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Agent skill

Chem DB Mof

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al.

MITAuto-check passed

Install Chem DB Mof

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill chem-db-mof -a claude-code

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

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills chem-db-mof --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-db-mof .claude/skills/chem-db-mof && 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-db-mof
GitHub stars
175
Token cost
~1.9k tokens
SKILL.md length
626 words
Files
2 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al.

  • Works in 3 steps: Choose a database and set filters → Run the query → Inspect outputs
  • SKILL.md covers Goal, Prerequisites, Instructions and Download Behavior: ARC-MOF DB7, plus 3 more sections
  • Runs Python scripts from its folder; needs MP_API_KEY

What it does

Chem DB Mof is an agent skill from learningmatter-mit/AtomisticSkills. Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al. via Zenodo) and download CIF structures with optional element or identifier filters.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/query_mof_db.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

  • “/chem-db-mof”

Requirements

  • Python 3
  • A credential in MP_API_KEY

Workflow steps

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

  1. Choose a database and set filters
  2. Run the query
  3. Inspect outputs

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):

    • doi.org
    • github.com

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • MP_API_KEY

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

Context cost

Chem DB Mof loads about 1.9k tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 626 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~44
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

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). 626 words, ~1,919 tokens.

Download SKILL.mdSave it as .claude/skills/chem-db-mof/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
chem-db-mof
description
Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al. via Zenodo) and download CIF structures with optional element or identifier filters.
metadata.category
chemistry
metadata.venv
cpu

chem-db-mof

Goal

Provide a unified interface for retrieving Metal-Organic Framework (MOF) crystal structures from multiple curated databases. Currently supported:

DatabaseAliasSizeAccessStructures
Quantum MOF (QMOF)qmof~20,000 DFT-relaxedMPContribs APIDFT-optimized CIFs + bandgaps
ARC-MOF DB7 (Majumdar et al.)arcmof-majumdar12,316 hypotheticalZenodo streamCIFs with REPEAT partial charges

Prerequisites

  • Environment: cpu (commands run through venv/run cpu ...)
  • Packages: mpcontribs-client, requests, pandas, pymatgen
  • Credentials: MP_API_KEY environment variable (required for qmof only)

Instructions

Step 1: Choose a database and set filters

Decide which database to query and which element/identifier filters to apply.

For QMOF — best for DFT-validated, experimentally-derived MOFs:

  • Use --formula for element filtering (e.g., Zn or Cu,N,O)
  • Use --identifier for a specific CSD refcode (e.g., KAXQIL)

For ARC-MOF DB7 (Majumdar et al.) — best for diverse hypothetical MOFs with underrepresented inorganic SBUs:

  • Use --elements for element filtering (e.g., Zn,O,C)
  • Use --identifier for a specific structure ID (e.g., DB7_00042)
  • First run: downloads geometric_properties.csv (~110 MB) to ~/.cache/arcmof/ — one-time only; subsequent runs are fast
Step 2: Run the query
bash
# QMOF — 10 Zn-containing MOFs
MP_API_KEY=<your_key> ${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_mof_db.py \
    --database qmof \
    --formula Zn \
    --max-results 10 \
    --output-dir ./research/<date>_<task>/structures/qmof
bash
# ARC-MOF DB7 (Majumdar) — 20 Zn,O,C hypothetical MOFs
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_mof_db.py \
    --database arcmof-majumdar \
    --elements Zn,O,C \
    --max-results 20 \
    --output-dir ./research/<date>_<task>/structures/arcmof_db7
bash
# ARC-MOF DB7 — retrieve a specific structure by identifier
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_mof_db.py \
    --database arcmof-majumdar \
    --identifier DB7_00042 \
    --output-dir ./research/<date>_<task>/structures/arcmof_db7
Available Arguments
ArgumentApplies toDescription
--databasebothqmof or arcmof-majumdar
--formulaqmofElement/formula filter string (e.g., Zn,O,C)
--elementsarcmof-majumdarComma-separated required elements; ALL must be present
--identifierbothSpecific structure name or ID substring
--max-resultsbothMax CIFs to download (default: 10)
--output-dirbothDirectory for output CIF files
--cache-dirarcmof-majumdarOverride default cache ~/.cache/arcmof/
Step 3: Inspect outputs

The script saves:

  • Individual .cif files named by structure identifier
  • arcmof_db7_metadata.csv (ARC-MOF only) — geometric properties for the downloaded subset

Verify the download:

bash
ls -lh <output-dir>/*.cif | head -20

Download Behavior: ARC-MOF DB7

The first call with --database arcmof-majumdar performs:

  1. Metadata download (~110 MB, one-time): geometric_properties.csv cached at ~/.cache/arcmof/
  2. DB7 filtering: identifies the 12,316 Majumdar structures from the full 288k-entry CSV
  3. CIF streaming: streams the ARC-MOF tarball (ARCMOF_20241004.tar.gz, ~670 MB) and extracts only the requested CIFs — the stream is read once but only matching files are written to disk

Subsequent runs with the same --output-dir skip already-downloaded CIFs.

Examples

Example 1: Query Zn MOFs from QMOF for CO₂ screening pre-processing

bash
MP_API_KEY=<your_mp_api_key> \
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_mof_db.py \
    --database qmof \
    --formula Zn \
    --max-results 10 \
    --output-dir ./research/2026-03-27_test/qmof_zn

Example 2: Query Zn, Ni, or Mg hypothetical MOFs from ARC-MOF DB7

bash
# Zn-based
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_mof_db.py \
    --database arcmof-majumdar \
    --elements Zn,O,C \
    --max-results 50 \
    --output-dir ./research/2026-03-27_arcmof_zn

# Ni-based
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_mof_db.py \
    --database arcmof-majumdar \
    --elements Ni,O,C \
    --max-results 50 \
    --output-dir ./research/2026-03-27_arcmof_ni

# Mg-based
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/query_mof_db.py \
    --database arcmof-majumdar \
    --elements Mg,O,C \
    --max-results 50 \
    --output-dir ./research/2026-03-27_arcmof_mg

Tip: You can expand diversity by adding more elements to --elements (e.g., Zn,Ni,O,C,N to retrieve MOFs containing all of those elements simultaneously), or run separate queries per metal node and combine the resulting CIF directories for a broader screening campaign.

Show full SKILL.md (242 more words)Show less

Constraints

  • API limits: QMOF via MPContribs has rate limits; keep --max-results ≤ 100 per call.
  • ARC-MOF first-run time: Downloading the metadata CSV (~110 MB) takes ~1–2 min; streaming the tarball for CIF extraction adds ~5–15 min depending on how many structures are requested and network speed.
  • ARC-MOF CIF fallback: If some DB7 structures are not found in ARCMOF_20241004.tar.gz, they may reside in all_structures_1.tar.gz or all_structures_2.tar.gz. Update ARCMOF_STRUCTURES_NAME in the script if needed.
  • Element filtering (ARC-MOF): Requires a formula or chemical_formula column in geometric_properties.csv. If the column is absent, all DB7 entries are returned without element filtering.
  • Post-download: Structures from ARC-MOF DB7 include REPEAT partial charges embedded in the CIF. These can be used directly for classical force-field simulations but should be relaxed with an MLIP before running Widom insertion (see chem-sorption-relax).

References

  • Raza, A. et al., "ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning", Chem. Mater., 2022. DOI: 10.1021/acs.chemmater.2c02485
  • Majumdar, S., Moosavi, S.M., Jablonka, K.M., Ongari, D., Smit, B., "Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening", ACS Appl. Mater. Interfaces, 2021. DOI: 10.1021/acsami.1c16220; dataset: Materials Cloud Archive 2021.126, DOI: 10.24435/materialscloud:yn-de
  • Chung, Y.G. et al., "Computation-Ready, Experimental Metal-Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals", Chem. Mater., 2014 (QMOF precursor). DOI: 10.1021/cm502594j
  • Rosen, A.S. et al., "Machine learning the quantum-chemical properties of metal-organic frameworks for accelerated materials discovery", Matter, 2021 (QMOF). DOI: 10.1016/j.matt.2021.02.015

Author: Sauradeep Majumdar Contact: 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 1 other file (scripts) in skills/chem-db-mof of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • scripts/query_mof_db.py

Open the folder on GitHubat commit 7f2d86d

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Questions about Chem DB Mof

What does Chem DB Mof do?

Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al. Chem DB Mof is an agent skill from learningmatter-mit/AtomisticSkills. Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al.

How do I install Chem DB Mof in Claude Code?

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

How do I install Chem DB Mof in Codex?

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

Can I use Chem DB Mof 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-db-mof -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-db-mof, .gemini/skills/chem-db-mof, .github/skills/chem-db-mof and .opencode/skills/chem-db-mof in your project.

What does Chem DB Mof need to run?

Going by SKILL.md and its folder, Chem DB Mof needs Python for the scripts in its folder and credentials named MP_API_KEY. Our summary lists: Python 3; A credential in MP_API_KEY.

Does Chem DB Mof access the network?

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

Is Chem DB Mof 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 DB Mof use?

Chem DB Mof 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 DB Mof use?

About 1.9k tokens (SKILL.md is roughly 7.7k 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 DB Mof?

Skills that share tags, products or a category with Chem DB Mof: Emotional Arc Designer (sickn33/agentic-awesome-skills, 47k stars), Form Field Multiple Labels (thedaviddias/Front-End-Checklist, 74k stars), Chem (franklee16/academic-research-skills, 223 stars) and Arc Region Switch (aws/agent-toolkit-for-aws, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Chem DB Mof?

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.