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 surrogate-validationgit 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/surrogate-validation)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/surrogate-validation"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/surrogate-validation/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/surrogate-validation"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/surrogate-validation.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 | $0.00003 | $0.07488 |
| Opus 5 | $0.00002 | $0.03744 |
| Sonnet 5 | $0.00001 | $0.01498 |
| Haiku 4.5 | $0.00000 | $0.00749 |
Grade A, and why
surrogate-validation 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 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.
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 — 665 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Surrogate Model Validation
Description
This skill covers validation of surrogate models for materials and chemistry: leakage-safe train/validation/test splits, nested cross-validation, leave-one-group-out validation, uncertainty calibration, parity and residual diagnostics, interpolation versus extrapolation checks, descriptor-domain coverage, learning curves, baseline and ablation studies, distribution-shift testing, and validation of Bayesian optimization or active learning loops. Invoke this skill before trusting a surrogate model for screening, optimization, scientific claims, or automated data acquisition.
Domain Context
A surrogate model is only useful if its validation protocol matches the way it will be used. A random split over a materials database may look strong because near-duplicate crystal prototypes, adjacent compositions, or repeated DFT tasks appear in both train and test sets. The same model may fail when asked to extrapolate to a new chemistry family, defect type, surface termination, or higher-fidelity calculation. Validation is therefore a statement about a deployment scenario, not a single score.
Materials and chemistry datasets are especially vulnerable to correlation leakage. Structures with the same prototype but different lattice constants can share almost identical descriptors. Consecutive active-learning iterations can contain many near-duplicate candidates. Database-derived records can duplicate the same material under several IDs. Random train/test splits treat these as independent, inflating R2 and deflating MAE.
Uncertainty validation is separate from point-error validation. A model can have acceptable MAE but be overconfident in extrapolation regions, making it dangerous for Bayesian optimization or active learning. Conversely, a conservative model may have wide intervals that cover most held-out labels but are too broad to guide decisions. Reliability diagrams, calibration curves, conformal intervals, and domain-shift tests are needed before using uncertainty as a query signal.
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 · 665 lines · 3 tokens per session scan A 454ad344ad48
surrogate-validation is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 3 tokens to every session and 7,488 once invoked, about $0.0000 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 2026-08-31.
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