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 md-equilibrationgit 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/md-equilibration)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/md-equilibration"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/md-equilibration/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/md-equilibration"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/md-equilibration.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 | $0.00004 | $0.09334 |
| Opus 5 | $0.00002 | $0.04667 |
| Sonnet 5 | $0.00001 | $0.01867 |
| Haiku 4.5 | $0.00000 | $0.00933 |
Grade A, and why
md-equilibration 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 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.
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 — 602 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MD Equilibration
Description
This skill covers the systematic process of bringing an atomistic simulation from its initial (often artificial) configuration to a state representative of the target thermodynamic ensemble before collecting production statistics. It addresses ensemble selection, thermostat and barostat time constants, timestep stability, velocity initialization, and quantitative criteria for detecting whether temperature, pressure, density, energy, and structural observables have converged. Invoke this skill before starting any production MD run, whenever changing thermodynamic conditions, and whenever diagnosing anomalous simulation behavior that may originate from incomplete equilibration.
Domain Context
An MD simulation starts from an initial configuration that is almost never a genuine sample from the target ensemble. Crystal structures from databases have zero-temperature geometry; liquid models begin from arbitrary packings; protein structures come from X-ray refinement at cryogenic conditions. The equilibration phase drives the system from this artificial starting point to a state where the time average of any observable is independent of when in the trajectory it is measured.
Three distinct processes happen during equilibration, each on its own timescale:
- Energy redistribution (thermalization): Kinetic energy assigned to all degrees of freedom reaches the target temperature. Timescale: tens to hundreds of picoseconds for most systems.
- Pressure and density relaxation: The simulation box adjusts to the target pressure. Timescale: typically 10–100× longer than thermalization for liquids; can be nanoseconds for dense polymers or glasses. [EXPERT REVIEW NEEDED]
- Structural relaxation: Local and global order parameters (RDF peaks, dihedral distributions, bond orientational order) reach their equilibrium values. This is the slowest and most system-dependent step. For crystalline systems, it may complete in picoseconds. For viscous liquids, glasses, or polymer melts, it may require microseconds of simulation time.
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 · 602 lines · 4 tokens per session scan A 2f1f734c6368
md-equilibration is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 4 tokens to every session and 9,334 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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