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 uncertainty-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/uncertainty-mlp)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/uncertainty-mlp"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/uncertainty-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/uncertainty-mlp"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/uncertainty-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 | $0.00006 | $0.13215 |
| Opus 5 | $0.00003 | $0.06607 |
| Sonnet 5 | $0.00001 | $0.02643 |
| Haiku 4.5 | $0.00001 | $0.01321 |
Grade B, and why
uncertainty-mlp scanned grade B with 2 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 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
| Overconfident extrapolation — ensemble collapse | σ_F is uniformly low (< σ_low) for all MD frames including frames where the MD trajectory later diverges; the model fails catastrophically without any warning from the 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 — 889 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Uncertainty Quantification for ML Interatomic Potentials
Description
This skill covers practical uncertainty quantification (UQ) for machine-learned interatomic potentials: committee (ensemble) models, model-deviation computation for forces, energies, and stresses, calibration of uncertainty thresholds against DFT errors, SOAP-based structural dissimilarity as a complementary out-of-distribution (OOD) detector, conformal prediction for rigorous confidence intervals, uncertainty propagation through MD trajectories, and stopping/extrapolation criteria. Invoke this skill when designing the active learning acquisition strategy for a new MLP campaign, when diagnosing silent extrapolation in production MD, or when quantifying the reliability of MLP predictions for properties that depend on the model's generalization behavior.
Domain Context
An MLP evaluated on a structure outside its training distribution will produce incorrect forces with no error message and no indication of failure. The only way to detect this before a catastrophic MD trajectory is through an uncertainty estimate that flags the structure as novel relative to the training set. UQ for MLPs is therefore a safety mechanism, not a statistical nicety.
Epistemic versus aleatoric uncertainty. Epistemic uncertainty arises from insufficient training data: the model has not seen enough structures to constrain its predictions in a region of configuration space. This type of uncertainty can be reduced by adding more training data — it is the signal that drives active learning. Aleatoric uncertainty arises from irreducible noise in the labels (DFT numerical noise, finite k-mesh errors, SCF threshold effects). For well-converged DFT labels (EDIFF=1e-6, PREC=Accurate), aleatoric uncertainty is typically < 1 meV/Å for forces and is negligible relative to model extrapolation errors. In practice, committee model disagreement primarily measures epistemic uncertainty, but cannot cleanly separate it from aleatoric contributions.
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 · 889 lines · 6 tokens per session scan B ddfa147780cc
uncertainty-mlp is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 6 tokens to every session and 13,215 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it B with 2 findings (strips warnings and disclaimers, 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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