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-electron-phononnpx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electron-phonongit 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-electron-phonon)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-dft-electron-phonon"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-dft-electron-phonon.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 | $0.00030 | $0.00672 |
| Opus 5 | $0.00015 | $0.00336 |
| Sonnet 5 | $0.00006 | $0.00134 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
mat-dft-electron-phonon 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mat-dft-electron-phonon
Goal
To determine the impact of electron-phonon coupling on the electronic eigenstates of a solid. At $T=0$ K, quantum fluctuations (zero-point motion) slightly perturb the geometric symmetry of a perfectly static lattice, causing a contraction known as zero-point renormalization (ZPR). As temperature increases, higher phonon modes dictate further eigenenergy shifts.
Background
Standard DFT predicts bandgaps under the Born-Oppenheimer limit (fixed infinite massive ions). Computing true temperature-dependent optical properties (photoluminescence shifting, exciton broadening) mandates adding back the phonon response. The ElectronPhononMaker calculates phonon modes first (via phonopy), generates properly thermalized stochastic structural snapshots respecting the true classical/quantum Bose-Einstein occupancies, and computes the static gap for each snapshot.
Instructions
1. Construct the Electron-Phonon Workflow
Generating the inputs uses the ElectronPhononMaker. You only need to provide the target primitive structure and the temperature list you want dynamically sampled.
# Env: atomate2-agent
python .agents/skills/mat-dft-electron-phonon/scripts/generate_inputs.py --output elph_flow.json
2. Job Execution
The default script serializes the Directed Acyclic Graph (DAG) logic. Because calculating robust phonon displacements involves constructing potentially hundreds of large supercell single-point DFT calculations, ensure you map this to an established HPC worker infrastructure (jobflow or Fireworks) rather than executing interactively locally.
3. Parse Output
The termination node evaluates the mean and variance of the bandgap/band edges from all stochastically distributed geometric snapshots at a given temperature, returning the renormalized gap.
Examples
Run the DAG generation for pristine primitive Silicon.
# Env: atomate2-agent
cd .agents/skills/mat-dft-electron-phonon/examples/silicon
python ../../scripts/generate_inputs.py --output si_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.
- yesterday First seen · 53 lines · 30 tokens per session scan A 46ff57f50c39
mat-dft-electron-phonon is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 672 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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