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-committee-uncertaintynpx skills add learningmatter-mit/AtomisticSkills --skill ml-committee-uncertaintygit 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-committee-uncertainty)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/ml-committee-uncertainty"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/ml-committee-uncertainty.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 | $0.00036 | $0.02021 |
| Opus 5 | $0.00018 | $0.01010 |
| Sonnet 5 | $0.00007 | $0.00404 |
| Haiku 4.5 | $0.00004 | $0.00202 |
Grade A, and why
ml-committee-uncertainty 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MACE Committee Model Uncertainty Quantification
Goal
To estimate the epistemic uncertainty of a MACE MLIP by running inference with a committee (ensemble) of independently trained models. Structures where the committee disagrees strongly (high energy or force variance) are flagged as candidates for DFT labelling, supporting active learning workflows and validating MLIP reliability in under-sampled regions of configuration space.
The uncertainty estimate is:
- Energy uncertainty: standard deviation of predicted energies across committee members (meV/atom)
- Force uncertainty: component-wise force RMSE of the across-committee
standard deviations (meV/Å): take the sample standard deviation for every
atom and Cartesian component, then take one root-mean-square over all
3Ncomponents. This is the conventional MLIP force-RMSE reduction used by MACE reporting.
[!WARNING] Energy std is only a valid disagreement signal when every committee member shares the same energy reference — the same training dataset, level of theory, and atomic reference energies (
E0). If the committee instead pools several independently pretrained foundation models (e.g. different MACE-MP / MACE-OMAT / MACE-MATPES releases) rather than same-data/different-seed checkpoints, their absolute energies are not on a common scale: an energy-std ranking will mostly reflect per-model reference-energy offsets, not genuine epistemic disagreement. For this heterogeneous committee flavor, rank structures by force disagreement instead — forces are invariant to each model's arbitrary atomic reference energy, so they remain a reliable cross-model signal. See the heterogeneous-committee note under Constraints.
Instructions
1. Obtain Committee Models
A committee requires N ≥ 3 independently trained MACE checkpoints covering the same chemical system. There are two ways to obtain them:
Option A — Train with different random seeds (recommended for fine-tuned models)
What ships with it
3 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.
- yesterday First seen · 150 lines · 36 tokens per session scan A badc91585d5b
ml-committee-uncertainty is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 2,021 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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