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Agent skill

Chem Sorption Relax

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.

MITAuto-check passed

Install Chem Sorption Relax

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill chem-sorption-relax -a claude-code

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

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills chem-sorption-relax --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/chem-sorption-relax .claude/skills/chem-sorption-relax && 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
chem-sorption-relax
GitHub stars
176
Token cost
~1.2k tokens
SKILL.md length
442 words
Files
7 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.

  • Works in 2 steps: Build supercell → Relax with UMA-S-1p2 via MCP Tool
  • SKILL.md covers Goal, Prerequisites, Instructions and Examples, plus 1 more section
  • Runs Python and Shell scripts from its folder

What it does

Chem Sorption Relax is an agent skill from learningmatter-mit/AtomisticSkills. Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts (for example `examples/README.md`, `examples/input_configs.yaml` and `examples/test_relax.sh`).

It works with Model Context Protocol and Python. 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

  • “Use the chem-sorption-relax skill to prepare supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard…”
  • “/chem-sorption-relax”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Build supercell
  2. Relax with UMA-S-1p2 via MCP Tool

What it can do on your machine

Read from SKILL.md and the folder at commit 6257444. 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 2 files in scripts/ (Python and Shell), 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

Chem Sorption Relax loads about 1.2k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 442 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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 6257444, republished under its MIT licence (© learningmatter-mit). 442 words, ~1,243 tokens.

Download SKILL.mdSave it as .claude/skills/chem-sorption-relax/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
chem-sorption-relax
description
Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.
metadata.category
materials, chemistry
metadata.venv
cpu, fairchem, mlip

chem-sorption-relax

<!-- mcp-tools-note -->

[!NOTE] Steps written server.tool are MCP tool calls: fairchem.relax_structure is the relax_structure tool of the fairchem server (mcp__fairchem__relax_structure, or mcp__plugin_atomistic-skills_fairchem__relax_structure when installed as a plugin). Without a connected server, run the same tools from the shell. Tools named in one command share a process, so a model loaded by load_model stays loaded:

bash
${CLAUDE_SKILL_DIR}/../../venv/run fairchem python -m src.mcp_server.cli fairchem relax_structure key=value load_model key=value
${CLAUDE_SKILL_DIR}/../../venv/run mlip python -m src.mcp_server.cli mace relax_structure key=value
${CLAUDE_SKILL_DIR}/../../venv/run mlip python -m src.mcp_server.cli matgl relax_structure key=value

Goal

To process porous frameworks (e.g., MOFs, COFs) for downstream molecular sorption calculations. It checks if the unit cell's interplanar distances are large enough (usually ≥ 12 Å for typical gases) to avoid self-interaction of gas molecules across periodic boundaries. If not, it builds an appropriate supercell. Finally, it uses a standard Machine Learning Interatomic Potential (MLIP) workflow to relax the structure.

Prerequisites

  • Input: A framework structure in CIF (or XYZ) format.
  • MLIP MCP Tool: A relaxation tool such as fairchem.relax_structure, mace.relax_structure, or matgl.relax_structure.
  • Environment: cpu for the supercell builder logic, followed by the specific environment for the chosen MLIP (fairchem for FairChem, mlip for MACE and MatGL).

Instructions

  1. Build Supercell (if necessary): Determine if the input framework needs to be expanded. Use the provided utility to read the input CIF, check interplanar distances, build a supercell if they are below the threshold, and save the result.
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/build_supercell.py \
    --structure path/to/framework.cif \
    --min-plane-dist 12.0 \
    --output-cif ./out/framework_supercell.cif

[!TIP] If the script output indicates a 1x1x1 supercell was created (i.e. no expansion needed), you can just use your original CIF or the output CIF, as they will be identical.

  1. Relax the Framework: Relax the output structure using the MCP server environment. Ensure that the correct MLIP is loaded first.
python
# (via MCP server)
fairchem.load_model(
    model_name="uma-s-1p2",
    device="auto"
)

fairchem.relax_structure(
    structure_data="./out/framework_supercell.cif",
    fmax=0.05,
    steps=500,
    optimizer="LBFGS",
    relax_cell=True,
    output_dir="./out/relaxed_framework"
)
Show full SKILL.md (163 more words)Show less
relax_structure.py Parameters
  • --structure: Path to input CIF or XYZ.
  • --name: Identifier used in output filenames.
  • --calculator: Backend MLIP (fairchem, mace, matgl).
  • --model-name: Named model (e.g. uma-s-1p2) or full path to checkpoint.
  • --task-name: Multi-task head (omol, omat, odac, oc20, omc).
  • --optimizer: LBFGS (default) or FIRE.
  • --fmax: Force convergence threshold in eV/Å (default: 0.05).
  • --steps: Maximum optimizer steps (default: 500).
  • --relax-cell: Relax unit cell (default: True). Use --fixed-cell to fix cell.
  • --output-dir: Directory to save <name>.relaxed.cif and relax_results.json.
  1. Proceed to downstream tasks: The relaxed CIF file (e.g. ./out/relaxed_framework/<name>.relaxed.cif) from step 2 is now ready for use in chem-sorption-widom and chem-sorption-gcmc.

Examples

Full workflow:

  1. Build supercell:
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/build_supercell.py \
    --structure my_cof.cif \
    --min-plane-dist 12.0 \
    --output-cif ./results/COF-1_supercell.cif
  1. Relax with UMA-S-1p2 via MCP Tool:
python
fairchem.load_model(
    model_name="uma-s-1p2",
    device="auto"
)

fairchem.relax_structure(
    structure_data="./results/COF-1_supercell.cif",
    fmax=0.05,
    steps=500,
    optimizer="LBFGS",
    output_dir="./results/relaxed"
)

Constraints

  • Input Structure: The initial framework should be somewhat reasonable; highly distorted structures might fail during relaxation.
  • Minimum Distance: The --min-plane-dist should be at least 2 × (cut-off radius) of the probe gas interaction length (typically 12 Å for CO2 or N2).

Authors: Artur Lyssenko, Sauradeep Majumdar Contact: GitHub @arturlyssenko12, GitHub @sauradeep93

© 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/chem-sorption-relax of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/README.md
  • examples/input_configs.yaml
  • examples/test_relax.sh
  • examples/test_structure.cif
  • scripts/build_supercell.py
  • scripts/relax_structure.py

Open the folder on GitHubat commit 6257444

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Works with

Questions about Chem Sorption Relax

What does Chem Sorption Relax do?

Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools. Chem Sorption Relax is an agent skill from learningmatter-mit/AtomisticSkills. Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.

How do I install Chem Sorption Relax in Claude Code?

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

How do I install Chem Sorption Relax in Codex?

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

Can I use Chem Sorption Relax 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 chem-sorption-relax -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chem-sorption-relax, .gemini/skills/chem-sorption-relax, .github/skills/chem-sorption-relax and .opencode/skills/chem-sorption-relax in your project.

What does Chem Sorption Relax need to run?

Going by SKILL.md and its folder, Chem Sorption Relax needs Python and a shell for the scripts in its folder. Our summary lists: Python 3; A Bash shell.

Does Chem Sorption Relax 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 Chem Sorption Relax 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 Chem Sorption Relax use?

Chem Sorption Relax 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 Chem Sorption Relax use?

About 1.2k tokens (SKILL.md is roughly 5k 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 Chem Sorption Relax?

Skills that share tags, products or a category with Chem Sorption Relax: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MemPalace Setup and Operation (MemPalace/mempalace, 59k stars) and LangBot Plugin Development (langbot-app/LangBot, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Chem Sorption Relax?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 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.