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-defect-energy-dftnpx skills add learningmatter-mit/AtomisticSkills --skill mat-defect-energy-dftgit 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-defect-energy-dft)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-defect-energy-dft"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-defect-energy-dft.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 | $0.00031 | $0.01440 |
| Opus 5 | $0.00015 | $0.00720 |
| Sonnet 5 | $0.00006 | $0.00288 |
| Haiku 4.5 | $0.00003 | $0.00144 |
Grade A, and why
mat-defect-energy-dft 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Point-Defect Formation Energy (DFT)
Goal
To calculate the formation energy of point defects (vacancies, substitutions, interstitials) including charged defect states and finite-size corrections using DFT (VASP) via atomate2 workflows. This produces formation energy diagrams showing defect charge transition levels as a function of Fermi energy.
$$E_f[D^q] = E[D^q] - E[\text{bulk}] + \sum_i \Delta n_i \mu_i + q(E_\text{VBM} + \Delta E_F) + E_\text{corr}$$
where $q$ is the charge state, $E_\text{VBM}$ is the valence band maximum, $\Delta E_F$ is the Fermi energy relative to VBM, and $E_\text{corr}$ is the finite-size correction (Freysoldt/FNV).
Instructions
1. Obtain Bulk Structure
Start with a relaxed primitive cell:
mcp_base_search_materials_project_by_formula(formula="MgO", save_to_file="MgO.cif")
2. Generate Defect Structures
Use pymatgen-analysis-defects to generate all symmetry-unique defect supercells with charge states:
# Env: base-agent
python .agents/skills/mat-defect-energy-dft/scripts/generate_defect_structures.py \
--bulk MgO.cif \
--supercell_size 3 3 3 \
--defect_type vacancy \
--charge_range -2 2 \
--output dft_defects/
This generates:
- POSCAR files for each defect × charge state
- A
defect_index.jsonmapping defect names to charge states and structures - Pristine supercell for the bulk reference
3. Run DFT Calculations (atomate2)
Submit calculations via the atomate2 MCP tool:
# Bulk supercell reference
mcp_atomate2_run_atomate2_vasp_calculation(
structures_path="dft_defects/pristine_supercell.cif",
output_dir="./dft_bulk/",
calculation_type="static",
preset_type="matpes-pbe",
execution_mode="remote"
)
# All defect structures
mcp_atomate2_run_atomate2_vasp_calculation(
structures_path="dft_defects/",
output_dir="./dft_defect_calcs/",
calculation_type="relaxation",
preset_type="matpes-pbe",
execution_mode="remote"
)
What ships with it
10 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/MgO_charged_vacancy/build_diagram.py 6.9 KB runs code
- examples/MgO_charged_vacancy/charged_formation_energies.json 31 KB
- examples/MgO_charged_vacancy/charged_formation_energy_diagram.png 136 KB
- examples/MgO_charged_vacancy/defect_index.json 3.5 KB
- examples/MgO_charged_vacancy/dft_energies.json 364 B
- examples/MgO_charged_vacancy/formation_energy_diagram_PBE.png 145 KB
- examples/MgO_charged_vacancy/full_results.json 876 B
- examples/MgO_charged_vacancy/README.md 5.4 KB
- scripts/generate_defect_structures.py 7.3 KB runs code
- scripts/parse_defect_results.py 14 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.
- yesterday First seen · 142 lines · 31 tokens per session scan A 284bfdeb80f5
mat-defect-energy-dft is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 1,440 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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