02695 |
| Opus 5 | $0.00010 | $0.01347 |
| Sonnet 5 | $0.00004 | $0.00539 |
| Haiku 4.5 | $0.00002 | $0.00269 |
Grade A, and why
ml-mace-finetune 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 yesterday.
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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MACE Fine-tuning
Goal
To evaluate and improve the accuracy of a foundation MACE potential for a specific chemical system or physical property using the provided Python fine-tuning script and data-augmentation.
Instructions
- Prepare Labeled Dataset: Obtain diverse structures with high-fidelity labels (energy, forces, stress). See the
/benchmark-finetuningworkflow for details. - Custom Data Conversion: Read the source data format and write a customized conversion script if needed, formatting it for the subsequent preparation step.
- Benchmarking: Predict results on the new labels and benchmark the foundation model using ml-mlip-benchmark.
- Data Preparation: Execute
scripts/prepare_mace_data.pyto convert JSON structures to.xyzdata files. - Config Generation: Execute
scripts/generate_mace_config.pyusing the.xyzdata to producefinetune_config.yaml. - Fine-Tuning: Execute
mace_run_train --config /path/to/finetune_config.yamlto begin fine-tuning natively on the GPU. - Validation: Verify convergence and compare against the benchmarked foundation metrics.
- Registration: Use the
register_modeltool to register the newly fine-tuned model checkpoint into the local registry so future research tasks can discover and reuse it.
Training Configuration
MACE fine-tuning is divided into a data preparation step, a configuration generation step, and a standard native training run. The script scripts/prepare_mace_data.py generates .xyz files, and scripts/generate_mace_config.py converts arguments into a fully-formed finetune_config.yaml configuration compatible with the MACE default parser.
Basic Arguments (Data Prep Script)
| Key | Type | Default | Description |
|---|---|---|---|
--data |
str | (Required) | Path to JSON file containing ASE/pymatgen structure dictionaries |
--output-dir |
str | ./fine_tuning_data |
Directory to save the converted .xyz data |
--val-split |
float | 0.1 | Fraction of data to set aside for validation |
--seed |
int | 42 | Random seed for validation splitting |
--vasp-stress-conversion |
flag | - | If set, multiplies stress values by -1/160.2x to convert VASP raw kB to eV/ų |
What ships with it
9 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/mace-wbm-finetune/finetune_config.yaml 3.7 KB
- examples/mace-wbm-finetune/label_distributions.png 150 KB
- examples/mace-wbm-finetune/README.md 2.7 KB
- examples/mace-wbm-finetune/training_history.json 3.6 KB
- examples/mace-wbm-finetune/training_history.png 337 KB
- examples/mace-wbm-finetune/valid.xyz 25 KB
- scripts/extract_mace_logs.py 6.0 KB runs code
- scripts/generate_mace_config.py 7.3 KB runs code
- scripts/prepare_mace_data.py 9.5 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.
- yesterday First seen · 160 lines · 21 tokens per session scan A fbdfaa0e9acf
ml-mace-finetune is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 2,695 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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