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 skills add SFETNI/Deep-Matter-Chem-Skills --skill materials-databases-accessgit clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-SkillsWrote 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/sfetni/deep-matter-chem-skills/materials-databases-access)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/materials-databases-access"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/materials-databases-access/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/materials-databases-access"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/materials-databases-access.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00005 | $0.07286 |
| Opus 5 | $0.00003 | $0.03643 |
| Sonnet 5 | $0.00001 | $0.01457 |
| Haiku 4.5 | $0.00001 | $0.00729 |
Grade A, and why
materials-databases-access scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(base_url, params=request_params, headers=headers, timeout=60) How it starts
The opening of the file, as written. The whole thing — 639 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Materials Databases Access
Description
This skill covers reproducible access to materials databases for computational materials science: Materials Project, AFLOW, OQMD, NOMAD, COD, and ICSD-style crystallographic sources; API authentication, pagination, rate limits, caching, snapshot provenance, structure matching, duplicate detection, energy compatibility, and dataset preparation for screening or ML. Invoke this skill when retrieving structures, entries, energies, band gaps, elastic data, phonons, tasks, or provenance metadata from external materials databases, or when auditing whether database-derived datasets are compatible enough for phase stability, ML training, or screening.
Domain Context
Materials databases are not neutral collections of facts. Each database encodes choices about DFT code, exchange-correlation functional, pseudopotentials, Hubbard U corrections, structural relaxation protocol, convergence thresholds, duplicate filtering, and post-processing. A formation energy from Materials Project, AFLOW, OQMD, or NOMAD may refer to a different reference state, correction scheme, or calculation generation. Combining them without compatibility checks creates a polished but physically inconsistent dataset.
The most common use cases are structure retrieval, property screening, phase stability analysis, and ML dataset construction. These are different tasks. Structure retrieval can tolerate mixed provenance if each structure is re-relaxed locally. Phase diagrams require energies on one compatible reference scale. ML datasets require clear target definitions, duplicate handling, train/test leakage control, and metadata that makes future re-querying possible.
Database APIs are also moving targets. Documents are added, deprecated, reprocessed, and corrected. The same query issued months apart can return different records. A reproducible workflow records query parameters, API versions, database release or build information where available, result identifiers, task identifiers, timestamps, and raw response caches. Without this, an ML benchmark or screening campaign cannot be reconstructed.
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.
- 11d ago First seen · 639 lines · 5 tokens per session scan A 7f2141108f9c
materials-databases-access is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 7,286 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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