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-qmofnpx skills add learningmatter-mit/AtomisticSkills --skill chem-db-qmofgit 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-qmof)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-db-qmof"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/chem-db-qmof.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 | $0.00691 |
| Opus 5 | $0.00023 | $0.00345 |
| Sonnet 5 | $0.00009 | $0.00138 |
| Haiku 4.5 | $0.00005 | $0.00069 |
Grade A, and why
chem-db-qmof 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 5d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
chem-db-qmof
Goal
To retrieve computational data and relaxed crystal structures (.cif) for roughly 20,000 Metal-Organic Frameworks (MOFs) that have been thoroughly characterized using DFT. QMOF is hosted by the Materials Project under the "MPContribs" project (qmof). This skill allows you to retrieve relaxed CIF structures by reference name, CSD refcode, or specific structural/electronic properties like bandgap.
Prerequisites
- Dependency:
mpcontribs-clientmust be installed. - Environment: Execution happens in the
base-agentenvironment. - Credentials: Requires the standard Materials Project API key exported as
MP_API_KEY.
Instructions
- Query Database: Use the provided script
query_qmof.pyto search by formula or identifier.
# Env: base-agent
python .agents/skills/chem-db-qmof/scripts/query_qmof.py \
--formula "Zn" \
--max-results 5 \
--output-dir ./research/qmof_results
Available Arguments:
--formula: Search by chemical formula or elements (e.g.,Zn,O,C).--identifier: Search by specific CSD refcode or MOF common name (e.g.,KAXQIL).--max-results: Maximum number of structures to download (default: 5).--output-dir: Directory to save the resulting.ciffiles.
Examples
Example 1: Automated testing script (Zinc MOF)
# Env: base-agent
bash .agents/skills/chem-db-qmof/examples/test_qmof.sh
Example 2: Query for 5 MOFs containing Zinc manually
# Env: base-agent
python .agents/skills/chem-db-qmof/scripts/query_qmof.py \
--formula "Zn" \
--max-results 5 \
--output-dir ./results/qmof_zn
Example 2: Retrieve a specific MOF by identifier
# Env: base-agent
python .agents/skills/chem-db-qmof/scripts/query_qmof.py \
--identifier "KAXQIL" \
--max-results 1 \
--output-dir ./results/qmof_kaxqil
Constraints
- API Limits: The Materials Project/MPContribs API has rate limits. Do not use extremely large values for
--max-resultsunless necessary. - Availability: If you cannot find a MOF in QMOF via the script, or if the API query is overly complex, you can also search the QMOF database manually through the Materials Project web interface under the "Contributions" section.
What ships with it
2 files 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.
- 5d ago First seen · 72 lines · 47 tokens per session scan A 51a3b2cf4eba
chem-db-qmof is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (160 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 691 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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