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 battery-materials-workflowgit 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/battery-materials-workflow)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/battery-materials-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/battery-materials-workflow/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/battery-materials-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/battery-materials-workflow.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 | $0.09136 |
| Opus 5 | $0.00003 | $0.04568 |
| Sonnet 5 | $0.00001 | $0.01827 |
| Haiku 4.5 | $0.00001 | $0.00914 |
Grade A, and why
battery-materials-workflow 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 — 492 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Battery Materials Workflow
Description
This skill covers computational and data-driven workflows for battery materials discovery and characterization: voltage profiles, redox couples, intercalation thermodynamics, phase stability, ion migration barriers, defect chemistry, ionic conductivity, electrochemical stability windows, surface and interface reactions, SEI/CEI formation concepts, high-throughput screening, surrogate modeling, and coupling to experiments. Primary backends are VASP (DFT) and pymatgen for structure manipulation, phase diagram construction, convex hull analysis, and migration pathway automation. Invoke this skill when an agent needs to reason computationally about cathodes, anodes, solid electrolytes, liquid electrolytes, interfaces, coatings, or degradation mechanisms in rechargeable battery systems.
Domain Context
A rechargeable battery converts chemical energy to electrical energy reversibly through coupled electrochemical reactions at two electrodes separated by an electrolyte. Performance is governed by thermodynamic, kinetic, mechanical, and interfacial properties across length scales from Ångstroms (defect cores, interfacial reactions) to micrometers (electrode particles, separator, pores) to centimeters (cell architecture, thermal gradients).
Voltage is set by the difference in lithium (or sodium, magnesium, zinc, …) chemical potential between cathode and anode:
V = -(μ_Li(cathode) - μ_Li(anode)) / F
In DFT, the average intercalation voltage over a composition range x₁ → x₂ in Li_x·HostO₂ is:
V_avg = -[E(Li_{x2}HostO₂) - E(Li_{x1}HostO₂) - (x2-x1)·E(Li_bcc)] / ((x2-x1)·F)
where E(Li_bcc) is the DFT total energy per atom of body-centered-cubic lithium metal. The sign convention requires care: a more negative total energy of the lithiated cathode raises the voltage. Wrong assignment of the lithium reference state is the most common numerical error in voltage calculations.
Energy density is limited by voltage × capacity (mAh g⁻¹); practical limits are dominated by cycle stability, safety, rate capability, and cost. Computational workflows address thermodynamics (what phases are stable at what compositions and temperatures?), kinetics (how fast do ions diffuse?), and interfacial chemistry (how does the electrolyte decompose and what passivating layer forms?).
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 · 492 lines · 5 tokens per session scan A 7c741f27f62d
battery-materials-workflow 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 9,136 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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