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-magnetic-densitynpx skills add learningmatter-mit/AtomisticSkills --skill mat-magnetic-densitygit 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-magnetic-density)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-magnetic-density"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-magnetic-density.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 | $0.00023 | $0.02435 |
| Opus 5 | $0.00012 | $0.01218 |
| Sonnet 5 | $0.00005 | $0.00487 |
| Haiku 4.5 | $0.00002 | $0.00244 |
Grade A, and why
mat-magnetic-density scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Magnetic Density
Goal
To calculate the magnetic moments and optionally extract the spin density ($\rho_{\text{spin}}$) of magnetic materials using spin-polarized DFT calculations. This skill enables the characterization of magnetic ordering, local magnetic moments on individual atoms, and spatial distribution of spin density.
Instructions
1. Structure Preparation
Obtain or prepare the structure of the magnetic material you want to study. You can:
- Query from Materials Project using the base MCP tools (recommended - already DFT-optimized)
- Load from a local CIF/POSCAR file
- Use a previously relaxed structure
Note on relaxation: For magnetic moment calculations, relaxation is optional if using high-quality experimental or Materials Project structures. Magnetic moments are relatively insensitive to small structural variations. However, relaxation is recommended for:
- New/hypothetical structures
- Surfaces, interfaces, or defects
- Systems where you need accurate total energies (not just magnetic moments)
- Strongly correlated oxides with significant magnetic-structural coupling
2. Run Spin-Polarized DFT Calculation
Use the atomate2 MCP tool to run a spin-polarized static calculation. The mp preset (MPStaticSet) automatically enables spin polarization and applies appropriate settings for magnetic systems.
mcp_atomate2_run_atomate2_vasp_calculation(
structures_path="structure.cif", # Path to your structure file
output_dir="magnetic_calc", # Directory to save results
preset_type="mp", # MPStaticSet with PBE (includes spin polarization)
calculation_type="static", # Static calculation
)
Important - Functional Selection:
- For metallic ferromagnets (Fe, Co, Ni): Use
mppreset (MPStaticSet with PBE). PBE provides excellent accuracy for magnetic moments (typically within ~2% of experimental values)[1]. - For strongly correlated oxides (NiO, CoO, FeO): Use
mppreset (see Example 2 below). MPStaticSet automatically applies appropriate GGA+U corrections for transition metal oxides. Standard PBE fails to predict the correct insulating antiferromagnetic ground state and severely underestimates band gaps[3]. Note: +U corrections improve electronic structure but do not guarantee better magnetic moments (e.g., GGA+U may overestimate for NiO or underestimate for CoO due to missing orbital contributions)[4]. - Avoid r2SCAN for metallic ferromagnets: r2SCAN significantly overestimates magnetic moments in itinerant ferromagnets like Fe (by ~24% compared to experimental values)[2].
What ships with it
7 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 · 227 lines · 23 tokens per session scan A c7191681ccc7
mat-magnetic-density is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 2,435 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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