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 skills add SFETNI/Deep-Matter-Chem-Skills --skill active-learning-mlpgit clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-SkillsWrote 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/sfetni/deep-matter-chem-skills/active-learning-mlp)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/active-learning-mlp"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/active-learning-mlp/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/active-learning-mlp"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/active-learning-mlp.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00005 | $0.09842 |
| Opus 5 | $0.00003 | $0.04921 |
| Sonnet 5 | $0.00001 | $0.01968 |
| Haiku 4.5 | $0.00001 | $0.00984 |
Grade A, and why
active-learning-mlp scanned grade A with 1 finding 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 12d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(cmd, check=True) How it starts
The opening of the file, as written. The whole thing — 796 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Active Learning for ML Interatomic Potentials
Description
This skill covers the systematic active learning workflow for iteratively building ML interatomic potential training datasets: initial dataset construction, uncertainty-guided exploration MD, DFT oracle labeling, query strategy, dataset versioning, retraining cadence, and stopping criteria. Active learning is the primary method for constructing compact, non-redundant MLP training datasets that generalize across the thermodynamic conditions and structural environments targeted for production MD. Invoke this skill when building a new MLP training dataset from scratch, when a trained MLP fails in MD outside its training distribution, or when designing a data generation campaign for a new chemical system.
Domain Context
An MLP trained on a finite DFT dataset is accurate only within the distribution of configurations that dataset samples. The critical challenge is that the target distribution — all configurations visited during production MD — is unknown before the potential exists. Active learning resolves this circularity by iterating:
- Explore: Run MD with the current provisional MLP to generate candidate structures.
- Query: Select from the candidates those most likely to improve the model (high uncertainty, high diversity, or in undersampled regions).
- Label: Compute DFT energies, forces, and stresses for the selected structures.
- Retrain: Add the new DFT data to the training set and retrain or fine-tune the MLP.
- Evaluate: Test whether the model has reached the target accuracy and stability.
The loop continues until a stopping criterion is met: typically when the fraction of exploration MD frames with high uncertainty falls below a threshold, indicating that the MLP has learned the accessible configuration space.
Why not random sampling? DFT calculations are expensive (minutes to hours each). Random structures are overwhelmingly drawn from high-energy, irrelevant regions of configuration space. Active learning focuses the DFT budget on structures that are both physically accessible (from MD) and maximally informative (high model uncertainty).
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.
- 12d ago First seen · 796 lines · 5 tokens per session scan A f58df10b881f
active-learning-mlp is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 9,842 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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