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/mat-dft-ferroelectricnpx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-ferroelectricgit 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/mat-dft-ferroelectric)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-dft-ferroelectric"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-dft-ferroelectric.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 | $0.00029 | $0.00654 |
| Opus 5 | $0.00015 | $0.00327 |
| Sonnet 5 | $0.00006 | $0.00131 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
mat-dft-ferroelectric scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mat-dft-ferroelectric
Goal
To calculate the spontaneous polarization ($P_s$) of a ferroelectric material. Because bulk polarization is a multi-valued quantum quantity (only differences in polarization are well-defined), this skill evaluates the continuous evolution of the Berry phase starting from a high-symmetry (centrosymmetric, non-polar) reference state and progressing via linear interpolation to the low-symmetry (polar) state.
Background
Material spontaneous polarization arises when positive and negative charge centers separate, breaking inversion symmetry. By linearly mixing the atomic positions between a cubic (non-polar) and tetragonal (polar) phase, we calculate the Berry phase for electrons across the geometric path. FerroelectricMaker automates the generation of these intermediate supercells, runs VASP with LCALCPOL=True, and stitches the branches together to avoid quantum jump discontinuities.
Instructions
1. Construct the Ferroelectric Workflow
Use the provided script to generate the sequence of calculation jobs evaluating the polarization across interpolated intermediate structures.
# Env: atomate2-agent
python .agents/skills/mat-dft-ferroelectric/scripts/generate_inputs.py --output ferroelectric_flow.json
2. Job Execution
The default script simply serializes the theoretical Directed Acyclic Graph (DAG) for structural reference. Run it locally via jobflow.run_locally(flow) if VASP is available, or dispatch it to Fireworks.
3. Parse Polarization
The final job merges the electronic polarization and ionic dipoles for each intermediate image, tracing the quantum branches. Extract the total polarization (in $\mu\text{C}/\text{cm}^2$) from the terminal task document.
Examples
Run the example demonstrating the DAG generation for Barium Titanate (BaTiO$_3$).
# Env: atomate2-agent
cd .agents/skills/mat-dft-ferroelectric/examples/BaTiO3
python ../../scripts/generate_inputs.py --output batio3_flow.json
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.
- today First seen · 53 lines · 29 tokens per session scan A aa0e4ff834d2
mat-dft-ferroelectric is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 654 once invoked, about $0.0001 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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