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-digitizernpx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-digitizergit 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-digitizer)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-xrd-digitizer"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-xrd-digitizer.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 | $0.00027 | $0.01033 |
| Opus 5 | $0.00014 | $0.00517 |
| Sonnet 5 | $0.00005 | $0.00207 |
| Haiku 4.5 | $0.00003 | $0.00103 |
Grade A, and why
mat-xrd-digitizer 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
XRD Digitizer
Goal
To convert an image or screenshot of an X-Ray Diffraction (XRD) pattern into a digitized, numeric .xy data file, which can then be used by downstream analysis tools like mat-xrd-phase-analysis.
This skill leverages the AI Agent's built-in Vision/Language Model (VLM) capabilities. The Agent will visually parse the provided image to extract key peak positions (2-theta) and approximate relative intensities, and then use a provided script to mathematically generate a representative pseudo-Voigt profile.
Instructions
1. Extract Peaks Visually
Provide the agent with an image (e.g., screenshot) of the XRD plot. The agent will visually inspect the plot and identify the coordinates of the major peaks.
Handling Multiple Curves/Colors: If the image contains multiple XRD patterns, the user should specify which curve to digitize by its color, label, or position (e.g., "digitize the red curve" or "digitize the curve labeled 'sample A'"). The agent will then selectively extract peaks from only that specific curve.
Agent Action: The agent should:
- Create a JSON file (e.g.,
peaks.json) containing the extracted peaks as an array of objects for the target curve. CRITICAL: You must ensure every single visible peak, including the tiny minor peaks, is reported and digitized to ensure accurate full-profile refinement downstream. - Save a copy of the original image (e.g.,
original_plot.png) in the same directory as the JSON file for future reference.
Example peaks.json format:
[
{"2theta": 8.8, "intensity": 0.05, "fwhm": 0.3},
{"2theta": 15.8, "intensity": 0.08, "fwhm": 0.3},
{"2theta": 33.1, "intensity": 1.00, "fwhm": 0.3}
]
Note: intensity should be normalized between 0 and 1.0 (where the highest peak is 1.0). fwhm defaults to 0.3.
2. Generate the Digitized .xy File
Use the provided script to generate the experimental .xy file based on the extracted peaks.
# Env: base-agent
python .agents/skills/mat-xrd-digitizer/scripts/digitize_plot.py peaks.json --output digitized_plot.xy --min-x 5.0 --max-x 80.0
What ships with it
6 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 · 84 lines · 27 tokens per session scan A e2479e7fa9db
mat-xrd-digitizer is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 1,033 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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