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-xrd-calculatornpx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-calculatorgit 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-xrd-calculator)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-xrd-calculator"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-xrd-calculator.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 | $0.00025 | $0.00624 |
| Opus 5 | $0.00013 | $0.00312 |
| Sonnet 5 | $0.00005 | $0.00125 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
mat-xrd-calculator 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
XRD Spectrum Calculation
This skill calculates the X-ray Diffraction (XRD) pattern of a crystal structure using pymatgen. It identifies diffraction peaks, their intensities, and associated (hkl) indices.
Requirements
- Conda environment:
base-agent pymatgenmatplotlib
Usage
The primary script for this skill is calculate_xrd.py. It takes a structure file as input and generates a JSON file with the diffraction data and a plot of the intensities versus $2\theta$.
Command Line Interface
python .agents/skills/mat-xrd-calculator/scripts/calculate_xrd.py <structure_file> --output_dir <output_dir> --wavelength <wavelength>
Arguments
structure: Path to the input structure file (e.g.,POSCAR,CIF).--output_dir: (Optional) Directory to save the results. Defaults to the current directory.--wavelength: (Optional) Radiation wavelength or source name (e.g.,CuKa,MoKa,CrKa). Defaults toCuKa($1.54184$ Å).--symprec: (Optional) Symmetry precision for identifying equivalent peaks. Defaults to0.1.
Output Files
<filename>_xrd.json: Contains $2\theta$ positions, intensities, d-spacings, and (hkl) indices.<filename>_PV_xrd.png: A plot of the simulated XRD spectrum (Pseudo-Voigt model).
Example
To calculate the XRD pattern for LiFePO4:
```bash
conda activate base-agent
python .agents/skills/mat-xrd-calculator/scripts/calculate_xrd.py .agents/skills/mat-xrd-calculator/examples/LiFePO4/LiFePO4.cif --output_dir .agents/skills/mat-xrd-calculator/examples/LiFePO4
## Foundation Potential Recommendations
Since XRD is a purely geometric property of the crystal structure, it does not require a machine learning interatomic potential (MLIP) for the calculation itself. However, it is **highly recommended** to perform a structure relaxation using a high-quality MLIP (e.g., MACE, CHGNet) before calculating the XRD pattern to ensure the structure is at its energy minimum.
For recommendations on relaxation models, see the [ml-foundation-potentials](file:///home/bdeng/projects/AtomisticSkills/.agents/skills/ml-foundation-potentials/SKILL.md) skill.
---
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/Li3PO4/Li3PO4_PV_xrd.png 156 KB
- examples/Li3PO4/Li3PO4_xrd.json 99 KB
- examples/Li3PO4/Li3PO4.cif 1.1 KB
- examples/LiFePO4/LiFePO4_PV_xrd.png 166 KB
- examples/LiFePO4/LiFePO4_xrd.json 101 KB
- examples/LiFePO4/LiFePO4_xrd.png 87 KB
- examples/LiFePO4/LiFePO4.cif 2.5 KB
- examples/LiFePO4/README.md 799 B
- scripts/calculate_xrd.py 3.4 KB runs code
- scripts/xrd_utils.py 2.5 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 · 59 lines · 25 tokens per session scan A f4935f349ad6
mat-xrd-calculator is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 624 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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