matminer-property-regression-starter

matminer-property-regression-starter is a skill for Claude Code, Codex from ma-compbio-lab/SkillFoundry. It costs 0 tokens per session (209 once invoked), scanned A, original, Apache-2.0.

A small materials-property prediction example that converts chemical formulas into Matminer features and fits a deterministic regression model. It reports model fit details and predictions for held-out examples.

In plain words
What is it for?
Use it to build and inspect a toy regression workflow for predicting a materials property from simple formulas.
Why use it?
It provides a lightweight template for connecting material composition to property prediction before using a larger dataset.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is ./slurm/envs/materials/bin/python skills/materials-science-and-engineering/matminer-property-regression-starter/scripts/run_matminer_property_regression.py --ou.

Good fit Use it to build and inspect a toy regression workflow for predicting a materials property from simple formulas.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/ma-compbio-lab/SkillFoundry
agentmods
npx agentmods add skills/ma-compbio-lab/skillfoundry/matminer-property-regression-starter

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for matminer-property-regression-starter

README.md
[![agentmods](https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/matminer-property-regression-starter/github.svg)](https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/matminer-property-regression-starter)
Your own site
<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/matminer-property-regression-starter"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/matminer-property-regression-starter/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.

agentmods 80×15 button for matminer-property-regression-starter

Your own site · 80×15
<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/matminer-property-regression-starter"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/matminer-property-regression-starter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 209 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.00209
Opus 5 $0.00000 $0.00105
Sonnet 5 $0.00000 $0.00042
Haiku 4.5 $0.00000 $0.00021

Measured 6d ago against content hash 209c26834fcb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

matminer-property-regression-starter 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 6d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/run_matminer_property_regression.py, tests/test_run_matminer_property_regression.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/materials-science-and-engineering/matminer-property-regression-starter/SKILL.md · 27 lines

What it actually says

Matminer Property Regression Starter

Use this skill to featurize simple formulas with Matminer and fit a deterministic toy regression model for materials-property prediction.

What This Skill Does

  • converts formulas into composition features with Matminer
  • fits a small deterministic regression model
  • reports feature count, train fit quality, and holdout predictions

When To Use It

  • when a user asks for a starter on materials-property prediction
  • when you need a lightweight local template before connecting to a larger materials dataset
  • when you want a runnable example for the materials-property-prediction taxonomy leaf

Run

./slurm/envs/materials/bin/python skills/materials-science-and-engineering/matminer-property-regression-starter/scripts/run_matminer_property_regression.py --out scratch/materials/property_regression_summary.json

Notes

  • This is a toy local regression starter, not a benchmark-quality predictive model.
  • Pair it with larger curated datasets later if you need scientific performance claims.
Files

What ships with it

8 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.

Changes

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.

  1. 6d ago First seen · 27 lines · 0 tokens per session scan A 209c26834fcb

Subscribe to this mod's changes

matminer-property-regression-starter is a skill published in the GitHub repository ma-compbio-lab/SkillFoundry (39 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 209 tokens. 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.

Related

Other skills, from other repositories

esmfold2

Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release…

aipoch/open-science · 223 tokens

scgpt

Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For…

aipoch/open-science · 89 tokens

evo2

Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring…

aipoch/open-science · 83 tokens

cv-classification

Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.

aiming-lab/AutoResearchClaw · 26 tokens

cv-detection

Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.

aiming-lab/AutoResearchClaw · 30 tokens

experimental-design

Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.

aiming-lab/AutoResearchClaw · 25 tokens