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Agent skill

Mat Xrd Digitizer

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Digitize an image of an XRD plot into a numeric .xy data file by extracting visual peaks.

MITAuto-check passed

Install Mat Xrd Digitizer

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-digitizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills mat-xrd-digitizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mat-xrd-digitizer .claude/skills/mat-xrd-digitizer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mat-xrd-digitizer
GitHub stars
175
Token cost
~1k tokens
SKILL.md length
445 words
Files
7 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Digitize an image of an XRD plot into a numeric .xy data file by extracting visual peaks.

  • Works in 2 steps: Extract Peaks Visually → Generate the Digitized .xy File
  • SKILL.md covers Goal, Instructions, Examples and Constraints, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Mat Xrd Digitizer is an agent skill from learningmatter-mit/AtomisticSkills. Digitize an image of an XRD plot into a numeric .xy data file by extracting visual peaks.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts (for example `examples/digitize-ybco/README.md`, `examples/digitize-ybco/peaks.json` and `scripts/digitize_plot.py`).

The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

Example prompts

  • “/mat-xrd-digitizer”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Extract Peaks Visually
  2. Generate the Digitized .xy File

What it can do on your machine

Read from SKILL.md and the folder at commit 7f2d86d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mat Xrd Digitizer loads about 1k tokens when it runs. Until then it costs about 27 tokens; SKILL.md has 445 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~27
When it runs · the whole SKILL.md, loaded when a task matches
~1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 7f2d86d, republished under its MIT licence (© learningmatter-mit). 445 words, ~1,007 tokens.

Download SKILL.mdSave it as .claude/skills/mat-xrd-digitizer/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
mat-xrd-digitizer
description
Digitize an image of an XRD plot into a numeric .xy data file by extracting visual peaks.
metadata.category
materials
metadata.venv
cpu

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:

  1. 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.
  2. 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:

json
[
  {"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.

Show full SKILL.md (199 more words)Show less
2. Generate the Digitized .xy File

Use the provided script to generate the experimental .xy file based on the extracted peaks.

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/digitize_plot.py peaks.json --output digitized_plot.xy --min-x 5.0 --max-x 80.0

Parameters:

  • input: The JSON file containing the extracted peak parameters.
  • --output: Path to save the resulting .xy file.
  • --min-x: Minimum 2-theta value to generate (default: 5.0).
  • --max-x: Maximum 2-theta value to generate (default: 90.0).
  • --points: Number of data points in the .xy file (default: 4000).
  • --noise: Amplitude of experimental noise to add (default: 0.01).
  • --background: Amplitude of exponential background baseline (default: 0.05).

Examples

For a full working example of extracting and digitizing a YBCO plot: See examples/digitize-ybco/README.md.

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/digitize_plot.py ${CLAUDE_SKILL_DIR}/examples/digitize-ybco/peaks.json --output test_ybco.xy

Constraints

  • Approximation: The digitized plot is a mathematical approximation using pseudo-Voigt profiles. It does not perfectly recreate the exact pixel-by-pixel raw data of the original scan, but it is highly effective for downstream phase matching tools.
  • Vision Accuracy: The accuracy of the 2-theta positions entirely depends on the clarity of the provided image axes.
  • Environments: Scripts require the cpu environment. Each code block MUST specify the environment.

References

  • Pseudo-Voigt profile generation is standard practice in XRD peak fitting (e.g., Rietveld refinement tools).

Author: Bowen Deng Contact: GitHub @learningmatter-mit

© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts) in skills/mat-xrd-digitizer of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/digitize-ybco/README.md
  • examples/digitize-ybco/example_ybco.png
  • examples/digitize-ybco/example_ybco.xy
  • examples/digitize-ybco/original_plot.png
  • examples/digitize-ybco/peaks.json
  • scripts/digitize_plot.py

Open the folder on GitHubat commit 7f2d86d

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Questions about Mat Xrd Digitizer

What does Mat Xrd Digitizer do?

Digitize an image of an XRD plot into a numeric .xy data file by extracting visual peaks. Mat Xrd Digitizer is an agent skill from learningmatter-mit/AtomisticSkills.xy data file by extracting visual peaks.

How do I install Mat Xrd Digitizer in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-digitizer -a claude-code`. Or copy the skill folder (skills/mat-xrd-digitizer in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-xrd-digitizer in your project. Claude Code loads it when a task matches its description.

How do I install Mat Xrd Digitizer in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-digitizer -a codex`. Or copy the skill folder (skills/mat-xrd-digitizer in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-xrd-digitizer in your project. Codex loads it when a task matches its description.

Can I use Mat Xrd Digitizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-digitizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mat-xrd-digitizer, .gemini/skills/mat-xrd-digitizer, .github/skills/mat-xrd-digitizer and .opencode/skills/mat-xrd-digitizer in your project.

What does Mat Xrd Digitizer need to run?

Going by SKILL.md and its folder, Mat Xrd Digitizer needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Mat Xrd Digitizer access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Mat Xrd Digitizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Mat Xrd Digitizer use?

Mat Xrd Digitizer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mat Xrd Digitizer use?

About 1k tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mat Xrd Digitizer?

Skills that share tags, products or a category with Mat Xrd Digitizer: Digital Forensics (sickn33/agentic-awesome-skills, 47k stars), Digital Forensics (zhaoxuya520/reverse-skill, 40k stars), Plotly (davila7/claude-code-templates, 32k stars) and Plotly (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mat Xrd Digitizer?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 175 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 6, 2026.

Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.