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 pymatgen-analysisgit 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/pymatgen-analysis)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/pymatgen-analysis"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/pymatgen-analysis/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/pymatgen-analysis"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/pymatgen-analysis.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 | $0.07889 |
| Opus 5 | $0.00003 | $0.03945 |
| Sonnet 5 | $0.00001 | $0.01578 |
| Haiku 4.5 | $0.00001 | $0.00789 |
Grade A, and why
pymatgen-analysis 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 12d 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 — 627 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pymatgen Analysis
Description
This skill covers pymatgen as a materials analysis and structure-processing framework: Structure, Molecule, Lattice, Composition, Element, Species, and Site objects; CIF/POSCAR/VASP output parsing; symmetry analysis; cell standardization; supercell, slab, defect, and substitution preparation; phase diagrams and formation energies; and interoperability with ASE, matminer, MP API, Phonopy, and visualization workflows. Invoke this skill when a workflow needs robust materials-domain parsing, symmetry-aware structure manipulation, VASP input/output analysis, or thermodynamic construction from computed energies.
Domain Context
pymatgen is a domain library for representing and manipulating materials data. Its core abstractions are chemically aware: a Composition knows oxidation states and reduced formulas; a Structure stores periodic sites in fractional coordinates with a Lattice; a Species can encode formal charge and spin; a Site can carry properties such as magnetic moment, selective dynamics, labels, or disorder. This is different from a generic coordinate container: pymatgen operations often use crystallographic assumptions, oxidation-state heuristics, and symmetry reductions.
The main strength of pymatgen is that it connects structure representation to analysis. The same Structure object can be standardized by spglib, converted to a VASP POSCAR, transformed into a slab, decorated with oxidation states, passed to matminer, converted to ASE, inserted into a phase diagram, or compared against entries from a database. This makes pymatgen a natural support layer for DFT workflows, dataset generation, visualization, and reproducibility.
The main risk is silent structure change. Symmetry analysis can reduce a distorted structure to a higher-symmetry prototype if tolerances are too loose. CIF parsing can expand partial occupancies or disorder in ways that are inappropriate for DFT. Conventional-cell choices differ between crystallographic, Setyawan-Curtarolo, and code-specific conventions. Site properties can be dropped during conversion. A structure that looks "standardized" may no longer be the structure actually computed.
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.
- 12d ago First seen · 627 lines · 5 tokens per session scan A 7c993bcd2f6a
pymatgen-analysis 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 7,889 once invoked, about $0.0000 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 2026-08-31.
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