Agent skill

Mat Xrd Refinement

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Perform Rietveld refinement from experimental XRD patterns using DARA (BGMN).

MITAuto-check passed

Install Mat Xrd Refinement

skills CLI

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

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

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-xrd-refinement --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-refinement .claude/skills/mat-xrd-refinement && 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/

Paste into Claude Code

Install the "mat-xrd-refinement" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-xrd-refinement into .claude/skills/mat-xrd-refinement/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-xrd-refinement", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-xrd-refinement

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-refinement -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-xrd-refinement --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

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

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "mat-xrd-refinement" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-xrd-refinement into .agents/skills/mat-xrd-refinement/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-xrd-refinement", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-refinement -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-xrd-refinement --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

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

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "mat-xrd-refinement" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-xrd-refinement into .cursor/skills/mat-xrd-refinement/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-xrd-refinement", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/mat-xrd-refinement

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-refinement -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-xrd-refinement --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

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

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "mat-xrd-refinement" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-xrd-refinement into .gemini/skills/mat-xrd-refinement/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-xrd-refinement", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-xrd-refinement

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-refinement -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

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

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "mat-xrd-refinement" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-xrd-refinement into .github/skills/mat-xrd-refinement/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-xrd-refinement", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-xrd-refinement -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-xrd-refinement --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

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

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "mat-xrd-refinement" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-xrd-refinement into .opencode/skills/mat-xrd-refinement/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-xrd-refinement", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: mat-xrd-refinement
GitHub stars: 175
Token cost: ~1.8k tokens
SKILL.md length: 659 words
Files: 27 (incl. scripts)
Skills in repo: 129
Repo updated: Oct 6, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Perform Rietveld refinement from experimental XRD patterns using DARA (BGMN).

Works in 2 steps: Prepare XRD data (.xy format) → Run refinement (refine.py)
SKILL.md covers Goal, Requirements, Scripts and Instructions, plus 4 more sections

What it does

Mat Xrd Refinement is an agent skill from learningmatter-mit/AtomisticSkills. Perform Rietveld refinement from experimental XRD patterns using DARA (BGMN).

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 33 other files, including scripts (for example `examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/README.md`, `examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/refinement_results/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/refinement_result.json` and `examples/LiFePO4/LiFePO4_xrd.json`).

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-refinement”

Requirements

Python 3

Workflow steps

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

Prepare XRD data (.xy format)
Run refinement (refine.py)

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/, 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):
- cedergrouphub.github.io
- 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 Refinement loads about 1.8k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 659 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~24
When it runs · the whole SKILL.md, loaded when a task matches: ~1.8k

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). 659 words, ~1,824 tokens.

Download SKILL.mdSave it as .claude/skills/mat-xrd-refinement/SKILL.md (or your agent's skills folder). This skill also uses 26 other files; get the full folder from GitHub.

name: mat-xrd-refinement
description: Perform Rietveld refinement from experimental XRD patterns using DARA (BGMN).
metadata.category: materials
metadata.venv: cpu

Goal

Perform quantitative Rietveld refinement of powder X-ray diffraction (XRD) patterns using DARA (Data-driven Automated Rietveld Analysis) with BGMN. Use when you have an experimental (or theoretical) pattern in .xy format and candidate phase CIFs.

Requirements

Environment: cpu (it includes dara-xrd and pymatgen; venv/run creates it on first use).
PNG export uses kaleido, which needs Chrome or Chromium (on x86_64, plotly_get_chrome installs one).
BGMN: DARA uses BGMN; ensure it is installed. On HPC without network, set --bgmn_dir or DARA_BGMN_DIR to a local BGMN directory.

Scripts

Script	Purpose
`scripts/refine.py`	Run Rietveld refinement with known phases; writes plots and summary under `refinement_results/`.
`scripts/convert_xrd_to_xy.py`	Convert XRD from JSON (xrd-spectrum) or DIF to `.xy` for DARA.
`scripts/dara_utils.py`	Helpers (e.g. `load_xrd_file`); used by other scripts.

Instructions

1. Prepare XRD data (`.xy` format)

Two columns (2θ and intensity), space-separated. Options:

From xrd-spectrum JSON: use convert_xrd_to_xy.py with --input_file your_xrd.json. Output is written next to the input as your_xrd.xy.
From experimental DIF: use convert_xrd_to_xy.py with --input_file your_data.txt (or .dif). Format is auto-detected if the file contains a header with 2-THETA and INTENSITY.

bash

# From JSON (e.g. xrd-spectrum output)
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/convert_xrd_to_xy.py --input_file path/to/xrd.json

# From DIF
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/convert_xrd_to_xy.py --input_file path/to/scan.txt

Convert arguments:

--input_file: Path to JSON or DIF file.
--format: auto (default), json, or dif to force format.

2. Run refinement (`refine.py`)

Refinement uses DARA’s do_refinement_no_saving (no BGMN working files left on disk). Output is written to refinement_results/ under the same directory as the XRD file (no --output_dir argument).

Refine arguments:

--xrd_data: (Required.) Path to the .xy pattern. Quote the path in the shell if it contains parentheses or spaces, e.g. --xrd_data "./path/with(Chem).xy".
--cifs: (Optional.) List of CIF paths. If omitted, CIFs are auto-discovered: first from a cifs/ subfolder next to the XRD file, then from the XRD directory. Example layout: examples/LiFePO4/LiFePO4_xrd.xy and examples/LiFePO4/cifs/LiFePO4.cif, Li3PO4.cif.
--instrument_profile: Default Aeris-fds-Pixcel1d-Medipix3.
--phase_params: Path to a JSON file with phase refinement parameters (e.g. lattice_range, b1, k1, gewicht). See DARA tutorial.
--refinement_params: Path to JSON for refinement options (e.g. wmin, wmax).
--bgmn_dir: Local BGMN directory (avoids download). Or set DARA_BGMN_DIR.
--quiet: Suppress progress output.

Normalized intensity: If the pattern’s maximum intensity is < 10, the script scales intensities to ~1000 before refinement so Rwp is comparable to the DARA tutorial; the applied scale is printed and stored in refinement_result.json as intensity_scale_applied.

Output files (`refine.py`)

All under <xrd_directory>/refinement_results/<stem>/ (e.g. refinement_results/LiFePO4/):

File	Description
`refinement_result.json`	Rwp, instrument_profile, phase_params, refinement_params, phases (lattice, gewicht), paths to plots and peak_data, optional `intensity_scale_applied`.
`<stem>_refinement.html`	Interactive Plotly refinement plot (observed, calculated, difference).
`<stem>_refinement.png`	Static plot (requires `kaleido`).
`<stem>_peak_data.csv`	Simulated peaks (2θ, intensity, h, k, l, phase, etc.).

No BGMN working files (.str, .par, .lst, etc.) are saved; DARA runs in a temporary directory.

Show full SKILL.md (233 more words)Show less

Examples

Example 1: LiFePO4 (CIFs in `cifs/` subfolder)

Layout: examples/LiFePO4/LiFePO4_xrd.xy and examples/LiFePO4/cifs/LiFePO4.cif, Li3PO4.cif. No --cifs needed.

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/refine.py \
  --xrd_data ${CLAUDE_SKILL_DIR}/examples/LiFePO4/LiFePO4_xrd.xy

Results: examples/LiFePO4/refinement_results/LiFePO4/ (refinement_result.json, HTML/PNG, peak_data CSV).

Example 2: CaNi(PO3)4 (path with parentheses — must quote)

bash

# Quote the path because of (PO3), (OH), (NH4).
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/refine.py \
  --xrd_data "${CLAUDE_SKILL_DIR}/examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO.xy"

CIFs are taken from examples/CaNi(PO3)4_.../cifs/ (NiO_225_sym.cif, CaNi(PO3)4_15_sym.cif). Results under that example’s refinement_results/.

Example 3: Explicit CIFs and optional parameters

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/refine.py \
  --xrd_data pattern.xy \
  --cifs phase1.cif phase2.cif \
  --phase_params phase_params.json \
  --refinement_params refinement_params.json

Standalone Plotting (`plot.py`)

If you want to adjust the visualization (e.g. dimensions, font sizes, legend position) without re-running the heavy DARA refinement process, you can use the standalone plot.py script. This script reads the *_curve_data.csv exported by refine.py.

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/plot.py \
  --data_dir refinement_results/my_pattern \
  --output refinement_results/my_pattern/reformatted_plot

You can independently edit plot.py directly to adjust any of the matplotlib/plotly formatting rules.

Constraints

BGMN: Must be installed and on PATH, or provide --bgmn_dir / DARA_BGMN_DIR on restricted networks.
Paths: In the shell, quote any path that contains ( ) or spaces.
Rwp: Good fits often < 15%. High Rwp with a good-looking plot can occur if the pattern is normalized (low intensity); the script auto-scales in that case. You can also try --phase_params (e.g. lattice_range, b1, k1, gewicht) per the DARA tutorial.
Instrument profile: Default is Aeris-fds-Pixcel1d-Medipix3; change with --instrument_profile if needed for your diffractometer.

mat-xrd-digitizer:
- Use this skill to digitize an image or screenshot of an XRD plot into an .xy file if you do not have raw experimental data.
mat-xrd-calculator:
- Calculate theoretical XRD patterns from crystal structures.
foundation-potentials:
- Relax structures before XRD for better agreement with experiment.

Author: Nofit Segal Contact: GitHub @nofitsegal

Files

SKILL.md and 26 other files (scripts) in skills/mat-xrd-refinement of learningmatter-mit/AtomisticSkills.

SKILL.md
examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO.xy
examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/README.md
examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/cifs/CaNi(PO3)4_15_sym.cif
examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/cifs/NiO_225_sym.cif
examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/refinement_results/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO_curve_data.csv
examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/refinement_results/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO_peak_data.csv
examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/refinement_results/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO_refinement.png
examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/refinement_results/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO_refinement.svg
examples/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/refinement_results/CaNi(PO3)4_800_240_Ca(OH)2_(NH4)2HPO4_NiO/refinement_result.json
examples/LiFePO4/LiFePO4_xrd.json
examples/LiFePO4/LiFePO4_xrd.xy
examples/LiFePO4/README.md
examples/LiFePO4/cifs/Li3PO4.cif
… and 13 more

Open the folder on GitHubat commit 7f2d86d

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Questions about Mat Xrd Refinement

What does Mat Xrd Refinement do?

Perform Rietveld refinement from experimental XRD patterns using DARA (BGMN). Mat Xrd Refinement is an agent skill from learningmatter-mit/AtomisticSkills. Perform Rietveld refinement from experimental XRD patterns using DARA (BGMN).

How do I install Mat Xrd Refinement in Claude Code?

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

How do I install Mat Xrd Refinement in Codex?

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

Can I use Mat Xrd Refinement 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-refinement -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-refinement, .gemini/skills/mat-xrd-refinement, .github/skills/mat-xrd-refinement and .opencode/skills/mat-xrd-refinement in your project.

What does Mat Xrd Refinement need to run?

SKILL.md names no scripts, command-line tools or credentials: Mat Xrd Refinement is instructions for the agent only. Our summary lists: Python 3.

Does Mat Xrd Refinement access the network?

SKILL.md names 2 domains. As links in the text: cedergrouphub.github.io and github.com. This is read from the text; nothing was executed.

Is Mat Xrd Refinement 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 Refinement use?

Mat Xrd Refinement 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 Refinement use?

About 1.8k tokens (SKILL.md is roughly 7.3k 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 Refinement?

Skills that share tags, products or a category with Mat Xrd Refinement: React Performance (affaan-m/ECC, 274k stars), Performance Profiler (alirezarezvani/claude-skills, 28k stars), Agent Performance Optimizer (ruvnet/ruflo, 74k stars) and Handsontable Performance Testing (handsontable/handsontable, 22k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mat Xrd Refinement?

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.

Install Mat Xrd Refinement

Facts

At a glance

What it does

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Rietveld Refinement

Goal

Requirements

Scripts

Instructions

1. Prepare XRD data (.xy format)

2. Run refinement (refine.py)

Output files (refine.py)

Examples

Example 1: LiFePO4 (CIFs in cifs/ subfolder)

Example 2: CaNi(PO3)4 (path with parentheses — must quote)

Example 3: Explicit CIFs and optional parameters

Standalone Plotting (plot.py)

Constraints

Related skills

Files

Compare with similar skills

Similar skills

More from learningmatter-mit/AtomisticSkills

Questions about Mat Xrd Refinement

1. Prepare XRD data (`.xy` format)

2. Run refinement (`refine.py`)

Output files (`refine.py`)

Example 1: LiFePO4 (CIFs in `cifs/` subfolder)

Standalone Plotting (`plot.py`)