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/chem-db-mofnpx skills add learningmatter-mit/AtomisticSkills --skill chem-db-mofgit 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/chem-db-mof)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-db-mof"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/chem-db-mof.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 | $0.00047 | $0.02183 |
| Opus 5 | $0.00023 | $0.01092 |
| Sonnet 5 | $0.00009 | $0.00437 |
| Haiku 4.5 | $0.00005 | $0.00218 |
Grade A, and why
chem-db-mof 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 4d ago.
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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
chem-db-mof
Goal
Provide a unified interface for retrieving Metal-Organic Framework (MOF) crystal structures from multiple curated databases. Currently supported:
| Database | Alias | Size | Access | Structures |
|---|---|---|---|---|
| Quantum MOF (QMOF) | qmof |
~20,000 DFT-relaxed | MPContribs API | DFT-optimized CIFs + bandgaps |
| ARC-MOF DB7 (Majumdar et al.) | arcmof-majumdar |
12,316 hypothetical | Zenodo stream | CIFs with REPEAT partial charges |
Prerequisites
- Environment:
base-agent - Packages:
mpcontribs-client,requests,pandas,pymatgen - Credentials:
MP_API_KEYenvironment variable (required forqmofonly)
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
--formulafor element filtering (e.g.,ZnorCu,N,O) - Use
--identifierfor a specific CSD refcode (e.g.,KAXQIL)
For ARC-MOF DB7 (Majumdar et al.) — best for diverse hypothetical MOFs with underrepresented inorganic SBUs:
- Use
--elementsfor element filtering (e.g.,Zn,O,C) - Use
--identifierfor 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
# Env: base-agent
# QMOF — 10 Zn-containing MOFs
MP_API_KEY=<your_key> python .agents/skills/chem-db-mof/scripts/query_mof_db.py \
--database qmof \
--formula Zn \
--max-results 10 \
--output-dir ./research/<date>_<task>/structures/qmof
# Env: base-agent
# ARC-MOF DB7 (Majumdar) — 20 Zn,O,C hypothetical MOFs
python .agents/skills/chem-db-mof/scripts/query_mof_db.py \
--database arcmof-majumdar \
--elements Zn,O,C \
--max-results 20 \
--output-dir ./research/<date>_<task>/structures/arcmof_db7
# Env: base-agent
# ARC-MOF DB7 — retrieve a specific structure by identifier
python .agents/skills/chem-db-mof/scripts/query_mof_db.py \
--database arcmof-majumdar \
--identifier DB7_00042 \
--output-dir ./research/<date>_<task>/structures/arcmof_db7
What ships with it
1 file 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.
- 4d ago First seen · 162 lines · 47 tokens per session scan A 233dc02a1a1c
chem-db-mof is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 2,183 once invoked, about $0.0002 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 2026-08-30.
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