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-automlnpx skills add learningmatter-mit/AtomisticSkills --skill ml-mlip-automlgit 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-automl)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/ml-mlip-automl"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/ml-mlip-automl.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 | $0.01000 |
| Opus 5 | $0.00017 | $0.00500 |
| Sonnet 5 | $0.00007 | $0.00200 |
| Haiku 4.5 | $0.00003 | $0.00100 |
Grade A, and why
ml-mlip-automl 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM-Coupled MLIP Hyperparameter Search (AutoML)
Goal
To automate the discovery of optimal training hyperparameters (learning rate, batch size, backbone freezing) by coupling the agent to an iterative execution loop. Instead of conducting exhaustive grid searches, the LLM actively interprets validation MAE trajectories per epoch and strategically deduces the next optimal parameter permutation to evaluate.
The Agentic Workflow
Instead of calling a monolithic training tool, this SKILL dictates how you (the Agent) should orchestrate a search using the individual foundation fine-tuning SKILLs (ml-mace-finetune, ml-matgl-finetune, ml-fairchem-finetune).
Execution Protocol
- Initial Anchor: Select a conservative parameter set based on the dataset size (e.g., small dataset: freeze backbone, lower LR).
- Execute Fine-Tuning: Create a dedicated subdirectory for the run (e.g.,
search_run_lr1e-3_frozen) and execute the respective MLIP'sprepare_*_data.pyand training runner. - Parse and Evaluate: Read the resulting
training_history.jsondirectly into your context. Analyze the validation loss and MAE curves (slope, signs of overfitting/forgetting). - Iterate: Deduce the next logical parameter adjustment based on the evaluation (e.g. "Validation MAE plateaued early. Action: Unfreeze backbone & lower LR to 1e-4").
- Terminate: Halt and report the best configuration once a performance target is met or degradation occurs repeatedly.
Hyperparameter Search Spaces
Different MLIP frameworks expose different tunable parameters. When deducing your next move, restrict your search space to the available features:
| Feature | ml-fairchem-finetune |
ml-matgl-finetune |
ml-mace-finetune |
|---|---|---|---|
| Freeze backbone | --freeze-backbone |
--freeze-backbone |
--freeze-backbone |
| Re-init head | N/A (Auto) | --reinit-head |
--reinit-head |
| Learning Rate | --lr (Default: 4e-4) |
--lr (Default: 1e-3) |
--lr (Default: 1e-4) |
| Batch Size | --batch-size |
--batch-size |
--batch-size |
| Scheduler | Cosine with Warmup | Cosine or Plateau | Exponential or Plateau |
| Force Loss Ratio | --force-weight |
--force-weight |
--forces-weight |
| Energy Loss Ratio | --energy-weight |
--energy-weight |
--energy-weight |
| Loss Criterion | Huber/L1/L2 | Huber/L1/L2 | universal/weighted |
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 · 58 lines · 34 tokens per session scan A 7734439bab62
ml-mlip-automl 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 1,000 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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