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-epw-mobilitynpx skills add learningmatter-mit/AtomisticSkills --skill mat-epw-mobilitygit 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-epw-mobility)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-epw-mobility"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-epw-mobility.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 | $0.00041 | $0.04128 |
| Opus 5 | $0.00020 | $0.02064 |
| Sonnet 5 | $0.00008 | $0.00826 |
| Haiku 4.5 | $0.00004 | $0.00413 |
Grade A, and why
mat-epw-mobility 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 — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mat-epw-mobility
Goal
To compute the intrinsic phonon-limited carrier mobility $\mu(T)$ of a 2D semiconductor in the Self-Energy Relaxation Time Approximation (SERTA), together with the mode-resolved electron-phonon coupling matrix elements $|g(k, q, \nu)|$, from first principles using the Quantum ESPRESSO + EPW pipeline. The route interpolates the electron-phonon vertex onto dense fine grids via maximally localized Wannier functions and applies the 2D Frohlich long-range kernel needed for polar monolayers.
Background
Carrier mobility limited by phonon scattering requires the electron-phonon
matrix elements $g_{mn\nu}(k, q)$ on grids far denser than any tractable DFPT
calculation. EPW solves this by computing $g$ on a coarse q-grid with DFPT,
transforming to a maximally localized Wannier basis, and interpolating to fine
k/q meshes. For 2D polar materials, the long-range Frohlich part of $g$ diverges
as $1/q$ near the zone centre and must be treated with a 2D-truncated Coulomb
kernel (lpolar, system_2d), otherwise $\mu$ collapses by a factor of ~3.
Unlike the DFT+AMSET route in mat-dft-electronic-transport, which uses a momentum-relaxation-time approximation on VASP band structures, this skill computes the full first-principles electron-phonon vertex. It is complementary to mat-dft-electron-phonon (which targets temperature-dependent bandgap renormalization) and to mat-phonon (MLIP phonons).
The pipeline is a DAG. Steps 3 and 5 are optional (a standalone dielectric check and a DFPT $|g|$ benchmark); the transport path is 1 -> 2 -> 4 -> 6 -> 7 -> 9.
1 SCF
+-------------+--------------+
v v v
3 DFPT-Gamma 4 DFPT 2 NSCF
(eps_inf, Z*) uniform-q (explicit k)
(optional) | |
v |
6 pp.py gather |
| \ |
| v v
| 7 Wannierize
| | |
v v v
9 EPW 8 EPW (8 also <- 5 DFPT single-q
SERTA prtgkk benchmark, optional)
mu(T) |g|
What ships with it
15 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.
- examples/zrs2-monolayer/README.md 3.5 KB
- examples/zrs2-monolayer/zrs2_monolayer.cif 521 B
- resources/inputs/epw_mob.in 1.4 KB
- resources/inputs/epw_prtgkk.in 1018 B
- resources/inputs/epw_write.in 1.3 KB
- resources/inputs/nscf.in 1.0 KB
- resources/inputs/ph_gamma.in 369 B
- resources/inputs/ph_single_q.in 692 B
- resources/inputs/ph_uniform.in 459 B
- resources/inputs/scf.in 966 B
- scripts/compare_reference.py 2.0 KB runs code
- scripts/gen_kpoints.py 2.1 KB runs code
- scripts/parse_epw_prtgkk.py 4.0 KB runs code
- scripts/parse_prt.py 3.8 KB runs code
- scripts/parse_wout.py 2.6 KB runs code
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 · 286 lines · 41 tokens per session scan A a496fb66cbc7
mat-epw-mobility is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 4,128 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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